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Exam results

The results of the ICAEW March 2024 ACA Professional Level exams were published here at 12:00 BST on Thursday 18 April 2024. Results are published directly to students. You can view a summary of exam statistics and the Orders of Merit using the tabs above.

For your information

The results of the ICAEW March 2024 ACA Professional Level exams were published here at 12:00 BST on Thursday 18 April 2024.

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Student results letters are available on your training file from 17:00 BST on 18 April.

The Student Support team will be available until 17:00 BST on 18 April, and on Friday 19 April from 09:00 BST until 17:00 BST. The office will not open on 20 and 21 April. If you have a query regarding your results, please contact the Student Support number on +44 (0)1908 248 250 or via webchat . You can also email Student Support at [email protected] .

The Subject Orders of Merit shows the candidates who have achieved the highest marks for each exam.

Please note only students who have given their consent will appear on the published lists. The Pass and Credit lists are no longer published.

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Archived ACA results - pass rate summaries and prize winners - can be found on the archived results page .

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The Affordable Care Act in the US (Obamacare)

Jump to a section.

The initiative

The challenge

The public impact

Stakeholder engagement Fair
Political commitment Weak
Public confidence Fair
Clarity of objectives Good
Strength of evidence Fair
Feasibility Fair
Management Fair
Measurement Good
Alignment Weak

Bibliography

The ACA took effect in 2010 and had as its two main goals providing better health insurance coverage for Americans and lowering the overall cost of healthcare. The programme aimed to extend health insurance to some of the estimated 16% of the population without access - covered neither by their employers nor by national programmes for the poor and elderly. [3]

The ACA changed the regulation of health insurance essentially in five ways, the first of which was extending federal regulation to most private insurance companies, which historically in the US had been regulated at the state level. The ACA specified some requirements of insurers, most significantly that private insurers must sell (or issue) a policy to any person who wants to buy one, regardless of their health status or risks. The federal government also offers financial assistance to states to improve their ability to review insurance premium rates and enforce compliance with the law.

The second reform establishes a "minimum coverage", requiring all citizens and lawful residents to obtain health insurance coverage or pay a penalty (this removes the incentive for only getting insurance when people become ill, thus addressing the risk for private providers). There are exceptions for very low-income people who could not afford this, as well as individuals who do not believe in medical care for religious reasons.

The third reform is the requirement for the establishment of health insurance exchanges as a marketplace where individuals and small businesses can buy insurance (people are also free to buy directly from an insurance company). The federal government will provide subsidies to individuals with incomes between 100% and 400% of the Federal Poverty Level to enable them to pay for the premiums. Most exchanges are run by the states, and the federal government has the authority to operate a federal exchange in states that fail to create their own. States also have authority to add further requirements to their health plans, meaning that plans may differ across states, especially in price.

The fourth reform encourages private employers to provide health insurance to employees - although it does not oblige them to. Finally, the fifth reform addresses the remaining people who cannot afford to buy insurance, primarily because they are not eligible for federal subsidies - both individuals and small businesses. For this group, the ACA amends the Medicaid statute to make everyone with an income below 138% of the Federal Poverty Level eligible for Medicaid health benefits, and the federal government will pay for 100% of the cost of adding this group to Medicaid, ultimately paying 90% by 2016. However, the US Supreme Court held that the states are not obliged to comply with this eligibility standard, so people living in states that decide not to comply may be left without coverage. [4]

In 2008, the US healthcare system was historically considered to be lagging behind its global peers in terms of efficiency and coverage. Two of its key characteristics pointed to a need for reform: "It spends far more per person on healthcare than any other country, yet it is the only country in the developed world that fails to provide healthcare coverage for almost all of its residents". [1]

One of the reasons that made the American system so expensive was that over half of the population relied on private insurance - mostly sponsored by employers. Otherwise, the federal Medicare programme only covered people with disabilities or retired people aged over 65 years. Medicaid - a joint state and federal programme - covered people with low incomes, but in most states it was limited to those fitting certain categories, such as single parents, children, and pregnant women. People who did not fit into any of these categories found it hard to obtain individual insurance. This was not only because of its cost but also that people were subject to very thorough screening (medical underwriting), which usually either declined people with existing medical conditions or charged them substantially more without even covering them for their existing ailments.

Figures from 2010 indicate that approximately 50 million people (16% of the population) had no public or private health insurance coverage, while national spending on medical care in the US at the time was close to USD3 trillion a year. [2]

The ACA has managed to extend coverage to a certain extent, but it has also been adversely affected by increases in premiums and by the public controversy occasioned by political issues and mismanaged expectations:

There is evidence that it has improved access to health insurance coverage for approximately 20 million Americans. However, it fell short of bringing the target of people uninsured from 50 million to 22 million people. [5]
Marketplace enrolment has been lower than expected: The Congressional Budget Office estimated that 21 million people would be enrolled on the exchanges. However, estimates indicate that between 9.4 million and 11.4 million of people signed up in 2016. [6]
From the public perspective, its impact was not perceived as fully beneficial. An inquiry in 2015 found that “most Americans (54%) said the healthcare law had not had an effect on them or their family up until that point. About one in four Americans (26%) said the impact of the law on them personally had been negative, compared with 19% who said it had been positive.” [7]
Opponents also point out some of ACA's undesired side-effects, for example of increasing insurance premiums in states like Wisconsin and Minnesota. “In Wisconsin, state regulators have announced that health insurance premiums are going up an average of 15.88%. Some plans will see premiums rise 30.37% and several large insurance companies have stopped offering plans via the Obamacare exchange in Wisconsin.” [8]
Some of the effects in the commercial market for insurance have been perceived as negative. “Most plans available in the individual and small-group markets prior to the ACA's enactment did not meet all of the law's new rules and mandates. As insurers adjusted to the new rules, the choices available to consumers declined markedly. Plan variation now largely consists of the size of cost-sharing amounts, including deductibles, and the scope of provider networks. As plans became standardised, premiums and cost sharing increased, and provider networks shrank.” [9]

Stakeholder engagement

There were numerous stakeholders involved in the formulation of Obamacare, including the following: federal and state governments, health insurers and other commercial groups related to the policy, political parties and leaders, National Republican Congressional Committee, the Supreme Court, and the general public.

This was reflected in a wide spectrum of opinions regarding what the legislation should look like. The debate between Democrats and Republicans indicates their contrasting positions: "Democrats in the US were in favour of a more socialised solution for healthcare reform, that is, to expand Medicare or create something like it that, along with Medicaid, covered everyone in a 'single payer' system, like that in Canada. While Republican proponents supported a 'less ambitious' law that uses government subsidies mainly to help only those people who are not insurable, leaving the rest of the market to function as normal." [10]

The administration has claimed to have learnt from previous experiences to obtain stronger support for this policy: giving Congress more input to the structure and more information about the policy goals, as well as looking to get support from industry specialists, including insurers, physicians, pharmaceutical companies, hospitals and other providers, and allowing the House and Senate to work through each of their concerns before merging these into a bill. [11]

Political commitment

Previous healthcare programmes in the US had received more consensual support from political leaders, while the ACA occasioned much more dispute. It was favoured by Democrats but strongly opposed by Republicans.

The process for approval of the ACA bill was also conflictual: “Republicans in the House have voted to delay, 'defund' or repeal the law some 60 times” [12] and it barely made it through Congress in 2010, with a slim Democratic majority. Amid extreme partisan divides along ideological lines, it faced all-out Republican opposition. In March 2010, the ACA was only narrowly passed by the House (by 219 votes to 212), with all 178 House Republicans voting against. "These numbers underline the exacerbated partisanship in Congress and, more generally, the lack of consensus surrounding this reform.” [13]

Public confidence

Obamacare has been widely debated both among political actors and the general public. Given that its measures have affected people differently (for better and worse), support and approval have been mixed and changing since implementation, and the public seem to be split down the middle in terms of overall support.

According to a Pew Research Center survey from February 2015, a greater share of the public disapproves (53%) than approves (45%) of the ACA. [14] A Kaiser poll found similar results in 2015, with 43% holding a favourable view as against 42% with an unfavourable one. These figures, however, represented an improvement for Obamacare on a previous survey in July 2014, which found only 37% of Americans supporting the policy, compared to 53% who rejected it. [15]

Clarity of objectives

The main goals of the ACA were to provide more Americans with health insurance and lower the overall cost of healthcare. When launching the ACA, President Obama gave some measurable estimates that helped reinforce these general objectives:

Under the Act, the number of uninsured was intended to decline from 50 million to 22 million in 2016. [16]
The ACA should reduce annual insurance premiums by USD2,500 for the average family. [17]

Strength of evidence

Evidence used for the implementation of Obamacare was based principally on the existing healthcare system in Massachusetts while there was also input from the testing of pilot programmes aimed at improving the quality of service to users.

Obama declared that the ACA was modelled on Mitt Romney's healthcare initiative in Massachusetts. “It's because you guys had a proven model that we built the Affordable Care Act on this template of proven bipartisan success. Your law was the model for the nation's law.” [18] The Romney plan itself has received mixed support, with significant praise of its model from some fellow Republicans, but also some criticism about its method. "The healthcare plans advocated by all three of the leading Democratic presidential candidates — Hillary Clinton, John Edwards, and Barack Obama — are all substantially the same as Romney's. They are all variations of a concept called 'managed competition', which leaves insurance privately owned but forces it to operate in an artificial and highly regulated marketplace similar to a public utility." [19]

Opinion on the Massachusetts model is divided, with some taking the view that it was ineffective. "As Massachusetts has shown us, mandating insurance, restricting individual choice, expanding subsidies, and increasing government control isn't going to solve those problems. A mandate imposes a substantial cost in terms of individual choice but is almost certainly unenforceable and will not achieve its goal of universal coverage. Subsidies may increase coverage, but will almost always cost more than projected and will impose substantial costs on taxpayers. Increased regulations will drive up costs and limit consumer choice." [20]

Additionally, the ACA has added a number of new and amended and/or extended demonstration and pilot programmes. For example, it created the Centers for Medicare and Medicaid Innovation (CMMI), tasked with developing and funding demonstration projects to improve the quality of care for patients. [21]

Feasibility

Policymakers considered the financial and legal constraints before formulating the ACA, but might not have assessed such constraints to their fullest extent. The federal system in the US allows states to have autonomy in the application of certain legislation, which was the case for this healthcare reform. "The Supreme Court's 2012 ruling found the ACA constitutional, but also struck down a provision saying states had to change how they administered the government health programme, Medicaid. Under Obamacare, states were supposed to expand the number of people who qualified for Medicaid, which had been reserved for the poor, and in return the federal government would provide the states more funding. The court said states could choose not to participate in Medicaid expansion.” [22] This was the case for some states which decided to opt out of the Medicaid expansion, leaving people who were supposed to get coverage outside the bill's auspices. [23]

Overall, the Medicaid expansion proved to be more expensive than was forecast. "Total federal spending on the expansion in 2015 was at least 50% above Congressional Budget Office's (CBO) 2014 projection of USD42 billion. Part of the reason for this unanticipated expense is that states are paying insurers higher rates than the government projected; in fact, spending per newly eligible enrollee was 49% higher in 2015 than was expected by the Obama administration in a 2014 report.” [24]

As a result of the ACA, premiums were predicted to rise by 25% in 2017, and government subsidies were to increase accordingly to help pay for insurance. However, those who should be covered by the Medicaid expansion were ineligible for those subsidies, and some may therefore be unable to afford health insurance at all. [25]

The Department of Health and Human Services (HHS) was responsible for implementation of the ACA, and faced substantial challenges. The diversity of the country - both in terms of individuals as well as the federal system - makes this programme difficult to implement. Accounting for the rights of all 314 million individuals made the management of the programme very difficult.

There are many examples of exceptions from the requirements of the ACA, of which the following is one. "An example is the contraception insurance requirement in health plans without 'cost-sharing' under the law's market reforms. This posed a serious issue for religious companies. The Department of Treasury, Labor and Health and Human Services recently provided an exception for non-profit religious employers. Unfortunately, this exception does not extend to for-profit employers. This is one example of how an exception was required." [26] It is expected that many similar exceptions will be requested in the future.

Measurement

The performance of the policy has been evaluated based on a number of indicators such as the number of people insured, the rise or fall in insurance premiums, and the number of enrolments in the policy. The main institution in charge of tracking indicators is the CBO, but, given the high profile of the policy, there are also several independent institutions and research firms tracking results and opinion, such as the Cato Institute and the Kaiser Foundation.

The CBO analyses the effects of the ACA under current law, and the effects of proposals to change the law. It regularly publishes a range of reports addressing costs, estimates and outlook related to the ACA. [27] For example, its report on "Federal Subsidies for Health Insurance Coverage for people under age 65: 2016 to 2026" was published in March 2016 and contained a number of projections of the ACA's impact. “CBO and JCT [the Joint Committee on Taxation] updated their estimates of the number of people under age 65 who have health insurance from various sources as well as their projections of the federal subsidies associated with that coverage. Those projections encompass a broad set of budgetary effects that operate under current law, including the effects of providing preferential tax treatment for employment-based coverage, costs for providing Medicaid coverage to people under age 65, and payments stemming directly from the ACA.” [28]

The bipartisanship affecting this policy had a significant influence on the collaboration and alignment between the actors involved in its implementation. There were some states that did not approve of the policy, which resulted in a fragmentation of its execution across the nation. The lack of support from certain states continues to exert a direct impact on the coverage of the policy, as they have the right to refuse the extension of Medicaid coverage. “Even if Republicans and conservatives continue to mount fierce political attacks calling for the repeal or gutting of the entire law, more consequential obstruction comes from state-level governors and legislators who can refuse to help establish exchanges to market subsidised private insurance and, more importantly, can block outright the expansion of Medicaid.” [29]

The interdependency of factors has repercussions across the board, affecting the costs and incentives affecting other actors, including the private sector. “Insurance companies are backing out of participating in Obamacare because fewer Americans than anticipated are signing up; that in turn raises insurances costs for everyone, which then further drives down participation. For some middle-income Americans, the subsidies available for buying Obamacare policies are not generous enough, and the fines for not having coverage are too small to encourage them to enrol in plans.” [30]

7 Obamacare failures that have hurt Americans , Diana Furchtgott-Roth, 25 March 2016, Marketwatch

Affordable Care Act, Congressional Budget Office

Affordable Care Act Facts , Obamacarefacts.com

Implementing Obamacare: A Review of CMS' Management of the Failed CO-OP Program , 13 September 2016, US House of Representatives Committee on Energy and Commerce

Lessons from the Fall of RomneyCare , Michael D. Tanner, January/February 2008, Cato Institute

Medicare and Medicaid Demonstrations and Pilots , the CMS Innovation Center, and the Shared Savings (ACO) Program, Association of American Medical Colleges

MN Gov says ObamaCare “no longer affordable”, Brian Sikma, 13 October 2016, The Insurgent

Obama Says Romney's Example Shows Health Care Reform Will Work , Denver Nicks, 30 October 2013, Time

ObamaCare (Affordable Care Act) Is Not An Insurance Or Healthcare Problem , Cameron Keng, 2 October, Forbes

Obamacare: what the Affordable Care Act means for patients and physicians, Mark A Hall, Richard Lord, 2014, Center for Bioethics Health and Society, Wake Forest University

ObamaCare's Failure And Moving Health Care Policy In A New Direction , Brian Blase, 15 December 2015, Forbes

Opinions on Obamacare remain divided along party lines as Supreme Court hears new challenge , Seth Motel, 4 March 2015, Pew Research Center

Public Support For Obamacare Grows As Less Than 30 Percent Of Americans Support Repeal , Keith Brekhus, 21 April 2015, Politic us USA

Stakeholders spend big on ACA ads, Paige Winfield Cunningham, 1 October 2013, Politico

The Politics of Obamacare, Social Security and Medicare, Scholars Strategy Network

The Real Story of Obamacare's Birth , Norm Ornstein, 6 July 2015, The Atlantic

Why is Obamacare so controversial? , 11 November 2016, BBC News

Why public opinion on ObamaCare should worry us all , Lawrence Jacobs and Suzanne Mettler, 21 June 2016, The Hill

The Public Impact Fundamentals - A framework for successful policy

This case study has been assessed using the Public Impact Fundamentals, a simple framework and practical tool to help you assess your public policies and ensure the three fundamentals - Legitimacy, Policy and Action are embedded in them.

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The ICAEW Advanced level exams present real-life scenarios, with greater complexity and wider implications than the Professional Level exams.

The Case Study exam will present a complex business issue which will require problem solving, the identification of ethical implications and your ability to provide effective solutions.

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How to Master the ICAEW ACA Case Study Marking Key

Four blogs into the ACA Case Study and you might just start to think that I am slightly obsessed with this exam. The ACA Case Study is so different to any other of the 15 ACA exams that I feel there is so much to discuss for this exam. I was not planning to go into detail about the Case Study marking key. However, I have recently realised many who are independently studying or who are taught by different providers may not have all these details or the same level of understanding. In order to pass the ACA Case Study, you do really need to understand how it is marked . Today this is exactly what I will delve into.

You may also want to refer to the other three ICAEW ACA Case Study blogs: ICAEW Case Study Made Easy , ICAEW Software Testing and How Much Preparation is Enough? .

Aca case study marking key terms, maximum marks and marking, different ways to fail the icaew aca case study, overall assessment criteria, executive summary (es), requirements – generic aca case study marking key, marking yourself, aca case study wrap up.

If you are unaware of the exam structure for the ACA Case Study exam, I would recommend reading the Made Easy blog before reading details about the Case Study marking key. You essentially need to know the report you are required to produce is split into an executive summary and three equally weighted requirements .

This will overlap with the ICAEW Case Study Made Easy blog where I have briefly touched on the marking key. The ACA Case Study marking key can be explained using the following terms: “ boxes ” and “ diamonds “, where boxes are skills assessment boxes (SABs). The meaning of these will get clearer as we go on but to give a quick explanation, the Case Study marking key is split into 40 boxes :

The executive summary has 6 boxes (2 for each requirement)
Each requirement (r1, r2 and r3) has 11 boxes
There is one final box for the overall assessment criteria

Within each box, there are diamonds /bullet points that you need to hit to get marks. The maximum mark for each box is 6 marks . Each box will have up to six “diamonds” that are up for grabs from your answer. For example, one diamond could be met by stating revenue increased by x amount/(y %) based on the case study numbers. This will make more sense once you start looking at mock exams for the ICAEW ACA Case Study.

The Case Study marking key is not completely straight forward. It is not one mark per one diamond. Here is how it works:

From the table above you can see you do not benefit from getting more than four diamonds as you will be capped at 6 marks . You may be in fact wasting time and time is definitely of the essence in the ACA Case Study even if it is 4 hours . However, it is difficult to know whether a point you make is a valid diamond or not so it is perhaps best to aim for 5 diamonds at most in each box of the Case Study marking key.

What makes the ACA Case Study very different from the other exams is that there are no predetermined correct answers . I have been told only after assessing the first say 100 papers are the diamonds and the marking key put together. You need to write what everyone else writes so state the obvious and work together when preparing beforehand! This ACA Advanced Level Telegram group (essentially WhatsApp without phone numbers) where you can connect with other students may help.

The maximum marks for the ACA Case Study is 240 marks . Remember, there are 40 boxes and the maximum mark is 6 per box. You may think as the ICAEW ACA Case study exam is 50% to pass , you just need 120 marks to pass. Again, the Case Study really is not that straightforward. 120 marks across the whole paper will not cut it. You actually need 50% in each requirement (including the executive summary) to pass. Getting 66 marks in requirement 1 but 20 marks in requirement 3 is a no go. However, this is not the only way to fail the ACA Case Study…

The below ways to fail which I will touch upon are less black and white than the 50% in each requirement criteria. This is a very grey area and is not nice for anybody in this position. I have come across candidates who have achieved 50% in each requirement and still failed. Below outline the other potential reasons why one could fail the ACA Case Study exam:

As mentioned, achieving less than the overall 50% pass mark.
Achieving less than 50% in each individual requirement and being deemed incompetent often. You really need to show you can produce a strong report to be on the safe side. In each requirement, you should ideally be getting over 50% competent grades (CC or SC) which means six or more boxes. Five SCs (30 marks) and the remaining ID/IC grades (6 marks) would get you above 50% in the requirement (33 marks) but may not be enough to pass.
Missing out parts of the report. If you score above 50% in all requirements but have missed out 4-5 skills assessment boxes, you are likely to fail. There cannot be gaps in the report. A fail could also be for writing poor conclusions and recommendations throughout.
This exam is all about balance . If you have too many NA and ID grades, this could also be a factor of failure.

Essentially, the examiner wants to see that you can write a complete and balanced report. If you do not do as ICAEW say in this exam you will be punished and you will fail. This really is a grey area , especially as for those marginal scripts there is re-moderation to decide which scripts pass and which fail. It does seem quite harsh as surely getting 50% would be enough but examiners will only want to pass a good quality report. Further marking details can be found here from ICAEW and is definitely worth a read.

ACA Case Study Marking Key

This one box will look as follows (imagine the dashes are diamonds):

If you meet all four diamonds, you will show you are clearly competent and should get 6 marks.

The executive summary is essentially meant to summarise each of the three requirements and provide the conclusion and recommendations. It is at the start of the report so if somebody did not have time to read the full report, they would be able to understand exactly what is covered by reading the ES. The Case Study marking key for the ES is as follows. Note this is very generic and may differ case study to case study:

ICAEW in recent years have been very clear that you cannot simply copy and paste the report into the executive summary. The two must differ otherwise you will not score any marks and this could result in failure! Spot the difference below…

I will keep this much more generic and do all three requirements in one. Through these boxes you will need to demonstrate that you can: assimilate and use information; structure problems and solutions; apply judgement and form conclusions and recommendations.

If you have not started any mocks yet, this may all look like gibberish to you. Trust me on this one, once you have been through a mock or so, you will be very grateful for the above tables. For the requirements, you essentially get two boxes for the appendices, two for each of the sub-requirements stated in the question and then two for the conclusions and recommendations. Keeping this in mind when you write your report is crucial to doing well.

If your ACA Case Study is marked by somebody else, I would highly recommend going through this yourself afterwards. This will really help you to understand what kind of points scores diamonds and which do not. You can also see where you are perhaps going overboard or not writing enough .

What I think is very beneficial about going through the Case Study marking key is that you will spot where things are in bold. You may have noticed this above. If “and figures” is written in bold, it means you cannot get the diamond without any numbers. Where I slipped up initially is that I was not including figures or sometimes comparative figures that were needed. You also sometimes must write the £ and % change to get a diamond. It really is worth going through this to maximise your diamond potential.

Hopefully this blog has stressed to you the importance of timings . If you get too many NA boxes, you are likely to fail. This means you cannot just miss areas out and move on but you do need to do it all . If you want help with your timings as well as planning sheets and help with requirement 1’s appendix, join my journey. I will be sending these out to you for free within a few days of joining.

Understanding the Case Study marking key is essential to do well in this exam. I can recall going through this for a full morning in college to make sure we really understood the marking key. If you do not have a solid understanding of this, as well as the different ways to fail, you may just do so. Hopefully this blog has been insightful and helped in some way. I would even suggest taking those generic marking keys into the exam with you so you do not miss anything out.

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Method of assessment

The ACA Case Study exam itself is 4 hours and given it forms part of the Advanced level, it can only be sat in one of two exam periods. These are in July and November each year.

As such, in each exam sitting all candidates will be set the same questions.

The exam will consist of three questions, and ethical issues and problems will likely appear in both questions.

The exam is fully open book and therefore students are permitted to take any written or printed material into the exam – this includes the pre-release.

How to pass ACA Case Study Exam

Without doubt the most difficult element of this exam and the main reason that candidates fail is the time pressure.

I’m order to pass this exam, you need to be regimented with your time keeping. You need to go into the exam having already worked out how many minutes per mark you are giving yourself, with a plan as to at what time you are going to move on to the next question.

In addition, make sure that the case that you have been given is tabbed up and organised, so you are able to flick to a particular area very quickly. I found a good way of doing this was to create an index which was colour coded. This was the way I found easiest to find pieces of information as I needed them.

Finally, make sure that you practice the first question over and over again. The first question of the paper is virtually the same in every sitting in that you are required to calculate year on year variances against the financial results presented in the pre-release case. This can be an easy way to pick up some marks without using up too much time.

How hard is the ACA Case Study Exam?

As Case Study is the final exam, many would expect this to be the hardest exam of all. However, especially in terms of content, this is not the case.

The difficulty here lies in the time pressure and the very specific mark scheme which comes with this exam. As such, timed practice papers are key to passing this exam.

That said, assuming preparation has been thorough, this exam is certainly passable.

I would give this exam a 4 out of 5 for difficulty.

The pass rate for this exam is 76.83%

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I failed Case twice (with another provider) and did the True Taste Limited Case in November and finally passed! The course that you offered was exceptionally useful as the hints and tips provided by [tutor name] allowed you to get under the business and treat it as if it's your own. The Mock Exams also allowed me to stick to time and practice over and over again and gave me good guidelines on how to set the Appendices and the markscheme gave you a good indication on whether I'm hitting those dreaded 40 Boxes.

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When I sat Case Study previously, I received no valuable technique or high level tips (something your materials are full of) on the courses I attended with [XXX]. I actually enjoyed revising for the exam this time (using your method) as I had a clear approach when attempting past papers; the Mock Exams were fantastic. I received a 'mock pack' of 2 Mock Exams from [XXX] which was 8 pages long so it was such a relief when I saw how detailed (198 pages in total) and focused the 5 Mock Exams provided by you were.

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Open access
Published: 15 May 2024

Arresting failure propagation in buildings through collapse isolation

Nirvan Makoond ORCID: orcid.org/0000-0002-5203-6318 1 ,
Andri Setiawan ORCID: orcid.org/0000-0003-2791-6118 1 ,
Manuel Buitrago ORCID: orcid.org/0000-0002-5561-5104 1 &
Jose M. Adam ORCID: orcid.org/0000-0002-9205-8458 1

Nature volume 629 , pages 592–596 ( 2024 ) Cite this article

11k Accesses

226 Altmetric

Metrics details

Civil engineering
Mechanical engineering

Several catastrophic building collapses 1 , 2 , 3 , 4 , 5 occur because of the propagation of local-initial failures 6 , 7 . Current design methods attempt to completely prevent collapse after initial failures by improving connectivity between building components. These measures ensure that the loads supported by the failed components are redistributed to the rest of the structural system 8 , 9 . However, increased connectivity can contribute to collapsing elements pulling down parts of a building that would otherwise be unaffected 10 . This risk is particularly important when large initial failures occur, as tends to be the case in the most disastrous collapses 6 . Here we present an original design approach to arrest collapse propagation after major initial failures. When a collapse initiates, the approach ensures that specific elements fail before the failure of the most critical components for global stability. The structural system thus separates into different parts and isolates collapse when its propagation would otherwise be inevitable. The effectiveness of the approach is proved through unique experimental tests on a purposely built full-scale building. We also demonstrate that large initial failures would lead to total collapse of the test building if increased connectivity was implemented as recommended by present guidelines. Our proposed approach enables incorporating a last line of defence for more resilient buildings.

Frequent disturbances enhanced the resilience of past human populations

Critical transitions in the Amazon forest system

Clarifying the four core effects of high-entropy materials

Disasters recorded from 2000 to 2019 are estimated to have caused economic losses of US$2.97 trillion and claimed approximately 1.23 million lives 11 . Most of these losses can be attributed to building collapses 12 , which are often characterized by the propagation of local-initial failures 13 that can arise because of extreme or abnormal events such as earthquakes 13 , 14 , 15 , 16 , floods 17 , 18 , 19 , 20 , storms 21 , 22 , landslides 23 , 24 , explosions 25 , vehicle impacts 26 and even construction or design errors 6 , 26 . As the world faces increasing trends in the frequency and intensity of extreme events 27 , 28 , it is arguably now more important than ever to design robust structures that are insensitive to initial damage 13 , 29 , irrespective of the underlying threat causing it.

Most robustness design approaches used at present 8 , 9 , 30 , 31 aim to completely prevent collapse initiation after a local failure by providing extensive connectivity within a structural system. Although these measures can ensure that the load supported by a failed component is redistributed to the rest of the structure, they are neither viable nor sustainable when considering larger initial failures 13 , 25 , 32 . In these situations, the implementation of these approaches can even result in collapsing parts of the building pulling down the rest of the structure 10 . The fact that several major collapses have occurred because of large initial failures 6 raises serious concerns about the inadequacy of the current robustness measures.

Traditionally, research in this area has focused on preventing collapse initiation after initial failures rather than on preventing collapse propagation. This trend dates back to the first impactful studies in the field of structural robustness, which were performed after a lack of connectivity enabled the progressive collapse of part of the Ronan Point tower in 1968 (ref. 33 ). Although completely preventing any collapse is certainly preferable to limiting the extent of a collapse, the occurrence of unforeseeable incidents is inevitable 34 and major building collapses keep occurring 1 , 2 , 3 .

Here we present an original approach for designing buildings to isolate the collapse triggered by a large initial failure. The approach, which is based on controlling the hierarchy of failures in a structural system, is inspired by how lizards shed their tails to escape predators 35 . The proposed hierarchy-based collapse isolation design ensures sufficient connectivity for operational conditions and after local-initial failures for which collapse initiation can be completely prevented through load redistribution. These local-initial failures can even be greater than those considered by building codes. Simultaneously, the structural system is also designed to separate into different parts and isolate a collapse when its propagation would otherwise be inevitable. As in the case of lizard tail autotomy 35 , this is achieved by promoting controlled fracture along predefined segment borders to limit failure propagation. In this work, hierarchy-based collapse isolation is applied to framed building structures. Developing this approach required a precise characterization of the collapse propagation mechanisms that need to be controlled. This was achieved using computational simulations that were validated through a specifically designed partial collapse test of a full-scale building. The obtained results demonstrate the viability of incorporating hierarchy-based collapse isolation in building design.

Hierarchy-based collapse isolation

Hierarchy-based collapse isolation design makes an important distinction between two types of initial failures. The first, referred to as small initial failures, includes all failures for which it is feasible to completely prevent the initiation of collapse by redistributing loads to the remaining structural system. The second type of initial failure, referred to as large initial failures, includes more severe failures that inevitably trigger at least a partial collapse.

The proposed design approach aims to (1) arrest unimpeded collapse propagation caused by large initial failures and (2) ensure the ability of a building to develop alternative load paths (ALPs) to prevent collapse initiation after small initial failures. This is achieved by prioritizing a specific hierarchy of failures among the components on the boundary of a moving collapse front.

Buildings are complex three-dimensional structural systems consisting of different components with very specific functions for transferring loads to the ground. Among these, vertical load-bearing components such as columns are the most important for ensuring global structural stability and integrity. Therefore, hierarchy-based collapse isolation design prevents the successive failure of columns, which would otherwise lead to catastrophic collapse. Although the exact magnitude of dynamic forces transmitted to columns during a collapse process is difficult to predict, these forces are eventually limited by the connections between columns and floor systems. In the proposed approach, partial-strength connections are designed to limit the magnitude of transmitted forces to values that are lower than the capacity of columns to resist unbalanced forces (see section ‘ Building design ’). This requirement guarantees a specific hierarchy of failures during collapse, whereby connection failures always occur before column failures. As a result, the collapse following a large initial failure is always restricted to components immediately adjacent to those directly involved in the initial failure. However, it is still necessary to ensure a lower bound on connection strengths to activate ALPs after small initial failures. Therefore, cost-effective implementation of hierarchy-based collapse isolation design requires finding an optimal balance between reducing the strength of connections and increasing the capacity of columns.

To test and verify the application of our proposed approach, we designed a real 15 m × 12 m precast reinforced concrete building with two 2.6-m-high floors. This basic geometry represents a building size that can be built and tested at full-scale while still being representative of current practices in the construction sector. The structural type was selected because of the increasing use of prefabricated construction for erecting high-occupancy buildings such as hospitals and malls because of several advantages in terms of quality, efficiency and sustainability 36 .

The collapse behaviour of possible design options (Extended Data Fig. 1 ) subjected to both small and large initial failures was investigated using high-fidelity collapse simulations (Fig. 1 ) based on the applied element method (AEM; see section ‘ Modelling strategy ’). The ability of these simulations to accurately represent collapse phenomena for the type of building being studied was later validated by comparing its predictions to the structural response observed during a purposely designed collapse test of a full-scale building (Extended Data Fig. 2 and Supplementary Video 7 ).

a , Partial-strength beam–column connection optimized for hierarchy-based collapse isolation. b , Partial collapse of a building designed for hierarchy-based collapse isolation (design H) after the loss of a corner column and two penultimate-edge columns. c , Total collapse of conventional building design (design C) after the same large initial failure scenario.

Following the preliminary design of a structure to resist loads suitable for office buildings, two building design options considering different robustness criteria were further investigated (see section ‘ Building design ’). The first option, design H (hierarchy-based), uses optimized partial-strength connections and enhanced columns (Fig. 1a ) to fulfil the requirements of hierarchy-based collapse isolation design. The second option, design C (conventional), is strictly based on code requirements and provides a benchmark comparison for evaluating the effectiveness of the proposed approach. It uses full-strength connections to improve robustness as recommended in current guidelines 37 and building codes 8 , 9 .

Simulations predicted that both design H and design C could develop stable ALPs that are able to completely prevent the initiation of collapse after small initial failure scenarios that are more severe than those considered in building codes 8 , 9 (Extended Data Fig. 3 ).

When subjected to a larger initial failure, simulations predict that design H can isolate the collapse to only the region directly affected by the initial failure (Fig. 1b ). By contrast, design C, with increased connectivity, causes collapsing elements to pull down the rest of the structure, leading to total collapse (Fig. 1c ). These two distinct outcomes demonstrate that the prevention of unimpeded collapse propagation can only be ensured when hierarchy-based collapse isolation is implemented (Extended Data Fig. 4 and Supplementary Video 1 ).

Testing a full-scale precast building

To confirm the expected performance improvement that can be achieved with the hierarchy-based collapse isolation design, a full-scale building specimen corresponding to design H was purposely built and subjected to two phases of testing as part of this work (Fig. 2a and Supplementary Information Sections 1 and 2 ). The precast structure was constructed with continuous columns cast together with corbels (Supplementary Video 4 ). The columns were cast with prepared dowel bars and sleeves for placing continuous top beam reinforcement bars through columns (Fig. 2b,c ). The bars used for these two types of reinforcing element (Fig. 1a ) were specifically selected to produce partial-strength connections. These connections are strong enough for the development of ALPs after small initial failures but weak enough to enable hierarchy-based collapse isolation after large initial failures.

a , Full-scale precast concrete structure and columns removed in different testing phases. The label used for each column is shown. The location of beams connecting the different columns is indicated by the dotted lines above the second-floor level. The expected collapse area in the second phase of testing is indicated. b , Typical first-floor connection before placement of beams during construction. c , Typical second-floor connection after placement of precast beams during construction. Both b and c show columns with two straight precast beams on either side (C2, C3, C6, C7, C10 and C11). d , Device used for quasi-static removal of two columns in the first phase of testing. e , Three-hinged mechanism used for dynamic removal of corner column in the second phase of testing.

After investigating different column-removal scenarios from different regions of the test building (see section ‘ Experiment and monitoring design ’, Extended Data Fig. 5 and Supplementary Video 2 ), two phases of testing were defined to capture relevant collapse-related phenomena and validate the effectiveness of hierarchy-based collapse isolation. Separating the test into two phases allowed two different aspects to be analysed: (1) the prevention of collapse initiation after small initial failures and (2) the isolation of collapse after large initial failures.

Phase 1 involved the quasi-static removal of two penultimate-edge columns using specifically designed removable supports (Fig. 2d and Extended Data Fig. 6 ). This testing phase corresponds to a small initial failure scenario for which design H was able to develop ALPs to prevent collapse initiation. Phase 2 reproduced a large initial failure through the dynamic removal of the corner column found between the two previously removed columns using a three-hinged collapsible column (Fig. 2e ).

During both testing phases, a distributed load (11.8 kN m −2 ) corresponding to almost twice the magnitude specified in Eurocodes 38 for accidental design situations (6 kN m −2 ) was imposed on bays expected to collapse in phase 2 (Fig. 2a and Supplementary Video 5 ). Predictive simulations indicated that the failure mode and overall collapse would be almost identical when comparing this partial loading configuration with that in which the entire building is loaded (Supplementary Video 3 ). However, the partial loading configuration turns out to be more demanding for the part of the structure expected to remain upright as evidenced by the greater drifts it produces during collapse (see section ‘ Experiment and monitoring design ’ and Extended Data Fig. 7 ). The structural response during all phases of testing was extensively monitored with an array of different sensors (see section ‘ Experiment and monitoring design ’ and Supplementary Information Section 3 ) that provided the information used as a basis for the analyses presented in the following sections.

Preventing collapse initiation

Collapse initiation was completely prevented after the removal of two penultimate-edge columns in phase 1 of testing (Fig. 3a ), demonstrating that design H complies with the robustness requirements included in current building standards 8 , 9 , 39 . As this initial failure scenario is more severe than those considered by standardized design methods 8 , 9 , 30 , it represents an extreme case for which ALPs are still effective. As such, the outcome of phase 1 demonstrates that implementing hierarchy-based collapse isolation design does not impair the ability of this structure to prevent collapse initiation.

a , Test building during phase 1 of testing after removal of columns C8 and C11. The beam depth ( h ) used to compute the ratio plotted in b is shown and the location of the strain measurement plotted in c is indicated. b , Evolution of beam deflection expressed as a ratio of beam depth at the location of removed column C11. The chord rotation of the beams bridging over this removed column is also indicated using a secondary vertical axis. c , Strain increase in continuity reinforcement in the second-floor beam between C12 and C11.

Source Data

Analysis of the structural response during phase 1 (Supplementary Information Section 4 ) shows that collapse was prevented because of the redistribution of loads through the beams (Fig. 3b,c ), columns (Extended Data Fig. 8 ) and slabs (Supplementary Report 4 ) adjacent to the removed columns. The beams bridging over the removed columns sustained loads through flexural action, as evidenced by the magnitude of the vertical displacement recorded at the removal locations (Fig. 3b ). These values were far too small to allow the development of catenary forces, which only begin to appear when displacements exceed the depth of the beam 40 .

The flexural response of the structure after the loss of two penultimate-edge columns was only able to develop because of the specific reinforcement detailing introduced in the design. This was verified by the increase in tensile strains recorded in the continuous beam reinforcement close to the removed column (Fig. 3c ) and in ties placed between the precast hollow-core planks in the floor system close to column C7 (Supplementary Information Section 4 ). The latter also proves that the slabs contributed notably to load redistribution after column removal.

In general, the structure experienced only small movements and suffered very little permanent damage during phase 1 (Supplementary Information Section 4 ), despite the high imposed loads used for testing. The only reinforcement bars showing some signs of yielding were the continuous reinforcement bars of beams close to the removed columns (Fig. 3c ).

Arresting collapse propagation

Following the removal of two penultimate-edge columns in phase 1, the sudden removal of the C12 corner column in phase 2 triggered a collapse that was arrested along the border delineated by columns C3, C7, C6 and C10 (Fig. 4a–d and Supplementary Video 6 ). Thus, the viability of hierarchy-based collapse isolation design is confirmed.

a , Collapse sequence during phase 2 of testing. b , Partial collapse of full-scale test building (design H) after the removal of three columns. The segment border in which collapse propagation was arrested is indicated. The axes shown at column C9 correspond to those used in f to indicate the changing direction of the resultant drift measured at this location. c , Failure of beam–column connections at collapse border. d , Debonding of reinforcement in the floor at collapse border. e , Change in average axial strains measured in column C7. A negative change represents an increase in compressive strains. f , Magnitude of resultant drift measured at C9. g , Change in direction of resultant drift measured at C9. The initial drift after phase 1 of testing and the residual drift after the upright part of the building stabilized are also shown in the plot.

During the initial stages following the removal of C12, the collapsing bays next to this column pulled up the columns on the opposite corner of the building (columns C1, C3 and C6). During this process, column C7 behaves like a pivot point, experiencing a significant increase in compressive forces (Fig. 4e and Supplementary Information Section 5 ). This phenomenon was enabled by the connectivity between collapsing parts and the rest of the structure. If allowed to continue, this could have led to successive column failures and unimpeded collapse propagation. However, during the test, the rupture of continuous reinforcement bars (Fig. 4c ) occurred as the connections failed and halted the transmission of forces to columns. These connection failures occurred before any column failures, as intended by the hierarchy-based collapse isolation design of the structural system. Specifically, this type of connection failure occurred at the junctions with the two columns (C7 and C10) immediately adjacent to the failure origin (around C8, C11 and C12), effectively segmenting the structure along the border shown in Fig. 4b . Segmentation along this border was completed by the total separation of the floor system, which was enabled by the debonding of slab reinforcements at the segment border (Fig. 4d and Supplementary Video 8 ).

Observing the building drift measured at the top of column C9 (Fig. 4f ) enabled us to better understand the nature of forces acting on the building further away from the collapsing region. The initial motion shows the direction of pulling forces generated by the collapsing elements (Fig. 4g ). This drift peaks very shortly after the point in time when separation of the collapsing parts occurs (Fig. 4f ). After this peak, the upright part of the structure recoiled backwards and experienced an attenuated oscillatory motion before finding a new stable equilibrium (Fig. 4g ). The magnitude of the measured peak drift is comparable to the drift limits considered in seismic regions when designing against earthquakes with a 2,500-year return period 41 (Supplementary Information Section 5 ). This indicates that the upright part of the structure was subjected to strong dynamic horizontal forces as it was effectively tugged by the collapsing elements falling to the ground. The building would have failed because of these unbalanced forces had hierarchy-based collapse isolation design not been implemented.

The upright building segment suffered permanent damages as evidenced by the residual drift recorded at the top of column C9 (Fig. 4g ). This is further corroborated by the fact that several reinforcement bars in this part of the structure yielded, particularly in areas close to the segment border (Supplementary Report 5 ). Despite the observed level of damage, safe evacuation and rescue of people from this building segment would still be possible after an extreme event, saving lives that would have been lost had a more conventional robustness design (design C) been used instead.

Discussion and future outlook

Our results demonstrate that the extensive connectivity adopted in conventional robustness design can lead to catastrophic collapse after large initial failures. To address this risk, we have developed and tested a collapse isolation design approach based on controlling the hierarchy of failures occurring during the collapse. Specifically, it is ensured that connection failures occur before column failures, mitigating the risk of collapse propagation throughout the rest of the structural system. The proposed approach has been validated through the partial collapse test of a full-scale precast building, showing that propagating collapses can be arrested at low cost without impairing the ability of the structure to completely prevent collapse initiation after small initial failures.

The reported findings show a last line of defence against major building collapses due to extreme events. This paves the way for the proposed solution to be developed, tested and implemented in different building types with different building elements. This discovery opens opportunities for robustness design that will lead to a new generation of solutions for avoiding catastrophic building collapses.

Building design

Our hierarchy-based collapse isolation approach ensures buildings have sufficient connectivity for operational conditions and small initial failures, yet separate into different parts and isolate a collapse after large initial failures. We chose a precast construction as our main structural system for our case study. A notable particularity of precast systems compared with cast-in-place buildings is that the required construction details can be implemented more precisely. We designed and systematically investigated two precast building designs: designs H and C.

Design H is our building design in which the hierarchy-based collapse isolation approach is applied. Design H was achieved after several preliminary iterations by evaluating various connections and construction details commonly adopted in precast structures. The final design comprises precast columns with corbels connected to a floor system (partially precast beams and hollow-core slabs) through partial-strength beam–column connections (Extended Data Fig. 1 and Supplementary Information Section 1 ). This partial-strength connection was achieved by (1) connecting the bottom part of the beam (precast) to optimally designed dowel bars anchored to the column corbels and (2) passing continuous top beam bars through the columns. With this partial-strength connection, we have more direct control over the magnitude of forces being transferred from the floor system to the columns, which is a key aspect for achieving hierarchy-based collapse isolation. The hierarchy of failures was initially implemented through the beam–column connections (local level) and later verified at the system (global) level.

At the local level, three main components are designed according to the hierarchy-based concept: (1) top continuity bars of the beams; (2) dowel bars connecting beams to corbels; and (3) columns.

Top continuity bars of beams: To allow the structural system to redistribute the loads after small initial failures, top reinforcement bars in all beams were specifically designed to fulfil structural robustness requirements (Extended Data Fig. 3 ). Particularly, we adopted the prescriptive tying rules (referred to as Tie Forces) of UFC 4-023-03 (ref. 9 ) to perform the design of the ties. The required tie strength F i in both the longitudinal and transverse directions for the internal beams is expressed as

For the peripheral beams, the required tie strength F P is expressed as

where w F = floor load (in kN m −2 ); D = dead load (in kN m −2 ); L = live load (in kN m −2 ); L 1 = greater of the distances between the centres of the columns, frames or walls supporting any two adjacent floor spaces in the direction under consideration (in m); L P = 1.0 m; and W C = 1.2 times dead load of cladding (neglected in this design).

These required tie strengths are fulfilled with three bars (20 mm diameter) for the peripheral beams and three bars (25 mm diameter) for the internal beams. These required reinforcement dimensions were implemented through the top bars of the beam and installed continuously (lap-spliced, internally, and anchored with couplers at the ends) throughout the building (Extended Data Fig. 1 ).

Dowel bars connecting the beam and corbel of the column: The design of the dowel bars is one of the key aspects in achieving partial-strength connections that fail at a specific threshold to enable segmentation. These dowel bars would control the magnitude of the internal forces between the floor system and column while allowing for some degree of rotational movement. The dowels were designed to resist possible failure modes using expressions proposed in the fib guidelines 37 . Several possible failure modes were checked: splitting of concrete around the dowel bars, shear failure of the dowel bars and forming a plastic hinge in the dowel. The shear capacity of a dowel bar loaded in pure shear can be determined according to the Von Mises yield criterion:

where f yd is the design yield strength of the dowel bar and A s is the cross-sectional area of the dowel bar. In case of concrete splitting failure, the highly concentrated reaction transferred from the dowel bar shall be designed to be safely spread to the surrounding concrete. The strut and tie method is recommended to perform such a design 42 . If shear failure and splitting of concrete do not occur prematurely, the dowel bar will normally yield in bending, indicated by the formation of a plastic hinge. This failure mode is associated with a significant tensile strain at the plastic hinge location of the dowel bar and the crushing of concrete around the compression part of the dowel. The shear resistance achieved at this state for dowel (ribbed) bars across a joint of a certain width (that is, the neoprene bearing) can be expressed as

where α 0 is a coefficient that considers the bearing strength of concrete and can be taken as 1.0 for design purposes, α e is a coefficient that considers the eccentricity, e is the load eccentricity and shall be computed as the half of the joint width (half of the neoprene bearing thickness), Φ and A s are the diameter and the cross-sectional area of the dowel bar, respectively, f cd,max is the design concrete compressive strength at the stronger side, σ sn is the local axial stress of the dowel bar at the interface location, ${f}_{{\rm{yd}},{\rm{red}}}={f}_{{\rm{yd}}}-{\sigma }_{{\rm{sn}}}$ is the design yield strength available for dowel action, f yd is the yield strength of the dowel bar and μ is the coefficient of friction between the concrete and neoprene bearing. By performing the checks on these three possible failure modes, we selected the final (optimum) design with a two dowel bars (20 mm diameter) configuration.

Columns: The proposed hierarchy-based approach requires columns to have adequate capacity to resist the internal forces transmitted by the floor system during a collapse. By fulfilling this strength hierarchy, we can ensure and control that failure happens at the connections first before the columns fail, thus preventing collapse propagation. The columns were initially designed according to the general procedure prescribed by building standards. Then, the resulting capacity was verified using the modified compression field theory (MCFT) 43 to ensure that it was higher than the maximum expected forces transmitted by the connection to the floor system. MCFT was derived to consistently fulfil three main aspects: equilibrium of forces, compatibility and rational stress–strain relationships of cracked concrete expressed as average stresses and strains. The principal compressive stress in the concrete f c 2 is expressed not only as a function of the principal compressive strain ε 2 but also of the co-existing principal tensile strain ε 1 , known as the compression softening effect:

where f c 2max is the peak concrete compressive strength considering the perpendicular tensile strain, ${f}_{c}^{{\prime} }$ is the uniaxial compressive strength, and ${\varepsilon }_{{c}^{{\prime} }}$ is the peak uniaxial concrete compressive strain and can be taken as −0.002. In tension, concrete is assumed to behave linearly until the tensile strength is achieved, followed by a specific decaying function 43 . Regarding aggregate interlock, the shear stress that can be transmitted across cracks v ci is expressed as a function of the crack width w , and the required compressive stress on the crack f ci (ref. 44 ):

where a refers to the maximum aggregate size in mm and the stresses are expressed in MPa. The MCFT analytical model was implemented to solve the sectional and full-member response of beams and columns subjected to axial, bending and shear in Response 2000 software (open access) 45 , 46 . In Response 2000, we input key information, including the geometries of the columns, reinforcement configuration and the material definition for the concrete and the reinforcing bars. Based on this information, we computed the M – V (moment and shear interaction envelope) and M – N (moment and axial interaction envelope) diagrams that represent the capacity of the columns. The results shown in Extended Data Fig. 4 about the verification of the demand and capacity envelopes were obtained using the analytical procedure described here.

At the global level, the initially collapsing regions of the building generate a significant magnitude of dynamic unbalanced forces. The rest of the building system must collectively resist these unbalanced forces to achieve a new equilibrium state. Depending on the design of the structure, this phenomenon can lead to two possible scenarios: (1) major collapse due to failure propagation or (2) partial collapse only of the initially affected regions. The complex interaction between the three-dimensional structural system and its components must be accounted for to evaluate the structural response during collapse accurately. Advanced computational simulations, described in the ‘ Modelling strategy ’ section, were adopted to analyse the global building to verify that major collapse can be prevented. The final design obtained from the local-level analysis (top continuity bars, dowel bars and columns) was used as an input for performing the global computational simulations. Certain large initial failures deemed suitable for evaluating the performance of this building were simulated. In case failure propagation occurs, the original hierarchy-based design must be further adapted. An iterative process is typically required involving several simulations with various building designs to achieve an optimum result that balances the cost and desired collapse performance. The final iteration of design H, which fulfils both the local and global hierarchy checks, is provided in Extended Data Fig. 1 .

Design C is a conventional building design that complies with current robustness standards but does not explicitly fulfil our hierarchy-based approach. The same continuity bars used in design H were used in design C. We adopted a full-strength connection as recommended by the fib guideline 37 . The guideline promotes full connectivity to enhance the development of alternative load paths for preventing collapse initiation. In design C, we used a two dowel bars (32 mm diameter) configuration to ensure full connectivity when the beams are working at their maximum flexural capacity. Another main difference was that the columns in design C were designed according to codes and current practice (optimal solution) without explicitly checking that hierarchy-based collapse isolation criteria are fulfilled. The final design of the columns and connections adopted in design C is provided in Extended Data Fig. 1 .

Modelling strategy

We used the AEM implemented in the Extreme Loading for Structures software to perform all the computational simulations presented in this study 47 (Extended Data Figs. 2 – 5 and 7 and Supplementary Videos 1 , 2 , 3 and 7 ). We chose the AEM for its ability to represent all phases of a structural collapse efficiently and accurately, including element separation (fracture), contact and collision 47 . The method discretizes a continuum into small, finite-size elements (rigid bodies) connected using multiple normal and shear springs distributed across each element face. Each element has six degrees of freedom, three translational and three rotational, at its centre, whereas the behaviour of the springs represents all material constitutive models, contact and collision response. Despite the simplifying assumptions in its formulation 48 , its ability to accurately account for large displacements 49 , cyclic loading 50 , as well as the effects of element separation, contact and collision 51 has been demonstrated through many comparisons with experimental and theoretical results 47 .

Geometric and physical representations

We modelled each of the main structural components of the building separately, including the columns, beams, corbels and hollow-core slabs. We adopted a consistent mesh size with an average (representative) size of 150 mm. Adopting this mesh configuration resulted in a total number of 98,611 elements. We defined a specialized interface with no tensile or shear strength between the precast and cast-in-situ parts to allow for localized deformations that occur at these locations. The behaviour of the interface was mainly governed by a friction coefficient of 0.6, which was defined according to concrete design guidelines 52 , 53 , 54 . The normal stiffness of these interfaces corresponded to the stiffness of the concrete cast-in-situ topping. The elastomeric bearing pads supporting the precast beams on top of the corbels were also modelled with a similar interface having a coefficient of friction of 0.5 (ref. 55 ).

Element type and constitutive models

We adopted an eight-node hexahedron (cube) element with the so-called matrix-springs connecting adjacent cubes to model the concrete parts. We adopted the compression model in refs. 56 , 57 to simulate the behaviour of concrete under compression. Three specific parameters are required to define the response envelope: the initial elastic modulus, the fracture parameter and the compressive plastic strain. For the behaviour in tension, the spring stiffness is assumed to be linear (with the initial elastic modulus) until reaching the cracking point. The shear behaviour is considered to remain linear up to the cracking of the concrete. The interaction between normal compressive and shear stress follows the Mohr–Coulomb failure criterion. After reaching the peak, the shear stress is assumed to drop to a certain residual value affected by the aggregate interlock and friction at the cracked surface. By contrast, under tension, both normal and shear stresses drop to zero after the cracking point. The steel reinforcement bars were simulated as a discrete spring element with three force components: the normal spring takes the principal/normal forces parallel to the rebar, and two other springs represent the reinforcement bar in shear (dowelling). Three distinct stages are considered: elastic, yield plateau and strain hardening. A perfect bond behaviour between the concrete and the reinforcement bars was adopted. We assigned the material properties based on the results of the laboratory tests performed on reinforcement bars and concrete cylinders (Supplementary Information Section 2 ).

Boundary conditions and loading protocol

We assumed that all the ground floor columns are fully restrained in all six degrees of freedom at the base location. This assumption is reasonable, as we expected that the footing would provide sufficient rigidity to constrain any significant deformations. We assigned the reflecting domain boundaries to allow a realistic representation of the collapsing elements (debris) that might fall and rebound after hitting the ground. The ground level was assumed to be at the same elevation at which the column bases are restrained. We applied the additional imposed uniform distributed load as an extra volume of mass assigned to the slabs. To perform the column removal, we used the element removal feature that allows some specific designated elements to be immediately removed at the beginning of the loading stage. This represents a dynamic (sudden) removal, as we expected from the actual test.

Extended Data Tables 1 and 2 summarize all key parameters and assumptions adopted in the modelling process. To validate these assumptions for simulating the precast building designs described previously, it was ensured that the full-scale test performed as part of this work captured all relevant phenomena influencing collapse (large displacements, fracture, contact and collision).

Experiment and monitoring design

We used computational simulations of design H subjected to different initial failure scenarios to define a suitable testing sequence and protocol. The geometry, reinforcement configurations, connection system and construction details of the purpose-built specimen representing design H are provided in Supplementary Information Section 1 and Supplementary Video 4 .

Initial failure scenarios

Initial failure scenarios occurring in edge and corner regions of the building were prioritized for this study because they are usually exposed to a wider range of external threats 58 , 59 , 60 , 61 . After performing a systematic sensitivity study, we identified three critical scenarios (Extended Data Fig. 5 and Supplementary Video 2 ):

Scenario 1: a scenario involving a two-column failure—a corner column and the adjacent edge column. We determined that the required gravity loads to induce collapse equal 11.5 kN m −2 and that partial collapse would occur locally.

Scenario 2: a scenario involving a three-column failure—two corner columns and the edge column in between the two corner columns. We determined that the required gravity loads to induce collapse equal 8.5 kN m −2 and that segmentation (partially collapsing two bays) would take place only across one principal axis of the building.

Scenario 3: a scenario involving a three-column failure: one corner column and two edge columns on both sides of the corner column. We determined that the required gravity loads to induce collapse equal 7.0 kN m −2 and that segmentation (partially collapsing three bays) would take place across both principal axes of the building.

Scenario 3 was ultimately chosen after considering three main aspects: (1) it requires the lowest gravity loads to trigger partial collapse; (2) the failure mode involves activating segmentation mechanisms in two principal axes of the building (more realistic collapse pattern); and (3) the ratio of the area of the intact part and the collapsed part was predicted to be 50:50, leading to the largest collapse area among the three scenarios.

Testing phases

To allow us to investigate the behaviour of the building specimen under small and large initial failures in only one building specimen, we decided to perform two separate testing phases. Phase 1 involved the quasi-static (gradual) removal of two edge columns (C8 and C11), whereas phase 2 involved the sudden removal of the corner column (C12) found between the columns removed in phase 1. A uniformly distributed load of 11.8 kN m −2 was applied only on the bays directly adjacent to these three columns without loading the remaining bays (Supplementary Video 5 ). This was achieved by placing more than 8,000 sandbags in the designated bays on the two floors (the first- and second-floor slabs). We performed additional computational simulations to compare this partial loading configuration and loading of the entire building. The simulations indicated that both would have resulted in almost identical final collapse states (Extended Data Fig. 7 and Supplementary Video 3 ). However, the partial loading configuration introduced a higher magnitude of unbalanced moment to surrounding columns, which induces more demanding bending and shear in columns. Simulations confirmed that the lateral drift of the remaining part of the building would be higher when only three bays are loaded, indicating that its stability would be tested to a greater extent with this loading configuration (Extended Data Fig. 7 ).

Specially designed elements to trigger initial failures

We designed special devices to perform the column removal (Extended Data Fig. 6 ). For phase 1, we constructed two hanging concrete columns (C8 and C11) supported only on a vertical hydraulic jack. The pressure in the jack could be gradually released from a safe distance to remove the vertical reaction supporting the column. In phase 2, a three-steel-hinged column was used as the corner column. The middle part of the column represents a central hinge that was able to rotate if unlocked. During the second testing phase, we unlocked the hinge by pulling the column from outside the building using a forklift to induce a slight destabilization. This resulted in a sudden removal of the corner column C12 and the initiation of the collapse.

Monitoring plan

To monitor the structural behaviour, we heavily instrumented the building specimen with multiple sensors. A total of 57 embedded strain gauges, 17 displacement transducers and 5 accelerometers were placed at key locations in different parts of the structure (Extended Data Fig. 8 and Supplementary Information Section 3 ) during all phases of testing. The data from these sensors (Supplementary Information Sections 4 and 5 ) were complemented by the pictures and videos of the structural response captured by five high-resolution cameras and two drones (Supplementary Videos 6 and 8 ).

Data availability

All experimental data recorded during testing of the full-scale building are available from Zenodo ( https://doi.org/10.5281/zenodo.10698030 ) 62 . Source data are provided with this paper.

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Acknowledgements

This article is part of a project (Endure) that has received funding from the European Research Council (ERC) under the Horizon 2020 research and innovation programme of the European Union (grant agreement no. 101000396). We acknowledge the assistance of the following colleagues from the ICITECH-UPV institute in preparing and executing the full-scale building tests: J. J. Moragues, P. Calderón, D. Tasquer, G. Caredda, D. Cetina, M. L. Gerbaudo, L. Marín, M. Oliver and G. Sempértegui. We are also grateful to the Levantina, Ingeniería y Construcción S.L. (LIC) company for providing human resources and access to their facilities for testing. Finally, we thank A. Elfouly and Applied Science International for their support in performing simulations.

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N.M. prepared the main text, performed the computational simulations and validated the test results. A.S. analysed the experimental data, performed data curation and prepared the Methods section. M.B. contributed to the design of the building specimen, the design of the test and data curation. J.M.A. contributed to the design of the research methodology, supervised the research and was responsible for funding acquisition. N.M., A.S. and M.B. contributed to the execution of the experimental test and to preparing figures, extended data and supplementary information. All authors interpreted the test and simulation results and edited the paper.

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Correspondence to Jose M. Adam .

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Extended data figures and tables

Extended data fig. 1 summary of building designs..

General building layout, connection details, and reinforcement configurations of Design H (“Hierarchy-based”) and Design C (“Conventional”).

Extended Data Fig. 2 Comparison of measured experimental data and simulation predictions.

a, Location of shown comparisons. All data shown in panels b to d refer to the change in structural response following the sudden removal of column C12 (after having removed columns C8 and C11 in a previous phase). b, Change in axial load in lower part of column C7. c, Change in axial load in lower part of column C9. d , Change in drift measured in both directions parallel to each building side.

Extended Data Fig. 3 Computational simulations of Design H and Design C subjected to small initial failures.

Principal strains and relative vertical displacement at the location of column C11 after removal of columns C8 and C11 from Design H ( a ) and Design C ( b ).

Extended Data Fig. 4 Demand and capacity envelopes of internal forces in Designs H and C subjected to large initial failures.

Evolution of axial loads, bending moments, and shear forces in column C7 compared to lower and upper bounds of its capacity after the removal of columns C8, C11, and C12 from Design H ( a ) and Design C ( b ).

Extended Data Fig. 5 Initial failure scenarios considered for testing.

Simulation of three different initial failure scenarios that were considered for testing. Scenario 3 was selected for the experimental test.

Extended Data Fig. 6 Specially designed removable supports to perform column removals.

Removable supports designed for quasi-static column removals in phase 1 and sudden column removal in phase 2.

Extended Data Fig. 7 Comparison of simulations of fully loaded and partially loaded building specimen.

a, Loaded bays, deformed shape, and principal normal strains following the sudden removal of column C12 (after having removed columns C8 and C11 in a previous phase). b, Horizontal displacement in the east-west and north-south directions at the top of columns C1 and C9 (2nd floor).

Extended Data Fig. 8 Measured redistribution of column axial forces during phase 1.

Maximum change in axial load of columns during phase 1 of testing based on recorded strain measurements.

Supplementary information

Supplementary information.

This file contains a supplementary test report that covers as-built building design, material properties, monitoring plan, structural response in phase 1 of testing and structural response in phase 2 of testing.

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Supplementary video 1.

Structural response of designs H and C.

Supplementary Video 2

Initial failure scenarios.

Supplementary Video 3

Comparison of partial and full loading.

Supplementary Video 4

Construction of the building.

Supplementary Video 5

An aerial view of the building before the test.

Supplementary Video 6

Multiple perspectives of the partial collapse of the building specimen in testing phase 2.

Supplementary Video 7

Experimental and simulation comparison of the partial collapse in testing phase 2.

Supplementary Video 8

Post-collapse inspection drone video.

Source data

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Makoond, N., Setiawan, A., Buitrago, M. et al. Arresting failure propagation in buildings through collapse isolation. Nature 629 , 592–596 (2024). https://doi.org/10.1038/s41586-024-07268-5

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Flood vulnerability assessment in rural and urban informal settlements: case study of Karonga District and Lilongwe City in Malawi

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Published: 24 May 2024

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Isaac Kadono Mwalwimba 1 ,
Mtafu Manda 2 &
Cosmo Ngongondo 3

Flood vulnerability assessment (FVA) informs the disaster risk reduction and preparedness process in both rural and urban areas. However, many flood-vulnerable regions like Malawi still lack FVA supporting frameworks in all phases (pre-trans-post disaster). Partly, this is attributed to lack of the evidence-based studies to inform the processes. This study was therefore aimed at assessing households’ flood vulnerability (HFV) in rural and urban informal areas of Malawi, using case studies of Traditional Authority (T/A) Kilupula of Karonga District (KD) and Mtandire Ward in Lilongwe City (LC). A household survey was used to collect data from a sample of 545 household participants. Vulnerability was explored through a combination of underlying vulnerability factors (UVFs)-physical-social-economic-environmental and cultural with vulnerability components (VCs)-exposure-susceptibility and resilience. The UVFs and VCs were agglomerated using binomial multiple logit regression model. Variance inflation factor (VIF) was used to check the multicollinearity of variables in the regression model. HFV was determined based on the flood vulnerability index (FVI). The data were analysed using Multiple Correspondence Analysis (MCA), artificial neural network (ANN) and STATA. The results reveal a total average score of high vulnerability (0.62) and moderate vulnerability (0.52) on MCA in T/A Kilupula of Karonga District and Mtandire Ward of Lilongwe City respectively. The FVI revealed very high vulnerability on enviroexposure factors (EEFs) ( $0.9$ ) in LC and $(0.8$ ) in KD, followed by ecoresilience factors (ERFs) (0.8) in KD and $(0.6$ ) in LC and physioexposure factors (PEFs) ( $0.5)$ in LC besides 0.6 in KD for the combined UVFs and VCs. The study concludes that the determinants of households’ flood vulnerability are place settlement, low-risk knowledge, communication accessibility, lack of early warning systems, and limited access to income of household heads. The study recommends that an FVA framework should be applied to strengthen the political, legal, social, and economic responsibilities of government for building the resilience of communities and supporting planning and decision-making processes in flood risk management.

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1 Introduction

Floods are a natural hazard that many communities have to cope with. Climate change and variability have resulted in changes in terms of the frequency and magnitudes of flood-inducing storms in many regions (Hodgkins et al. 2017 ; Kundzewicz et al. 2019 ). The Emergency Events Database (CRED, 2019) reported that around 50,000 people died and approximately 10% of the world population was affected by floods between 2009 and 2019 (Moreira et al. 2021 ). In recent years, the world has deviated from flood hazard control to flood vulnerability assessments (Ndanusa et al. 2022; Ran et al. 2018). This is because the vulnerability of a community partly induces floods to become disasters (Nong and Sathyna 2020 ; Salami et al. 2017 ) and such assessments are important in strategic decision-making and planning (de Risi et al. 2013 ). Consequently, vulnerability assessment has become a primary component of flood hazard mitigation, preparedness and management (Ndanusa et al. 2022). Based on the findings of many studies in the assessment of flood vulnerability, it has been noted that several studies have not combined indicators of UVFs and VCs in their assessments. Those that have combined the indicators (Karagiorgos et al. 2016 ; Mwale 2014 ; Nazeer and Bork 2021 ) have not gone further to propose FVA frameworks to support decision-making, creating a gap which has been addressed in this current study. Anwana and Oluwatobi ( 2023 ) provided a review of the literature on flood vulnerability in informal settlements globally and in South Africa, in particular. Their review found a distinct knowledge gap in flood vulnerability studies. In the Ibadan metropolis area of Nigeria, Salami et al. ( 2017 ) proposed and applied a flood vulnerability assessment framework to provide flood vulnerability assessments of the human settlements and their preparedness to mitigate flood risk. The study established that previous experience of flooding was a key factor in awareness levels, although this awareness was not significantly related to the level of preparedness during flooding. De Risi et al. ( 2013 ) proposed a probabilistic and modular approach to analysing flood vulnerability in informal settlements of Dar es Salaam City in Tanzania. Alam et al. ( 2022 ) conducted a vulnerability assessment based on household views from the Dammar Char in Southeastern Bangladesh by constructing a vulnerability index using quantitative and qualitative data. The study revealed that, on average, the people living in the Dammar Char have a high vulnerability to coastal hazards and disasters. In North-West Khyber Pakhtunkhwa of Pakistan, Nazeer and Bork ( 2019 ) carried out a flood vulnerability assessment through different methodologies of rescaling, weighting and aggregation schemes to construct the flood vulnerability indices. The study found that the weighting scheme had a greater influence on the flood vulnerability ranking compared to data rescaling and aggregation schemes. Oyedele et al. ( 2022 ) analysed vulnerability to flooding in Kogi State of Nigeria as a function of exposure, susceptibility and lack of resilience using 16 sets of indicators. The indicators were normalized and aggregated to compute the flood vulnerability index for the 20 purposively selected communities. The study established that the selected communities had varying levels of risk of flooding, “very high” to “high” vulnerability to flooding. Munyai et al. ( 2019 ) examined flood vulnerability in three rural villages in South Africa’s northern Limpopo Province using a flood vulnerability index. The study revealed that all three villages have a “vulnerability to floods” level, from medium to high vulnerability. While all these studies have assessed flood vulnerability, a framework for guiding its assessment process has been not proposed. The lack of such a framework implies that flood risk reduction is not programmed to address current and future risks. This could be a reason why disaster risk management in Malawi, for example, is described as post-event humanitarian actions and reactive.

The Sentinels-4-African DRR rank Malawi position 11 out of 53 African countries affected by floods from 1927 to 2022 with statistics of 42 events, 948 deaths and 3531, 145 people affected (Danzeglocke et al. 2023). Similarly, the 2011 Climate Change Vulnerability Index by the British Risk Analysis Firm Maplecroft ranks Malawi 15 out of 16 countries with extreme risks to climate change impacts in the world. GOM (2023) indicates that over twenty-five disasters experienced in Malawi have been associated with severe rainfall events in the last decade. For instance, between the periods of 2015–2023, about four major floods induced by tropical cyclones have affected communities. The most destructive was the floods of 11–13 March 2023, influenced by tropical cyclone Freddy (TCF), which killed about 679 people, injured 2178 people, displaced about 563,602 people, and about 511 people were reported missing, including causing several other damages and loss in sectors such as agriculture, infrastructure, food security and health (GOM, 2023). A “state of disaster” was declared on the 13th of March in the districts that were affected by the cyclone namely; Blantyre City and District, Chikwawa District, Chiradzulu District, Mulanje District, Mwanza District, Neno District, Phalombe district, Nsanje district, Thyolo district and Zomba city and district. Relatedly, in January 2022, the passage of a tropical storm named “Ana” over southern Malawi with heavy rainfall caused rivers to overflow, floods and landslides. The flooding affected 19 districts in the southern region and among the heavily affected districts were Chikwawa, Mulanje, Nsanje and Phalombe. The event caused 46 deaths, and 206 injuries, 152,000 people were displaced with several infrastructural damages. The country also experienced the worst cyclone Idai which originated from Mozambique in 2019. This cyclone induced floods which killed 60 people as well as affected 975,000, displaced 86,976 and injured 672 people (PDNA, 2019 ). In January and February 2015, over 1 million people were affected and about US$ 335 million was incurred on infrastructural damage (PDNA, 2015 ). However, floods have been considered largely as a rural manifestation during the past years (Chawawa, 2018 ), with district councils taking the lead in flood management through the development of disaster risk management strategies and policies (Manda and Wanda, 2017 ). This neglect made disaster management policies and strategies to be limited to cities as compared to rural areas. Recently, Lilongwe City has experienced numerous flooding with varying impacts of damage in schools, health centres, shops, houses and loss of lives (LCDRMP, 2017). This increased occurrence and devastating impacts calls for putting measures in place to protect people living in flood-prone areas, including flood risk reduction, prevention, mitigation and management. However, strong measures cannot be put without FVA which is a cornerstone for disaster risk reduction (Munyai et al. 2019 ; Nazeer and Bork 2021 ; Nong and Sathyna 2020 ).

FVA provides a significant opportunity towards identifying factors leading to flooding losses (Lidiu et al. 2018; Nazeer and Bork 2021 ; Ndanusa et al. 2022). FVA is an impetus in which science may help to build a resilient society (Ran et al. 2018; Birkmann et al. 2013 ). In addition, FVA provides metrics that can support decision-making processes and policy interventions (Mwale et al. 2015 ; Ndanusa et al. 2014) and is a proactive task for pre-hazard management and planning activities (Parvin et al. 2022 ). Nazir et al. (2013) argue that FVA provides an association between theoretical conceptions of flood vulnerability and daily administrative processes. Mwale ( 2014 ) holds that vulnerability must be quantified and analysed to identify specific dimensions of vulnerability. Birkmann et al. ( 2013 ) add that the need to understand vulnerability is a primary component of disaster risk reduction at the household and community level and culture of building resilience. Iloka ( 2017 ) highlights that measuring vulnerability helps to determine immediate impacts on lives as well as future impacts of the affected households and communities. The Sendai Framework (2015–2030), an international policy for DRR also emphasises vulnerability assessment as a tool for minimizing the impact of hazards (UNISDR 2017 ). The Sendai Framework posits that vulnerability assessment should be conducted to understand risk in all dimensions of vulnerability, capacity, exposure of persons, hazard characteristics and the environment (UNISDR 2017 ). Birkmann et al. ( 2013 ) suggest that a vulnerability assessment is a prerequisite to reducing any natural hazard's impacts. Therefore, this study was aimed at assessing household flood vulnerability in both rural and urban informal settlements in Malawi. This was achieved by: (1) analysing the variability of households' flood vulnerability (based on physical, social, economic, environmental and cultural factors (2) quantifying household vulnerability to floods in Karonga District and Lilongwe City using multicollinearity analysis of vulnerability factors (physical, social, economic, environmental and cultural) and vulnerability components (exposure, susceptibility and resilience) (3) proposing FVA framework for rural and urban informal settlements, including constructing a multi-hazard vulnerability indicators which is missing in most studies. The study contributes to scanty literature on FVA in developing countries such as Malawi. As many areas of Malawi are flood-prone, the study directly informs decision-making for both preparedness and mitigation measures among the vulnerable communities.

2 Materials and methods

2.1 study approach.

This study carried out flood vulnerability assessment (FVA) using an inductive approach (Abass 2018 ; Kissi et al. 2015 ). The use of an inductive approach allows the study to apply quantitative techniques (Fig. 1 ). These techniques helped to isolate variables and indicators that were significant to contribute to household flood vulnerability.

Methodology layout

2.2 Study area

This study was carried out in Karonga District and Lilongwe City in the northern and central regions of Malawi respectively. Specifically, this study was carried out in Mtandire Ward and Traditional Authority Kilupula in Karonga District and Lilongwe City respectively.

The target flood-prone area of T/A Kilupula in KD was the Lufilya catchment (Fig. 2 ). This study targeted two groups of village headmen (GVH) in T/A Kilupula of the northern part of Karonga district. These include GVH Matani Mwakasangila and Mujulu Gweleweta in Traditional Authority Kilupula. The area of GVH Matani Mwakasangila is found in T/A Kilupula located about 16 km north of Karonga town. GVH Matani Mwakasangila has five Village headmen (VH) namely Eliya Mwakasangila, Matani Mwakasangila, Chipamila, Shalisoni Mwakasangila and Fundi Hamisi. The greater part of the area—Eliya Mwakasangila, Chipamila and Matani Mwakasangila, are bounded by Lake Malawi to the eastern side and the M1 road-Songwe-Tanzania border to the Western side. The other two villages Shalisoni Mwakasangila and Fundi Hamisi are to the Western side of the M1 road. The area has numerous networks of rivers such as Lufilya, Kasisi, Fwira, Ntchowo, and Kasoba.

Map T/A Kilupula in Karonga District showing Villages of Study Area

This catchment of T/A Kilupula was selected based on the frequency of flood occurrence (Table 1 ). Kissi et al. ( 2015 ) indicate that the magnitude of an extreme event is inversely related to its frequency of occurrence. It was also chosen because the nature of their locations is prone to flooding (Mwalwimba 2020 , 2024 ; SEP-2013–2018). This makes the residents vulnerable to flood hazards that cause disaster every year.

The area is dominated by floodplains along the shores of Lake Malawi (SEP-2013–2018). These areas are flat and low-lying areas as such this becomes the pre-requisite to flooding in the event of a heavy downpour (Karonga Met Office 2021). Furthermore, the choice of this area was due to settlement patterns, located in flood plains and issues of culture that have forced the people to occupy dangerous areas and even occupy the protected areas rendering them vulnerable to the effects of flooding (Mwalwimba 2020 ) (Fig. 3 ).

Settlement patterns of households in T/A Kilupula of Karonga District

Lilongwe district hosts the capital city of Malawi. The district became the host of the Capital city in 1975 after it was relocated from Zomba. The district owes its name to the Lilongwe River, which flows across the centre of the district (SEP, 2017–2022). The city has grown tremendously since 2005 when the government relocated all the head offices from Blantyre (SEP 2017 ). This growth has been also amplified by the presence of numerous opportunities in the city like access to socio-economic services and availability of markets for the produced products. This growth has contributed in generating a lot of vulnerable conditions of people to hazards such as floods, accidents, fires, droughts, environmental degradation and disease epidemics (LCDRM 2017) because of increased environmental degradation, and increased conversion of agricultural land into urban infrastructural development. Though hazards in the city overlap and interact in cause and effect, floods are the most frequently occurring hazards that affect many parts of the city (SEP 2017 ). As a category related to water and weather, floods, mostly affect areas like Mtandire (area 56), Kauma, Kaliyeka, Chipasula, Kawale, Nankhaka, Area 22, Kauma, New Shire, Area 25, Kawale, and Mgona in the city (LCDRM 2017) (Fig. 4 ).

Map of Malawi showing the Location of Karonga District and Lilongwe City

Mtandire Ward in Lilongwe City (Fig. 5 ) was chosen because it is an informal settlement, a condition that would likely put residents susceptible to environmental hazards like floods. The records indicate that floods repeated in 2013, 2014, 2015, 2016 and 2017. Data indicates that in February 2017, floods caused a magnitude of the disaster which caused great damage; more than 4000 people were affected including loss of people’s lives. The affected areas were Mtandire, Kauma, New Shire, Area 25, Kawale, Nankhaka and Mgona.

Settlement Patterns in Mtandire Ward of Lilongwe City

2.3 Flood vulnerability

Vulnerability is a complex concept and includes diverse components (Rana et al. 2018). Therefore, vulnerability requires a comprehensive methodology which can help to reveal various components (Moreira et al. 2021 ). Rana et al. (2018) stipulate that there is a lack of integrated methodology that fuses all the components. This study used an indicator-based approach to quantitatively assess household flood vulnerability. As accorded by ISDR (2014), the quantitative approach was useful in establishing indicators of the FVA framework. Kablan et al. ( 2017 ), and Nazeer and Bork ( 2021 ) agree that quantitative indicators are used to predict flood vulnerability. However, variation exists in the selection of the quantitative tools (Kissi et al. 2015 ). For instance, Nazeer and Bork ( 2021 ) applied Pearson’s correlation to predict flood vulnerability. Kissi et al. ( 2015 ) used deductive and inductive approaches to select flood vulnerability indicators. This study used binomial multiple logistical regression to predict household flood vulnerability. The use of this method allowed us to agglomerate the indicators of the UVFs and VCs (Fig. 6 ).

Conceptual framework

2.3.1 Conceptual framework on flood vulnerability

This study developed a conceptual framework based on the understanding that a vulnerability occurs as an intersection of biophysical vulnerability and social vulnerability (Iloka 2017 ; Wisner et al. 2004 , Cutter 2003). This entails that the combination of hazard (floods) and vulnerability to harm society depends on the physical risk and social risk.

This conceptual framework indicates that two forces create vulnerability of households/communities to floods. First, households can be vulnerable to floods when subjected to the underlying vulnerability factors (physical, social, economic, environmental and cultural causes). Each of the causes, physical-social-economic-environmental-cultural, have the indicators that are used to identify households’ vulnerability to floods. Depending on variations that exist among these indicators in terms of their scores, percentages, inertias and probability values, households may be determined and/or predicted their vulnerabilities. The second force is determined by vulnerability components (exposure, susceptibility and resilience) (Kissi et al. 2015 ). Households are vulnerable to floods if they are exposed and susceptible to it and have less resilient to withstand its impacts (Rana et al. 2018). In this study, exposure is portrayed as the extent to which an area that is subject to an assessment falls within the geographical range of the hazard event (Nazeer and Bork 2021 ). This implies that exposure looks at possibility of flooding to impact people and their physical objects (Nazeer and Bork 2021 ) in the location they live. Furthermore, susceptibility means the predisposition of elements at risk (social and cultural) to suffering harm resulting from the levels of fragility conditions (Birkmann et al. 2013 ; Kablan et al. 2017 ; Nazeer and Bork 2021 ). Resilience of households is evaluated based on the capacity of people or society potentially exposed to hazards to adapt, by resisting or changing in order to reach and maintain an acceptable level of functioning and structure (Ndanusa et al. 2022). This is determined by the degree to which the social system is capable of organising itself to increase its capacity for learning from past disasters for better future protection and to improve risk reduction measures as well as to recover from the impact of natural hazard (Birkmann et al. 2013 ; Nazeer and Bork 2021 ). Iloka ( 2017 ) states that low incomes, lack of resources, and unemployment are some of the factors that make vulnerability leading to disasters. This study’s conceptual framework highlights the scenario that the occurrence of hazards (floods) in a community (Lilongwe city and Karonga district) where households are subjected to many characteristics in the vulnerability factors while at the same time the households are exposed and are susceptible to floods, the condition may turn floods to become disasters. It is only when the households have enough resilience and adaptive measures that they can either cope up with or respond quickly to the hazard (floods). Similarly, if the households are not resilient and have fewer adaptive measures, a situation that may increase vulnerability of households to the hazard impact resulting in a devastating disaster. Therefore, lack of adaptive capacity means that the community may be limited to respond to the disaster on time thereby their vulnerability will be always high. This conceptual framework gives a basis that flood vulnerability assessment therefore should examine factors that predict household vulnerability to floods and link them to the composite indicators of vulnerability, including understanding their adaptive capacity that would help them to cope with flood impacts. The assessment, using this framework should analyse several indicators from the underlying vulnerability factors and components of vulnerability to fully identify which of these conditions contribute to vulnerability in a specific location to generate standardised indicators of flood vulnerability assessment.

2.3.2 Indicators of flood vulnerability

Flood vulnerability was explored through the lens of underlying vulnerability factors (UVFs)-physical-social-economic-environmental and cultural (Table 2 ). The physical vulnerability (PV) has been defined as the vulnerability of the physically constructed materials. The indicators were defined as pre-underlying factors that may contribute to the constructed elements (houses & other infrastructures) being vulnerable to flood hazards. Social vulnerability (SV) is looked at by the influences of the variety of social processes which create the vulnerability of households to floods (Joakim 2008 ). Economic vulnerability (EcV) is defined as the influences of economic processes existing in the community i.e. livelihood activities that may or may not contribute to household vulnerability. Environmental vulnerability (EnV) is the vulnerability of the built environment as described by pre-existing conditions like residing in prone areas and use of natural resource base. Cultural vulnerability has been defined as vulnerability influenced by cultural fabric such as beliefs, customs, cultural conflicts and absence of resource ownership.

The vulnerability components (VCs)-exposure-susceptibility and resilience (Table 3 ) were combined by UVFs. Physical and environmental factors linked to exposure (i.e. human settlement damage, house type, location, rivers). Social and cultural factors combined with susceptibility (i.e. community accessibility, flood risk awareness, adaptation mechanisms, warning systems) to determine household vulnerability. Economic factors linked with resilience (i.e. a source of income, the capacity of economic skills and resource skills).

Both the UVFs and VCs were selected based on a thorough review of contemporary frameworks such as Pressure and Released Mode (Wisner et al. 2014 ); Urban Flood Vulnerability Framework (Salami et al. 2017 ); and the Hazard of Place Model (Cutter 1996 ). Since there is no generally acceptable way of selecting vulnerability indicators (Kablan et al. 2014; Nazeer and Bork 2021 ), this study considered the indicators based on a cut-off point of probable value zero to one where zero represents the minimum and one indicates maximum values (Kissi et al. 2015 ; Nazeer and Bork 2021 ; Ndanusa et al. 2022). Data on the UVFs and VCs were collected using a quantitative cross-sectional structured survey questionnaire from 200 and 345 household participants in T/A Kilupula of KD and Mtandire Ward of LC respectively. The questionnaire was programmed in KoBocollect and Android tablets were used to capture the data from household participants. Data were also collected for the elements at risk from each underlying vulnerability component to determine the contribution of vulnerability for the households.

The vulnerability component indicators (Table 3 ) were normalised to have a comparable set of indicators, the study adopted the Min–Max normalisation to convert the values to a linear scale (such as 0–1) (Balica et al 2012 ; Erena et al. 2019; Kissi et al. 2015 ; Nazeer and Bork 2021 ; Ndanusa et al. 2022). Vulnerability increases with an increase in exposure and susceptibility, and it decreases with an increase in Resilience (Kissi et al. 2015 ; Mwale 2014 ; Munyani et al. 2019 ; Nazieer 2021). Therefore, normalisation was based on the assumptions that:

(a) Vulnerability (V) increases as the absolute value of the indicator also increases. In this case, where the functional relationship between the indicator and vulnerability is positive, the normalised indicator is derived using the following equation (Oyedele et al. 2022 ).

(b) Vulnerability (V) decreases with an increasing absolute value of the indicator. Here, when the relationship between vulnerability and the indicator is found to be negative, the data are rescaled by applying the equation (Oyedele et al. 2022 ).

where Xi = normalised value; Xa = actual value; XMax = maximum value; XMin = minimum value for an indicator i (1, 2, 3... n) across the selected communities.

Furthermore, no weight was assigned to the indicators of vulnerability components. The reason for not including weights was that most of the responses during the stakeholders’ engagement were contradictory and highly inflicting. Therefore, to avoid an index value that will mislead the end users, the normalised indicator was aggregated into its respective sub-indices for the final flood vulnerability index. The additive arithmetic function was employed in the aggregation of the indicator into its respective sub-indices (exposure, susceptibility, and lack of resilience) using an equation (Kissi et al. 2015 ; Nazeer and Bork 2021 ; Oyedele et al. 2022 ).

The overall flood value of the vulnerability index was computed with Eq. ( 4 ), an additive function (Nazeer and Bork, 2019 ; Lee and Choi 2018; Oyedele et al. 2022 ).

where SIE means sub-indices exposure, Susceptibility (SIS), and lack of resilience (SILoR) for “n” numbers of indicators in each component of vulnerability.

The study measured the level of vulnerability of the elements at risk in all the underlying vulnerability factors (Table 4 ). These were evaluated based on the constructed scale which modified the Balica et al. ( 2012 ) and was calibrated as (0–0.2) very low vulnerability; (0.2–0.49) moderate vulnerability; (0.5–0.59) vulnerability (0.6–0.79) high vulnerability and (0.8–1) very high vulnerability. However, in the actual data collection tool (household questionnaire survey), Mwalwimba ( 2020 ) measurements scale of “not vulnerable”, “slightly vulnerable”, “vulnerable”, “severely vulnerable” and “do not know” were used and later the percentage obtained during univariate analysis were computed and compared to the weighting scale constructed (Balica et al. 2012 ) (3.10). Ndanusa et al. (2022) argued that a breakdown of the elements at risk poses a serious threat to communities' vulnerability and prosperity. This consequently contributes to the higher vulnerability of the community to hazards.

2.4 Study population and sampling determination

The target flood-prone area of TA Kilupula in KD was selected based on the frequency of flood occurrence. Kissi et al. ( 2015 ) indicate that the magnitude of an extreme event is inversely related to its frequency of occurrence. Whilst, Mtandire Ward in Lilongwe City was chosen because it is an informal settlement. Household participants in Mtandire ward were those specifically in two Group Village Headmen, Chibwe and Chimombo of Senior Chief Ligomeka. These villages are located along the Lingadzi River opposite area 49 (New Gulliver). This study used a total of 10 headmen (VH). The choice of the VH was based on proximity to Lingadzi River. Mtandire has a total population of 66,574 people, but 5000 people are reported to be at risk of floods (MDCP 2010–2021; MPHC 2018). Relatedly, the target population in Karonga district were households of GVH Matani Mwakasangila and Mujulu Gweleweta in Traditional Authority (TA) Kilupula. These household villages share a network of water systems such as Lufilya, Mberere, Ntchowo and Fwira (Mwalwimba 2020 ). This study used a total of 10 village headmen (VH), five from each GVH. The choice of five VH in each GVH was based on the fact that each GVH in T/A Kilupula has a minimum number of five Village Headmen (Karonga Chief Classification 2016). T/A Kilupula has a total population of 78,424 people, with approximately 9500 households at risk of floods (KD-SEP 2013-2018; MPHC 2018).

The sample size (n) for this study was calculated using the formula in Fisher et al. ( 2010 ) as shown in the Eq. ( 5 ). The formula in Eq. ( 5 ) returns the minimum sample size required to ensure the reliability of the results.

In Eq. ( 7 ), Z is the confidence level (1.96 for 95%), p is the proportion of the target households, q = is the alternative (1-P) and d is the power of precision (d = 0.05 at 95%). The formula requires knowing the target population (P) and it also assumes “P” to be 0.5 which is conservative. Therefore, the fact that the number of households prone to floods in T/A Kilupula and Mtandire ward is known, using this formula, 384 and 246 households were obtained from Mtandire ward and T/A Kilupula respectively. The study used 0.5 (50%) to represent “P” in Mtandire Ward and 0.2 (20%) to represent “P’ in T/A Kilupula. The reason for differentiating the “P” was that in the Mtandire ward, the whole area was selected while in T/A Kilupula not all the GVHs were selected and involved in the survey. Furthermore, unlike in T/A Kilupula where the population is sparsely distributed and households were selected based on location to flood-prone areas, in Mtandire ward 50% was used as conservative because of high population density such it was possible to interview many households. During data collection, the researcher managed to collect data from 345 and 200 household participants, representing 90% and 81% of the total sampled in Mtandire ward and T/A Kilupula respectively. The reason for not completing the actual sample size was that the household survey interviewed houses along the buffer zones of Lingadzi and Lufilya rivers and the whole area of the buffer was randomly selected. Therefore, continuing to interview every household in the buffered area would have meant interviewing every household. This would have worked against the rule of simple random sampling strategy and survey ethics (Kissi et al. 2015 ).

2.5 Questionnaire design and administration

This study used a structured household questionnaire survey. This questionnaire captured information that provided the linkages of households’ vulnerability factors, exposure, susceptibility and resilience. Associations of vulnerability factors have been supported in the literature (Kissi et al 2015 ; Mwale 2014 ; Nazeer and Bork 2021 ). Nazeer and Bork ( 2021 ) argue that the issue of double counting of the indicators is an important step to be considered in the formation of composite indicators. The household questionnaire survey was coded in KoBoToolBox. The household questionnaire survey was administered face-to-face with household participants who were above 21 years old. The age parameter was controlled in the KoBoToolBox environment such that the interviewers could not proceed with administering the questionnaire if this question was not answered even if the age entered was below 21. It is also important to note that the attributes of the variable age were not coded because it is a continuous variable hence the ages were manually collected from the participants. Finally, the household questionnaire survey was pretested and piloted in Mchesi and Mwanjasi in LC and KD respectively. Before pretesting and piloting, the research assistants (RAs) were trained to have a common local understanding of the terms that were contained in the questionnaire, specifically vulnerability, floods, resilience, susceptibility, adaptive capacity and exposure.

2.6 Data analysis

To determine variations among the indicator variables of UVFs for the predicted factors, a Minitab statistical test called Multiple Correspondence Analysis (MCA) was computed. MCA produced two outputs called “Indicator Analysis Matrix” and “Column Contribution table”. The column of contribution is used to determine the variations that exist between indicators (Husson 2014). On the other hand, the total inertia in the Analysis of Indicator Matrix (AIM) was averaged for all the five UVFs in LC and KD to obtain a single inertia which was used to determine a multi-correspondence variations of vulnerability factors (MIHVF).The indicators in the assessment that contributed to flood vulnerability were marked with red ink in the measurement scale of important (INT) and very important (VINT). The significance levels between demographics and vulnerability factors were analysed using the single chi-square test and a combined value analysis package. Also, chi-square tests and probability value ( p value) were used to compute significance levels of variables in UVFs and VCs. The formula for chi-square statistics is:

In addition, it follows a with (r−1) (c−1) degrees of freedom. Where

O ij is the observed counts in cell ij; i = 1, 2, 3…..r and j = 1, 2, 3…..c where r is the number of rows and c is the number of columns in an r × c contingency table.

E ij the expected counts in cell ij; i = one, 2, 3…..r and j = 1, 2, 3…..c where r is the number of rows and c is the number of columns in an r × c contingency table.

Those that were significant were computed in the modified binomial multiple logistical regression model using equations. All these were performed in “r” and STATA version 12.

A post-analysis of computed results was carried out using an artificial neural network (ANN). ANN is a machine learning method that stands more independent in comparison than statistical methods (Ludin et al. 2018; Parvin et al. 2022 ). Several studies have used ANN to predict specific events (Mwale 2014 ). Due to its predictive ability, this method was applied in this study as a post-analysis to predict the causes of flood vulnerability of the variables which were statistically tested using a combined P value package between UVFs and VCs. ANN comprises several nodes and interconnected programming elements (Mwale 2014 ; Parvin, et al. 2022 ). It contains input layers, hidden layers and output layers (Ahmadi 2015 ) (Fig. 7 ).

Example of ANN using MLP

The multivariate level used the multiple binomial logistical regression model (Eq. 6 ) (Israel 2013) to predict household flood vulnerability. It utilised a paired comparison model (Hamidi et al. 2020; Chen et al. 2013), in which each UVF was linked with a selected vulnerability component (exposure, susceptibility and resilience). This link is accorded in the studies of Wallen et al. (2014) and Mwale ( 2014 ). This model generated significant levels of physical exposure, social-susceptibility, eco-resilience, enviro-exposure and cultural-susceptibility. Then, the Flood Vulnerability Index (FVI) was applied to determine which factor contributes to vulnerability (Balica et al. 2012 ; Kissi et al. 2015 ). The FVI uses a probability range of 0–1 (Balica et al. 2012 ) where 0 means not vulnerable and 1 means more vulnerable. Using Eq. 1 , the paired attributes were run in r environment through the modified binomial logit multiple regression (Eq. 6 ). However, it would have been significant to use logit-ordered regression since the vulnerability has a certain order (Kissi et al. 2015 ; Hamidi et al., 2020).

where ${y}_{j}$ is a response variable (i.e., as selected from exposure, susceptibility and resilience) ${\beta }_{i}$ is intercepted (values generated by the equation after extraction in r- environment, ${\delta }_{i}$ is predictor variable (selected from physical, social, economic, environmental and cultural), ${O}_{i}$ operator (i.e., measurement scale, less important and very important which considered by the model), ${\epsilon }_{j}$ is an error. This equation was applicable for all the $UVFs,$ thus parameters in the $UVFs$ were predicted separately based on the $VCs$ to which they were associated. The link of UVFs and VCs in the regression model was computed in an implicit relationship showing the predictor and response variables (Table 5 ).

The binomial logit regression model was used based on three assumptions which implied that:

The indicators for UVFs should be measured as a proportional value of household participants involved during the survey. The percentage values should be generated using a scale range with the operator of “ less important ”; “ important ” and “ very important ” to contribute to flood vulnerability”. However, for flood vulnerability determination, a cut-off point should be placed at greater or equal to 50% for each indicator from the operator of the scale range of “important” and “very important”. In this case, all the values generated in the scale of “less important” as responded by the participants should be left out during determination and selection.

The linkage of UVFs and VCs should be based on statistical tests using P-values or correlation (r) or simply any statistical test applicable to the researcher. The values that are significant at a certain confidence level (i.e. 0.05 in this study) should be selected to be included in the framework for specific combinations like Physical Exposure Factors (PEFs), Socio-Susceptibility Factors (SSFs), Eco-Resilience Factors (ERFs), Enviro-Exposure Factors (EEFs) and Cultural-Susceptibility Factors (CSFs). Furthermore, those values significant at an appropriate confidence level should be considered as factors generating flood vulnerability in the studied areas.

Multicollinearity of the UVF and VC variables should be checked using variance independent factor (VIF) to assess the level of correlation in the regression model. It is assumed that a variable with VIF ≥ 10 has higher variance inflation in influencing other response variance and is redundant with other variables. As such, that variable should be dropped. In this study, the VIF process was done in SPSS.

Flood vulnerability index (FVI) was used in the determination of household flood vulnerability based on the output of the analysis of the results. A summarized comparison flood vulnerability index (FVI) probability scale 0 to 1 (Balica et al. 2012 ) has been presented in Table 6 .

Results were presented on spatial distribution maps, computed in ArcGIS 10.8 Desktop. Shapefiles for Malawi administrative boundaries were downloaded from MASDAP (Malawi Spatial Data Application Portal). Then Excel was used to generate the tabulated information and pie charts and later exported the output to ArcMap. The Maps were coloured to show the contribution of each variable to households' flood vulnerability (Fig. 8 ).

Vulnerability levels

3 Results and discussions

3.1 variability of underlying vulnerability factors.

The results of Multiple Correspondence Analysis (MCA) output have been outlined in Tables 7 , 8 , 9 , 10 and 11 , with those with higher quality value (Qual.), inertia, correlation (Corr.) and contribution (Contr.) marked with red ink to depict variation in flood vulnerability.

The results in Table 7 show all the physical variables marked by red ink have larger quality values in Mtandire Ward of LC. However, the results in T/A Kilupula of KD show the greater quality value in the scale of “VINT” for indicator values of poor construction standards for houses (0.551) and lack of construction materials (0.708). Furthermore, the results also indicate a higher correlation (corr.) for poor construction standards for houses in the scale value of “INT” and ‘VINT, accounting for a higher amount of inertia in both rural and urban areas. Construction of roads and other infrastructures (0.234) account for a high contribution to the inertia in Mtandire Ward of LC while poor construction of housing standards account for a higher inertia value (0.201) in both Mtandire Ward of LC and (0.313) and in T/A Kilupula of KD (Table 7 ). The results further established that physical elements at risk on the scale of “severe vulnerable” have the vulnerability thresholds of 0.5 and 0.6 in Mtandire ward and T/A Kilupula respectively.

The results of MCA show a significant contribution of vulnerability with a quality value in the category of social security on the scale of INT (0.506) and VINT (0.500). The results further show a significant contribution of vulnerability in the category of inavailability of health services (0.513) in the scale of INT in LC. In T/A Kilupula of KD, the results show significant quality values on lack of capacity to cope (0.821) in the scale of INT, social security and human rights in the scale of INT and VINT (Table 7 ). While the results of the inert values in Mtandire Ward of LC do not deviate much from the expected, in T/A Kilupula of KD the inert value of lack of capacity to cope (0.124) in scale of INT and social security (0.117) in a scale of VINT deviate from the expected value. The results also indicate a higher correlation (corr.) social security (0.504) and human rights (0.648) and unavailability of health services (0.506) in Mtandire Ward of LC while lack of capacity to cope (0.790) and social security (0.560) have higher Corr in T/A Kilupula of KD accounting higher amount of inertia to contribute to vulnerability. The results further show all the indicator variables in the scale of “INT) contribute higher to the inertia in Mtandire Ward of LC while only lack of capacity to cope (0.2613) and social security (0.2141) contribute higher to the same in T/A Kilupula of KD (Table 8 ).

The results in Table 9 show that lack of markets (0.574) and poverty (0.513) in the scale of “INT” have higher quality value in Mtandire Ward of LC while lack of credit unions and lack of markets showed higher quality value in T/A Kilupula of KD. These results suggest that lack of markets, poverty and lack of credit unions contribute more to household vulnerability to floods than lack of alternative livelihoods. The results further show that all the indicator variables in Mtandire Ward of LC have an inertia value at the expected rate of less than 10% while in T/A Kilupula of KD lack of credit unions (0.103), lack of markets (0.499) and poverty (0.123) display values that deviate from the expected. Similarly, the results show a weak correlation (less than 1) for all the economic indicator variables in Mtandire Ward of LC and only lack of markets (0.499) is close to 1 in T/A Kilupula of KD thereby contributing highly to the inertia. The lack of credit unions and lack of markets account for a high contribution to the inertia, thereby suggesting a high contribution to flood vulnerability. The results also found that the economic elements at risk have a higher vulnerability value in T/A Kilupula (0.55) compared to Mtandire ward (0.33) on the scale of severe vulnerable.

The results in Table 10 show that except for poor land management in T/A Kilupula of KD for scales of INT and VINT, environmental mismanagement and inappropriate use of resources have larger quality values in Mtandire Ward of LC and T/A Kilupula of KD. No indicator variable depicted the unexpected inertia value in Mtandire Ward of LC and T/A Kilupula of KD. In LC, the results further revealed that the correlation is higher for environmental mismanagement (0.524) in the scale of INT, poor land management is also higher in both scales and inappropriate use of resources (0.518) in the scale of INT. However, extensive paving (0.674), environmental mismanagement (0.557) and poor land management (0.677) have higher correlation values close to one. Environmental mismanagement (0.169), poor land management (0.202; 0.104) and inappropriate use of resources (0.152; 0.105) account for high contribution to the inertia in Mtandire Ward of LC while extensive paving (0.1721) and environmental mismanagement (0.137; 0.101) account for higher contributions in T/A Kilupula of KD (Table 10 ). It was also found that environmental elements at risk are more vulnerable in T/A Kilupula of Karonga on a scale of “slightly vulnerable” (Fig. 4.39) compared to the Mtandire ward of Lilongwe City.

The results in Mtandire Ward of LC showed that lack of safety measures (0.551) and lack of personal responsibility (0.632) have high-quality values above the cut-off of 50% while in T/A Kilupula of KD traditional beliefs (0.508), settlements conditions (0.579), lack of safety measures (0.596) and lack of personal responsibility (0.636) have high-quality values. No indicator variable depicted the unexpected inertia value in Mtandire Ward of LC and T/A Kilupula of KD. The results further revealed no strong correlation (close to 1) in Mtandire Ward of LC to contribute to inertial variability. Nevertheless, in T/A Kilupula of KD, the results showed a strong correlation between traditional beliefs (0.506) and poor settlement conditions (0.576). This suggests people living in Mtandire Ward are not aware that they live informally. It was noted that Mtandire Ward is not properly defined as it is part of the Lilongwe City or Lilongwe District. While results show no higher value for contribution (Contr) in Mtandire Ward of LC, traditional beliefs (0.187), settlement conditions (0.199) and language of communication (0.1526) account for high contribution to the inertia in KD (Table 11 ).

Cumulatively, the results of the MCA for all indicators in the category of quality value $\ge$ 0.50 (50%) revealed an average of “high vulnerability” (0.62) in T/A Kilupula of KD and “moderately vulnerability” (0.52) in Mtandire Ward of LC. Based on individual factors, the results found high physical vulnerability in both T/A Kilupula (0.61) and Mtandire Ward (0.65), high social vulnerability in T/A Kilupula (0.68) compared to moderate social vulnerability in Mtandire Ward (0.58), high economic vulnerability in T/A Kilupula (0.60) compared to moderate economic vulnerability in Mtandire Ward (0.51), high environmental vulnerability in both T/A Kilupula (0.67) and Mtandire Ward (0.68) and moderate cultural vulnerability in T/A Kilupula (0.54) compared to very low cultural vulnerability in Mtandire Ward (0.16).

3.1.1 Artificial neural network: multi-layer Perceptron (MLP)

The results of the ANN in multi-layer perceptron (MLP) to show the relationship between the indicators used in the UVFs and those in the VCs are presented in Tables 12 , 13 , 14 , 15 and 16 .

The results of exposure linked with physical factors reveal that there is a strong relationship between house type with PCS in T/A Kilupula of KD, while in Mtandire Ward of LC the relationship is not very strong (−9.116) (Table 12 ). The relationships of house type with CRFs imply that these contribute to household flood vulnerability. Lack of construction materials (PCMs) has a strong network value in T/A Kilupula of KD compared to Mtandire Ward of LC with a negative value (Table 12 ). The results reveal that houses made up of bamboo followed by those made up of mudstone are strongly associated with PCS in T/A Kilupula of KD. The results further show that houses made up of unburnt bricks are strongly associated with poor settlement conditions in Mtandire Ward of LC. Lack of construction materials has a strong relationship in T/A Kilupula of KD than in Mtandire Ward of LC. Similarly, CRF and AI have a strong relationship with house material type in Mtandire Ward of LC thereby contributing to high household flood vulnerability in LC.

In Table 13 , the results revealed that sex is significant with social vulnerability factors (0.0539), physical vulnerability factors (0.0371), economic vulnerability factors (0.0562) and cultural vulnerability factors (0.0594) in KD while only environmental factors are significant with sex (0.0331) in LC. The result further revealed that marital status is significant with physical vulnerability factors in T/A Kilupula of KD (0.0265), environmental factors (0.0383) and economic factors (0.0497) in Mtandire ward of LC while in T/A Kilupula (0.0526) with cultural factors (Table 13 ). In terms of education, the results established that social factors (0.001), environmental factors (0.0064) and economic factors (0.0235) are significant to education in Mtandire ward of LC while economic factors (0.0378) are significant in T/A Kilupula of KD (Table 13 ). Finally, the results show that cultural factors (0.0075) and economic factors (0.0106) are significant to occupation in T/A Kilupula and Mtandire ward respectively (Table 13 ).

The results show positive and negative outcome of LOC in T/A Kilupula of KD and Mtandire Ward of LC respectively (Table 14 ). These results point to the fact that lack of capacity to cope contributes to household vulnerability in T/A Kilupula of KD than in Mtandire Ward of LC. The results further show that LAL and LS have positive values both in Mtandire Ward of LC and T/A Kilupula of KD, but with greater contribution to household flood vulnerability in Mtandire Ward of LC. Finally, the results reveal that AHS has positive and negative value in T/A Kilupula of KD and Mtandire Ward of LC. This result indicates that AHS contribute to household flood vulnerability in T/A Kilupula of KD compared to Mtandire Ward of LC (Table 14 ).

The results of ANN revealed that all the UVFs for economic factors have positive values in Mtandire Ward of LC and T/A Kilupula of KD, but with higher values in Mtandire Ward of LC. Lack of income generating activities was revealed to be higher both in Mtandire Ward of LC and T/A Kilupula of KD. These results imply that the NCU, LAL, PO and LGA contribute to household flood vulnerability in Mtandire Ward of LC and T/A Kilupula of KD (Table 15 ).

The results of geography linked with environmental factors reveal that there is strong relationship between them, all with a value greater than “0” in Mtandire Ward of LC compared to T/A Kilupula of KD (Table 15 ). The results show that poor land management (PLM) has strong network value (9.554) in Mtandire Ward of LC and (0.951) in T/A Kilupula of KD, followed by RPA in Mtandire Ward of LC (3.839). These results point to the fact that the CL, RPA, EMS, PLM and IUR contribute to households flood vulnerability in LC and KD, with higher contribution in Mtandire Ward of LC (Table 16 ).

The results of communication linked with cultural factors revealed a strong relationship between in the sets of the combined indicators, all with value greater than “0” in Mtandire Ward of LC compared to T/A Kilupula of KD (Table 16 ). The results show that traditional beliefs (TB) have strong network value (79.789) in T/A Kilupula of KD compared to a network value of 7.872 in Mtandire Ward of LC followed by cultural conflicts with value of 11.864 in T/A Kilupula of KD compared to a value of 6.426 in Mtandire Ward of LC (Table 17 ).

3.1.2 Relationships between vulnerability factors and components

This section combined underlying vulnerability factors (UVFs) and vulnerability components (VCs) to determine indicators that integrate the two parameters to determine households’ vulnerability. The analysis was carried through bivariate statistical test after normalisation of indicators of UVFs and VCs (Table 18 ). The results between physical factors and exposure variables reveals significant relationships between proximity to rivers and settlements (0.0380) in KD, house type (0.0001) in LC and roofing material (0.0072) in Lilongwe and (0.0364) in KD.. The results reveal that all the susceptibility factors are significant to social factors. This result indicates that the susceptibility variables contribute to generate households’ vulnerability to floods in Mtandire ward of LC and T/A Kilupula of KD. The results show that communication accessibility, access to healthcare, access to water, and sanitation contribute to vulnerability to floods in LC and KD are all significant at P-value 0.05 in both Mtandire Ward and T/A Kilupula (Table 16 ). The results reveal that all the resilience variables are significant to economic factors in KD while only income of household head is significant in LC. This result indicates the resilience variables contribute to generate households’ economic vulnerability to floods in T/A Kilupula district than in Mtandire Ward (Table 18 ). The results reveal that some exposure variables combined with environmental variables contribute to household’s flood vulnerability. While geography contributes to very high vulnerability of households to floods in T/A Kilupula of KD (0. 0084), the same is not the case in Mtandire Ward of LC (0.864). House type contributes to very high vulnerability of households to floods in Mtandire Ward of LC compared to T/A Kilupula in KD while roofing material contributes to generate vulnerability in both Mtandire Ward of LC and T/A Kilupula of KD (Table 17 ). The combined results of susceptibility variables with human/cultural factors reveal that communication accessibility contributes to flood vulnerability in Mtandire Ward of LC (0.0002) and not in T/A Kilupula of KD (0.5136). The results further indicate that limited education facilities as well as health facilities contribute to vulnerability in T/A Kilupula of KD and not in Mtandire Ward of LC at p-value 0.05 (Table 18 ).

3.2 Quantification and prediction of household vulnerability

The binomial Logit Multiple Regression was computed in r to generate five scores outlined in the Eqs. 12 to 15 .

3.2.1 Computation of socio-susceptibility score

The underlying social vulnerability factors (SVFs) linked with communication accessibility (ca) in the susceptibility indicators generated the output of socio-susceptibility score (Eq. 12 ).

where S = Susceptibility, ca = communication accessibility, HR = human rights, HS = health services sint = scale of less important, svint = scale of very important.

The above output (Eq. 12 ) linked the susceptibility indicators (communication accessibility) with social variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq. 12 ).

3.2.2 Computation of physio-exposure score

The underlying physical vulnerability factors (PVFs) linked with housing material types (hmt) in the exposure indicators generated the output of physio-exposure score (Eq. 13 ).

where E = Exposure, hmt = housing material type, PC = Poor construction, CM = Construction materials, CR = Construction of roads, sint = scale of less important, svint = scale of very important.

The output (Eq. 13 ) linked the exposure indicators (housing material type) with physical variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq. 13 ).

3.2.3 Computation of eco-resilience score

The underlying economic vulnerability factors (EVFs) linked with income of household head (ihh) in the resilience indicators generated the output of eco-resilience score (Eq. 14 ).

where R = Resilience, ihh = income of household head, PV = Poverty, AL = Alternative livelihoods, sint = scale of less important, svint = scale of very important.

The output (Eq. 14 ) linked the resilience indicators (income of household head) with economic variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq. 14 ).

3.2.4 Computation of enviro-exposure score

The underlying environmental vulnerability factors (EVFs) linked with geography (ge) in the exposure indicators generated the output of enviro-exposure score (Eq. 15 ).

where E = Exposure, Ge = Geography, CL = Cultivated land, EM = Environmental mismanagement, PLM = Poor land management, AUR = Inappropriate use of resources, sint = scale of less important, svint = scale of very important.

The output (Eq. 15 ) linked the exposure indicators (geography) with environmental variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq. 15 ).

3.2.5 Computation of cultural-susceptibility score

The underlying cultural vulnerability factors (CVFs) linked with inaccessibility of communication (ic) in the susceptibility indicators generated the output of cultural-susceptibility score (Eq. 15 ).

where S = Susceptibility, cb = cultural behaviour, LN = local norms, sint = scale of less important, svint = scale of very important.

The output (Eq. 16 ) linked the susceptibility indicators (cultural behaviour) with cultural variables. Therefore, to compute the scores in Lilongwe City (Mtandire Ward) and Karonga District (T/A Kilupula), the percentage values generated using descriptive statistics from the scale of “important” and “very important” were separately inputted in the equation (Eq. 16 ).

The score measure of UVF (physical, social, economic, environmental and cultural) against VCs (exposure, susceptibility and resilience) generated a single value according to the association which was as follows: Physical with exposure factors (PEFs), Social with susceptibility factors (SSFs), economic with resilience factors (ERFs), environmental with exposure factors (EEFs) and cultural with susceptibility factors (CSFs). This association further generated value that was divided by the total sample size 345 and 200 household participants in Lilongwe City and Karonga District and multiplied by the 100 percent to obtain a percentage value of each category in the calibrated formula, for example:

Then the percentage result obtained in equation (Eq. 17 ) for each factor was further divided by 100% to generate the vulnerability level (extent of vulnerability) of each factor (i.e., V L PEFs). This computed arbitrary value was compared to the FVI to predict the extent of vulnerability per factor, for example:

where V L PEFs means the extent (level) of vulnerability to Physio-Exposure factors. This formula was applied to all the combined categories (i.e., SSFs, ERFs, EEFs and CSFs) by substituting the category that was required to be worked out in the equation to obtain the value that was used to determine vulnerability. Finally, the result was used to predict vulnerability in terms of “high vulnerability” and “very high vulnerability” per the FVI scale range. Ordinal categories for the indicators of vulnerability determinants (less important, important and very important) and indicators of elements at risk (not vulnerable, small vulnerable, vulnerable, highly vulnerable and very highly vulnerable) were used for selection of variables.

Finally, the relationship (using Eq. 18 ) generated results in the category of the physio-exposure factors (PEFs), social susceptibility factors (SSFs), eco-resilience factors (ERFs), enviro-exposure factors (EEFs) and cultural-susceptibility factors (CSFs) (Fig. 8 ).

The results of PEFs fall in scale range of “vulnerability” in Mtandire Ward of Lilongwe City (0.52) compared to “high vulnerability” in T/A Kilupula of Karonga District (0.64). This means that while it contributes to vulnerability in both areas, it is much higher in T/A Kilupula of KD compared to the Mtandire ward of LC. The results of the digitized flood maps overlayed with surveyed households’ showed that most houses that are highly vulnerable to floods are between a distance of 0.06–0.12 km to Lingadzi river in Mtandire ward of LC and 0.198–0.317 km along the buffer zones of Lufilya river in T/A Kilupula of KD (Figs. 9 and 10 ).

Map of Mtandire showing households/buildings about Wetlands and drainage systems

Map of T/A Kilupula showing households/buildings about Wetlands and drainage systems

4 Discussion

Though variations exist in the causes of vulnerability, the results of this study have demonstrated that the vulnerability of households to floods in rural and urban informal settlements is very high based on a lack of building materials, proximity to catchments, and limited communication among other factors. Similar, to this finding Alam et al., ( 2022 ) also found a high vulnerability value of 0.7015 for rural people living in the Dammar Char in Southeastern Bangladesh compared to urban areas. While, Alam et al. ( 2022 ), did not specify the causes of such high vulnerability, this study attributes the high vulnerability to the aspect of lack of construction materials, distance to markets and transport cost that people have to incur to access construction materials in rural areas. These causes agree with the findings of Qasim et al. ( 2016 ) in which vulnerability to flooding was attributed to poor/lack of materials used to construct houses. The results also revealed that poor construction of infrastructural facilities falls in the scale of “high flood vulnerability in both LC and KD. This implies that substandard construction of infrastructure such as houses contributes to vulnerability. This finding is supported by literature that substandard infrastructures contribute to flood vulnerability (Salami et al. 2017 ). Furthermore, the ANN results in MLP revealed a strong association of physical vulnerability factors (lack of construction materials, construction of infrastructures, and ageing infrastructures) with housing type. This implies that they contribute to generating vulnerability because people live in substandard houses. This finding confirms the result finding of Movahad et al. (2020) and Aliyu Baba Nabegu (2018) who indicated that people are vulnerable to floods because they usually live in substandard housing conditions which become prone to floods.

The SFFs generated a vulnerability value (0.61) for people living in T/A Kilupula in Karonga District compared to a low vulnerability value (0.2) for people living in Mtandire Ward in Lilongwe City. The above findings indicate that key factors for households’ flood vulnerability are associated with knowledge of building codes and standards. This means that the culture of shelter safety is lacking and that there is a lack of knowledge of the type of houses that they can build to resist floods and any other type of natural hazards. These could be attributed to dynamic pressures influencing households’ vulnerability to floods. That’s to say, people do have enough resources, decision-making, and societal skills to access housing materials that can help them build strong houses. In this situation, the programming of flood risk management and in general DRM mitigation, preparedness and recovery measures should focus on reducing the pressures by strengthening households’ knowledge and building standards. This can be achieved through designing mitigation measures that address the root causes that contribute to increased vulnerabilities in the pre-flood and post-flood phases rather than focusing too much on the trans-flooding phase. In terms of social-susceptibility vulnerability, the results found that the SSFs that contribute to generating vulnerability both in T/A Kilupula of KD and Mtandire Ward of LC are lack of access to health services, human rights, limited institutional capacities and lack of awareness. However, the binomial logistical regression of the SFFs generated a vulnerability value (0.61) for people living in the studied area of KD compared to a low vulnerability value (0.2) of people living in the studied area of LC. This finding differs from the findings of Munyai et al. ( 2019 ) in Muungamunwe Village in South Africa, which found that the value of FVI social was 0.80 higher than all the factors assessed. However, it is noted that the later study did not comprehensively link various factors between UVFs and VCs to determine the degree of contribution to vulnerability. The results further imply that the socio-susceptibility factors contribute to higher vulnerability in rural areas than in urban areas. This finding is supported by the study of Mwale ( 2014 ) in which social susceptibility was categorised from “high to very high vulnerability” among the communities in rural Lowershire of Chikwawa and Nsanje Districts of Malawi.

The ERFs contribute to “very high vulnerability” in Karonga (0.8) and “high vulnerability” in Mtandire Ward of Lilongwe City (0.6). The high vulnerability is linked to factors such as poverty, lack of alternative livelihoods, and lack of income-generating activities. Similar to these results, the study of Mwale ( 2014 ) also established a predominantly very high economic susceptibility based on causes such as a lack of economic resources, an undiversified economy and a lack of employment opportunities among communities in the lower Shire Valley of Malawi. Despite the results revealing the same outcome, the earlier study linked economics with susceptibility measures while this study agglomerated economics with resilience measures. The existing variation placed some causes in different association order. For example, poverty in the study of Mwale ( 2014 ) was categorised as a social susceptibility indicator, while in this study it was used as the eco-resilience measure. The understanding of this study is that poverty is a measure of the income level of a household. That is to say, a household with enough income will be less poor thereby becoming more resilient and vice versa. Therefore, poverty was classified as a cause of “high vulnerability” both in Mtandire Ward of LC with a value of 0.73 and T/A Kilupula of KD with a value of 0.68. On the other hand, the lack of alternative livelihoods contributes to “vulnerability” in Mtandire Ward with a value of 0.54 while ‘high vulnerability” in T/A Kilupula with a value of 0.71). These findings point out the notion that programming current and future flood disaster mitigation plans and vulnerability reduction measures requires the formulation of relevant financial and economic measures which may contribute to poverty alleviation in the community and society.

The EEFs revealed “very high vulnerability” in both Mtandire Ward of LC (0.8) and T/A Kilupula of KD (0.9). The EEFs revealed “very high vulnerability” of EEFs (0.8) in Mtandire Ward and (0.9) in T/A Kilupula. Except for the pressure on cultivated land in Mtandire Ward, all underlying environmental vulnerability factors (UEVFs) contribute to vulnerability in both rural and urban areas. This result points out that pressure on land is an environmental indicator that predicts households’ vulnerability to floods in rural areas (T/A Kilupula) and not in urban areas (Mtandire Ward). The high vulnerability depicted by the EEFs is a total indication that households are more vulnerable due to the built environment. This could be attributed to the fact that people have allowed development in areas where danger exists due to the lack of policy and legal systems to help and guide government and enterprises in disaster risk management. This argument is supported by literature that development in dangerous areas increases peoples’ exposure to danger (Birkmann et al. 2013 ; Nazeer and Bork 2021 ). Barbier et al. (2012) support that environmental damage affects the well-being of the local people since it leads to soil degradation which eventually causes low food production. To this end, laws and policies to regulate development and habitation in risk areas should be seamlessly programmed into the current and future flood mitigation and preparedness plans at all levels.

Finally, the CSFs revealed a low vulnerability in both Mtandire Ward of LC (0.34) and T/A Kilupula of KD (0.39) (Fig. 6 ). In the FVI scale, the SSFs and CSFs contribute to low vulnerability in Mtandire Ward of LC while only the CSFs contribute to low vulnerability in T/A Kilupula of KD (Fig. 3 ). The CSFs show a value of 0.34 in Mtandire Ward and 0.39 in T/A Kilupula, indicating that it contributes to low vulnerability in both areas. However, it was established that household flood vulnerability in T/A Kilupula is high due to other factors such as cultural beliefs of conserving their ancestors’ graveyards and land ownership issues . In support of this result, Iloka ( 2017 ) found that a system of beliefs regarding hazards and disasters contributes to vulnerability. The findings of the author further established that cultural issues do not assist households to be resilient to floods. In Mtandire Ward of LC, it was observed that land use and human occupancy in risk areas contribute to household flood vulnerability. Furthermore, it was reported that rich people have occupied places which are not habitable thereby changing the course of the Lingadzi River. Further to this, youths have resorted to destroying the banks of the river due to a lack of economic activities and high unemployment. It was noted that people do not fear or abide by city regulations because there is no punishment that they receive from city councils.

4.1 FVA indicators for rural and urban informal settlements

Based on the results, and to provide proper flood mitigation and programming of current and future challenges in flood management, this study constructed the FVA framework as a combination of variables from the UVFs and VCs (Fig. 11 ). On the one hand, the physio-exposure indicators (PEIs) relate to the housing and infrastructure in the physical vulnerability factors (PVFs). These should be evaluated based on exposure with its operator house material and type to understand how they contribute to vulnerability (Eq. 13 ). In Fig. 11 , those that intersect (housing typology (HT), poor construction of standards (PCS), lack of building materials (LBM) and loss of physical assets (LPA) and infrastructural standards) are the PEIs for both rural and urban areas. While location (LC) and growth of informal settlement (GIS) are PEIs for rural and urban areas respectively. On the other hand, the enviro-exposure indicators (EEIs) relate to environmental causes such as land use planning and management. These were quantified based on exposure variables, specifically location (Eq. 15 ). In the Fig. 11 , environmental mismanagement (EM), proximity to rivers (PR), poor land management (PLM), inappropriate use of resources (IUR) and siltation of rivers (SR), river catchment morphology (RCM) flooding risk location (FRL) intersect, implying that they are the EEIs for both rural and urban informal areas. Those outside the intersection apply specifically as EEIs conforming either in Lilongwe including waste management (WM), land use planning (LUP) or in Karonga, cultivated land (CL) and topography (TP).

FVA framework

This study derived the physio-exposure indicators (PEIs) and enviro-exposure indicators (EEIs) by agglomerating them with the exposure factors (housing material and geography respectively). This demonstrates the notion that flood risk is a product of exposure to the hazard (flood) and vulnerability. Literature reveals that exposure entails the probability of flooding affecting physical objects-buildings and people (Mwalwimba 2024 ; Balica et al. 2012 ; Nazeer and Bork 2021 ) due to location. Since location is an exposure variable, defined by the geographical position to which the assessment was done (Nazeer et al. 2022), this study relates the physical causes to that location/geography to predict household vulnerability and thereby all the significant indicators were grouped as physio-exposure factors (PEFs) to give rise to the PEI. Also, significant indicators were grouped as enviro-exposure factors (EEFs) and referred to as the EEIs in Fig. 11 . The PEIs and EEIs correlate with the indicators propagated in the hazard of place model (Joakim 2008 ), which relates the vulnerability determinates to biophysical vulnerability i.e. geography, location and proximity.

The amount of social risk experienced by the household was understood by agglomerating socio-susceptibility indicators (SSIs). The SSIs relate to the linkage of social causes with access to communication as a susceptibility variable (Eq. 12 ). Susceptibility deals with elements that influence an individual or household to respond to the hazard itself. In Fig. 11 , the SSIs, lack of access to health services (LHS), communication accessibility (CA), access to training and advocacy (ATA) and level of sanitation (LS) are indicators that intersect, implying they apply to both rural and urban informal areas. However, lack of human rights (LHR) and level of waste management and drainage systems (LWDS) are SSIs in rural and urban respectively. Relatedly, cultural-susceptibility indicators (CSIs) link cultural causes with access to communication in the susceptibility category. Susceptibility deals with elements that influence an individual or household to respond to the hazard itself. In Fig. 11 , lack of personal responsibility, lack of adherence to regulations, lack of institutional support and flood perception are indicators that intersect, implying that they are the CSIs for both rural and urban areas. However, cultural beliefs and myths about floods should be indicators to be evaluated specifically in rural areas, while power conflicts, limited DRR strategies and lack of cooperation should be used to assess vulnerability in urban areas, though they can apply to rural areas too. So, access to communication is a susceptibility condition which may result in making households vulnerable to floods because they cannot anticipate the impending flooding. Hence this study related social and cultural causes with access to communication to develop a combination of socio-susceptibility factors (SSFs) and cultural-susceptibility factors (CSFs). Qasim et al. ( 2016 ) stated that certain beliefs and poverty play a role in the lack of resilience among communities. Birkmann et al. ( 2013 ) and Kablan et al. ( 2017 ) stated that susceptibility relates to the predisposition of the elements at risk in social and ecological spheres. Hence, most of the susceptibility factors relate to social and cultural causes because they are all an integral part of humanity's suffering if conditions do not support them to withstand and resist the natural hazard impacts.

The eco-resilience indicators (ERIs) should put much emphasis on economic causes of vulnerability. Economic indicators such as limited access to alternative livelihoods and poverty contribute to generating vulnerability. These indicators may or may not be affected by the resilience of households to the shock. As such, resilience is measured based on the ability of the households to cope with the event. As such, key factors to measure resilience include access to resources, improved livelihoods and access to income among others. The framework therefore strongly overlaps economic causes with resilience factors to assess the vulnerability of households to floods. In Fig. 11 , poverty (PO), limited livelihoods (LVs), lack of income of household head (LIHH), and loss of economic assets (LEA) are indicators that intersect, implying that they are eco-resilience indicators (ERIs) that can be used for vulnerability assessment in both rural and urban areas. The ERIs for only rural lack of markets (LM), limited credit unions (LCU) and reduction in agricultural land (RAL) while in urban informal settlements, they include lack of employment opportunities (LEO). Birkmann et al. ( 2013 ) stipulated that resilience comprises pre-event risk reduction, time-coping, and post-event response actions. Therefore, this study relates the economic causes of resilience to give rise to the eco-resilience indicators (ERIs) (Fig. 11 ).

The adaptive capacity provides key adaptive measures that can be incorporated to deal with vulnerability conditions generated from each intersected category. The adaptive measures relating to housing strategies can be utilised to minimised flood impact on households under the physio-exposure factors in the category of the PEIs are strengthening the availability of building materials (SULBM), enforcement of building codes and standards (EBCS) and empowering locals on flood resilient structures (ELFRS). Similarly, social organisational measures can be utilised to minimise socio-susceptibility factors relating to SSIs. The adaptive capacities that can contribute to reducing vulnerability in the category are the ability to make decisions (AMD), the ability to organise and coordinate (AOC) and communal strategic grains for resilient buildings (CSGRB). In addition, the economic measures can be utilised to minimise flood impacts relating to eco-resilience factors for the category of ERIs and they include saving agricultural produce (SAP), strengthening diversification (SD) strengthening livelihoods opportunities (SLO) can be used as adaptive capacity under this category. In terms of exposure, households to adapt to flood impact can use land management measures. These practices include: elevating house location (EHL), afforestation and re-afforestation (AR) and building dykes and embankments (BDE) can be used as adaptive capacity under this category. Finally, households can minimise the cultural-susceptibility factors that generate their vulnerability through the application of warning systems for impending flooding (WS) and the use of indigenous and scientific knowledge (ISK). This is contrary to the PAR model (Wisner et al., 2004 ) and Urban Flood Vulnerability Assessment (Salami et al. 2017 ), which did not elaborate the adaptive strategies. However, the FVA relates well with the ISDR framework (2004) on adaptive capacity because the ISDR (2004) emphasizes disaster risk reduction through adaptive responses such as awareness knowledge, development of public commitment, application of risk reduction measures, early warning and preparedness (Mwale 2014 ).

5 Assumptions of the FVA framework

Assumptions are key to the realisation of the results. They are critical for achieving the successful implementation of an intervention. In this regard, the fact that the FVA framework provides the indicators which can be used to assess flood vulnerability in rural and urban informal settlements, the following eleven assumptions are vital to achieving the results:

The UFV should be constituted by physical, social, economic, environmental and cultural factors while the VC is composed of exposure, susceptibility and resilience to determine flood vulnerability. The selection of variables for these key components should consider vulnerability in a combination of physical and social sciences.

The UVFs and VCs should be linked to generate Physio-Exposure Factors (PEF), Socio-Susceptibility Factors (SSF), Eco-Resilience Factors (ERF), Enviro-Exposure Factors (EEF) and Cultural-Susceptibility Factors (CSF) to determine flood vulnerability or any particular hazard.

The generated indicators in the PEF, SSF, ERF, EEF and CSF should lead to the production of physio-exposure indicators (PEIs), social susceptibility indicators (SSIs), eco-resilience indicators (ERIs), enviro-exposure indicators (EEIs) and cultural-susceptibility indicators (CSIs), which in turn should capture indicators for FVA framework (Fig. 11 ).

A comprehensive flood vulnerability assessment framework that can give rise to multi-hazard vulnerability assessment should deviate from the common systematisations of vulnerability by using one set of variables. A combination of UVFs and VCs should be used to generate a wide range of issues and variables.

The linkage between the factors that amplify vulnerability and those that can enhance vulnerability reduction should be demonstrated through adaptive capacity and disaster risk reduction measures and incorporated in the framework. Those that cannot be quantified should be supported by qualitative methods.

The linkage of the UVFs and VCs as a key explanation of the generation of vulnerability should be emphasised and the conceptual framework for FVA should provide clear connectivity of the variables of the UVFs and VCs.

The variables for UVFs (physical, social, economic, environmental and cultural) should be measured as the absolute proportion value of household participants involved during the survey. The percentage values should be generated using a scale range with operators of “ less important ”; “ important ” and “ very important ” to contribute to flood vulnerability”. However, for flood vulnerability determination, a cut-off point should be placed at greater or equal to 0.5 (50%) for each indicator from the operator of the scale range of “important” and “very important”. In this case, all the values generated in the scale of “less important” as responded by the participants should be left out during determination of flood vulnerability.

The selected variables UVFs indicators (at 50%) should be tested using the variables of VCs (exposure, susceptibility and resilience) in the order stipulated in 2 and 3 through statistical tests using P-values or correlation (r) or simply any statistical test applicable by the researcher. The values that are significant at a certain confidence level (i.e. 0.05 in this study) should be selected to be included in the framework for specific combinations like PEFs, SSFs, ERFs, EEFs and CSFs (Fig. 11 ). Furthermore, those values significant at an appropriate confidence level should be considered as factors generating flood vulnerability.

Household vulnerability to floods should be predicted based on logistical regression test between the UVFs for all the operators of less important, important and very important and the VCs indicators (in exposure, susceptibility and resilience). The selection of the VC indicators should be based on those that were significant during the statistical test. Furthermore, variance independent factor (VIF) should be used to check the multicollinearity of the indicators for computation in the regression model.

Demographic characteristics should be statistically tested to determine their significant level of P-value 0.05 with the underlying vulnerability factors (UVFs) to explain who is vulnerable to what. However, because other explanations might be hidden in a quantitative assessment, a qualitative –in-depth assessment must be done to understand those hidden issues per se. In so doing, the assessment would be informative in identifying the factors that give rise to the pressures that generate vulnerable conditions in society for different groups.

Adaptive capacity should be assessed both quantitatively and qualitatively since it is a component of vulnerability reduction. This entails that if adaptive capacity is sufficient, it is likely that households' response to floods would be high and vulnerability is also likely to reduce and vice versa.

5.1 FVA application and comparability

The FVA should be applied as a pre-hazard, trans-hazard and post-hazard (flood) tool. In the pre-hazard category, all the proposed indicators should be used to determine vulnerable conditions which may (or may not) put some households at risk of flood disaster in the event of a flood occurrence. In the trans-hazard, the FVA indicators should be used to determine the vulnerabilities of households to identify the households that have been affected by floods as part of the disaster response and recovery process. In so doing, the FVA indicators should be used as a means of establishing strategies for disaster response and recovery as part of building back better. As a post-hazard tool, indicators should be used to determine the vulnerabilities that contributed to a disaster situation. Users should prioritize these indicators as a means of building DRR for disaster rehabilitation and reconstruction. In this case, the FVA framework contrasts itself to available tools such as the Unified Beneficiary Register (UBR) and Hazard Rapid Assessment (HRA) which largely are implemented only after the hazard in Malawi. Furthermore, it separates indicators that generate vulnerability in subsectors, but most available frameworks do not portray this separation. Therefore, participating enterprises can implement the FVA framework based on the needs of the assessment. The FVA framework can be implemented through hydrological assessment, flood modelling, quantitative, qualitative, GIS and remote sensing methodologies, giving opportunity to multiple users. The framework emphasizes UVFs (physical, social, economic, environmental and cultural) and VCs (exposure, susceptibility and resilience) as intersection constructs of flood vulnerability in urban and rural areas of Malawi and other places where it can be applied. It provides very simplified indicators of assessing flood vulnerability at local and national levels, deviating from the generalised frameworks that look at a wider scale like the PAR model (Wisner et al., 2004 ). More importantly, the framework provides tailor-made indicators thereby localizing the assessment of flood vulnerability in Malawi. This framework gives indicators that can be easily measured and evaluated at any level using different tools (statistical applications) thereby giving empirical scientific data on floods. The framework is coined strategically for researchers to utilise it in measuring the vulnerability of a single underlying factor of interest (i.e., physical vulnerability or social vulnerability etc.). It also gives simplified indicators that can be utilised by policy and decision-makers for planning interventions. The framework provides a good alignment of adaptive capacity to underlying vulnerability factors and components. In this case, the framework integrates DRR into vulnerability reduction strategies. Unlike the PAR model (Wisner et al., 2004 ) which does not explain exactly the measures of vulnerability reduction, this framework, through the integration of adaptive capacity, has filled up this gap. Finally, the framework intersects the significant factors of vulnerability in a set theory analysis giving new thinking in outlining FVA indicators in Malawi and beyond. The framework goes beyond the Community-Based Disaster Risk Index (CBDRI) by Bollin et al. (2003) which provides a proper link of indicators between vulnerability factors and components. For example, the CBDRI considers vulnerability components as structure, population, economy, environmental and capacity measures (Mwale et al. 2015 ) yet alone these could be grouped as conditions that generate flood vulnerability as tested in the FVA framework.

From the findings of this study, the FVA is comparable with various contemporary disaster management frameworks such as the PAR Model (Wisner et al. 2014 ), the Hazard of Place Framework (Cutter 1996 ), the Sustainable Livelihood Model (2004), the Community-based Disaster Risk Management Model (Kelman 2010 ), Turner et al. (2003) framework and the International Disaster Risk Reduction Framework (ISDR 2004). Therefore, based on the indicators intersected in Fig. 11 (such as housing conditions, access to information, access to resources, poor land use, social networks, and location), the FVA framework correlates well with most of the indicators stipulated in Hazard of place model (Cutter 1996 ), PAR model (Winser et al. 2014 ), Urban Flood Vulnerability Assessment Framework (Salami et al. 2017 ), ISDR framework (2004). However, the FVA framework has provided simplified indicators of flood vulnerability assessment because the indicators are simple to be used by experts and non-experts whether they are in urban or rural areas. They can be easily understood by ordinary users and policymakers. Furthermore, the indicators can be used for multi-hazards vulnerability assessment, since the H and F in the constituted equation can be changed based on hazard. In this case, the FVA Framework is widening vulnerability assessment beyond a focus on floods. The FVA, therefore, eliminates the gaps that most studies in literature mainly focus on, single hazards, ignoring the multi-hazard assessment (Kamanga et al. 2020 ). The FVA includes variables that can be measurable through quantitative and ANN (machine learning platform) thereby expanding the process of vulnerability analysis.

The FVA separated the indicators that generate vulnerability in different subsectors of UVFs and VCs. This separation deviates from most of the contemporary frameworks. Joakim ( 2008 ) noted that most contemporary frameworks fail to portray the linkages and networks that exist with the layers or sections leading to the vulnerability. For example, the PAR (Wisner et al. 2014 ) model provides a generalised causation of vulnerability. It portrays the progression of vulnerability from root causes to unsafe conditions, but it fails to explicitly acknowledge the linkages that exist within each progression (Joakim 2008 ). The FVA has provided a straightforward linkage of indicators by systematizing and assessing vulnerability in different subsectors. Similarly, the International Strategy for Disaster Reduction (ISDR) (2004) framework, the Hazard of Place Framework (HOP) (Cutter 1996 ), Borgardi, Birkmann and Cadona (BCC) (2004) and the Turner et al. (2003) framework, all have methodological difficulty of translation of some concepts into practice (Mwale 2014 ). This methodological variation, further makes the contemporary frameworks to be difficult to incorporate different links that exist between vulnerability factors. Mwale ( 2014 ) argues that the HOP framework does not provide a causal explanation of the vulnerability, instead variables are selected the way they are. Joakim ( 2008 ) further noted that the applicability of the HOP framework is a Canadian context, giving an impression that some indicators might manifest themselves differently in small political, economic and social processes. However, the HOP framework in some instances, relates very well with FVA, particularly the inclusion of perceptions, emphasis on understanding the underlying vulnerability factors, and inclusion of mitigation and adaptive capacity in the analysis of vulnerability. It is also highlighted that Turner II et al.’s (2003) framework is too theoretical and lacks specificity (Mwale 2014 ). This means that the framework is not simple and easy to use. The ISDR (2004) does not link the preparedness response system and thereby not explicit on how vulnerability can be reduced. Also, the use of one-dimensional indicators is demonstrated in the Turner II et al. (2003) framework which defines vulnerability in terms of exposure, susceptibility and responses. For this part, the ISDR (2004) defines vulnerability in the realms of social, economic, environmental and physical (Mwale 2014 ), missing the aspects of exposure, susceptibility and resilience. Above all, most of these frameworks have neglected to agglomerate the UVFs and VCs in their analysis and development of vulnerability frameworks. These FVA have attempted to fill these gaps, giving vulnerability assessment a new direction. In Malawi and SSA in general, Mwale et al. ( 2015 ) in a study of contemporary disaster management framework quantification of flood risk in rural lower Shire Valley, Malawi found medium, high and very high flood vulnerability in the same construct of indicators of the FVA framework. This implies that the FVA indicators are locally comparable and can be used for the decision-making process. The FVA indicators are more practical and can ably enhance community and household resilience. These indicators can thus be applied in promoting the resilience of communities to mitigate flood risks and key components for planning and decision-making processes.

6 Conclusion

This study carried out flood vulnerability assessment (FVA) using quantitative methods by utilising MCA, ANN (machine learning) and multiple logistical regression. The high flood vulnerability and lack of adaptive capacity among the households and communities in rural and urban informal settlements is an indication that catchment management in most areas remains a challenge to the water sector, disaster professionals and other players. This study highlights place settlement (proximity to catchments), low-risk knowledge, limited access to communication, poor sanitation, limited institutional capacity, and lack of alternative livelihoods as key drivers of flood vulnerability. These, among others, prevent households near the catchments from living in harmony and at peace with their water resources catchments. As the FVA framework specifies the indicators that contribute to flood vulnerability in rural and urban informal settlements, it is important to consider shifting towards investing in the adaptive capacity of communities along the catchments for better resilience building. The FVA framework considered adaptive capacity to mean actions taken by households to manage their catchments and livelihoods before, during and after floods. The adaptive measures entail the level of resilience households would be (or would not be) to floods. This study considered it crucial to constitute this framework in this manner to provide a roadmap for identifying the underlying causes of household levels of vulnerability to floods. This flood vulnerability assessment framework is applicable for both rural and urban and could be fit for purpose in sectors such as climate change, water resources management, disaster risk management, disaster risk reduction, integrated water management, food security, health, environmental management, engineering etc. The government might find the framework significant to establish clear regulations and accountability mechanisms to ensure that their involvement genuinely contributes to sustainable and equitable outcomes. Enterprises would find the framework useful for mapping vulnerability to natural hazards to address current and future risks in communities, including building community resilience and a line of separation with government.

7 Recommendations

The FVA framework is the first attempt to agglomerate operators in the UVFs and VCs through a multicollinearity analysis in a logit multiple regression to give rise to indicators in the PEIs, SSIs, ERIs, EEIs and CSIs for flood vulnerability assessment. The framework emphasises both understanding the conditions that generate vulnerability and those that can reduce vulnerability. Therefore, the study emphasises that the Malawi government through the Department of Disaster Management Affairs (DODMA) should strengthen disaster risk reduction by maintaining (1) political responsibility through the formulation of public policies with a clear understanding of people’s vulnerabilities (2) Legal responsibility through incorporating the framework as a way of perfecting the legal system, enforce the laws and establish laws that are not a centric symbol of disaster enterprise (3) Social responsibility through applying the framework to harmonise systems to be fair and just, without treating others in a sense of societal leniency, greenwashing practices and prioritisation of profit over environmental and social responsibility (4) Economic responsibility through utilising the framework to formulate relevant financial and economic measures i.e. disaster risk funds, to make disaster funds not to base on the declaration of a disaster.

Similarly, mapping vulnerability to natural hazards in urban areas should be enhanced to provide data necessary for developing disaster risk awareness and communication strategies vital to strengthening urban risk knowledge of natural hazards. The framework should be applied in promoting the resilience of communities to mitigate flood risks and can be a key component for planning and decision-making processes both in rural and urban areas. Finally, this study focused on one rural area and one urban informal area, so there is a need for district-wide or city-wide study and/or there is a need for study in urban between planned settlement and unplanned traditional housing areas (UTHA).

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The publication of this research received financial support from the NORHED Project on Climate Change and Ecosystem Management in Malawi and Tanzania (#63826) and the Centre for Resilient Agri-Food Systems (CRAFS) under the Africa Centres of Excellence (ACE2) programme.

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Centre for Environmental Affairs, Department of Geography and Environmental Studies, Faculty of Science, Catholic University of Malawi, P.O. Box 5452, Limbe, Malawi

Isaac Kadono Mwalwimba

Department of Built Environment, Faculty of Environmental Sciences, Mzuzu University, Private Bag 201, Luwinga Mzuzu 2, Malawi

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Department of Geography, Earth Sciences and Environment, School of Natural and Applied Sciences, University of Malawi, P.O. Box 280, Zomba, Malawi

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Ethical approval was sought from the Mzuzu University Research Ethics Committee (MZUNIREC). The MZUNIREC Permission was submitted to the Karonga District Council (KDC) and Lilongwe City Council (LCC). Participants were assured of confidentiality.

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Mwalwimba, I.K., Manda, M. & Ngongondo, C. Flood vulnerability assessment in rural and urban informal settlements: case study of Karonga District and Lilongwe City in Malawi. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06601-5

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Received : 08 October 2023

Accepted : 31 March 2024

Published : 24 May 2024

DOI : https://doi.org/10.1007/s11069-024-06601-5

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How to Pass the ICAEW ACA Case Study Exam
ACA Case Study
ICAEW Case Study Made Easy
How To Pass The ICAEW ACA Case Study Exam
How to Pass the ICAEW ACA Case Study Exam
ICAEW ACA Case Study

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Exam results
You will receive your results the day after you take your Certificate Level exam. They will be published here. You will receive your results for all Certificate Level exams, the day after you take the exam and usually five weeks after a Professional and Advanced Level exam session has taken place. Access your latest and archived exam results here.
ACA dates and deadlines
ACA Professional and Advanced Level exam dates, results dates and exam booking deadlines. Visit the Level 4 Accounting Technician page or Level 7 Accountancy Professional page for key dates and deadlines.. Exam system status. Remember to frequently check our dedicated exam system status page for all the latest exam updates which may affect you.
I got prizewinner for Case Study ACA and i'm so happy!
Case study tests very little accounting knowledge other than the ability to calculate profit, margins and variances (very basic stuff). The entire exam is mostly based around your detailed knowledge of a 50ish page case study business, its industry , financial performance, wider context and media articles, its recent developments and customer/supplier base etc. Literally everything you can ...
Exam results
The results of the ICAEW March 2024 ACA Professional Level exams were published here at 12:00 BST on Thursday 18 April 2024. If you have problems viewing the results, your computer may be showing content stored on the hard disk (cache). To view the most up-to-date information you will need to override your cache.
ICAEW Case Study Made Easy
The ICAEW Case Study exam is COMPLETELY different to all other ACA exams. ... Always state "any changes in the assumptions will affect the results of the model" for any ICAEW case study - it is an easy, guaranteed mark! ... above that requirement 2 and 3 have simple calculations but these can actually sometimes be tricky depending on the ...
The Affordable Care Act in the US (Obamacare)
Case Studies Examples of public policy succeeding or failing, drawing out the key lessons for future ... (45%) of the ACA. A Kaiser poll found similar results in 2015, with 43% holding a favourable view as against 42% with an unfavourable one. These figures, however, represented an improvement for Obamacare on a previous survey in July 2014 ...
How to Pass the ICAEW ACA Case Study Exam
3. Understand what information is provided in the Advance Information (AI) and what is provided in the exam. 4. Learn the Case Study exam technique which ensures that you hit all the boxes on the mark scheme. 5. Practice mock exams to master your exam technique. Step 1: Understand how Case Study is marked.
ACA Advanced Level
The ICAEW Advanced level exams present real-life scenarios, with greater complexity and wider implications than the Professional Level exams. The Case Study exam will present a complex business issue which will require problem solving, the identification of ethical implications and your ability to provide effective solutions.
How to pass the ICAEW ACA Case Study exam
Step 1 - write down how long the exam is in minutes. We know the exam is four hours long. This converts to 240 minutes in total. Step 2 - Take off 10 minutes from the total which is the time set aside to work out and write down your timings per question. This leaves 230 minutes of actual question-answering time.
How to Master the ICAEW ACA Case Study Marking Key
The maximum marks for the ACA Case Study is 240 marks. Remember, there are 40 boxes and the maximum mark is 6 per box. You may think as the ICAEW ACA Case study exam is 50% to pass, you just need 120 marks to pass. Again, the Case Study really is not that straightforward. 120 marks across the whole paper will not cut it.
ACA Advanced
Strategic Business Management (SBM) Essential. You'll demonstrate your quantitative and qualitative skills to make business recommendations in complex scenarios. You'll apply professional judgement to examine quantitative and qualitative data from many sources, to evaluate different solutions. done. Live Online. From US $ 126.67.
ACA Case Study
The ACA Advanced level Case Study (CS) exam is the final exam sat by ACA candidates. The Case Study exam is designed to test all areas developed in previous ACA exams. The exam places the student at the heart of a business and gives students a 'case' which students are given around 4 weeks prior to the exam. The case is a fictional ...
ICAEW Prize-Winning Tutor
Case Study. Having scored 92% in the Case Study exam, and winning the ICAEW prize for the highest score in the world, Kieran now teaches students the analytical skills and exam technique required to emulate this success. Under his guidance, one of our students also went on to win the ICAEW prize for the highest score in the world (score of 90% ...
ACA Exam
Case Study. Module covers: tests professional skills in the context of a specific business issue; ... ACA Results and Passing Rates. The results of the ACA exams are published on the ICAEW website, and the results in letters are later posted to the students. In 2019, the cumulative pass percentage of ACA was above 70%. ...
Case Study Mock Exam Pack
The other important aspect of preparing for Case Study is that it is a long, 4-hour exam (the longest of all the ACA papers) and one where you must pass all the different elements of the examination (Executive Summary, Requirement 1, Requirement 2 and Requirement 3) or you will be failed by the examiners, no matter how brilliant the other parts ...
How to Pass the ICAEW ACA Case Study Exam in 2021
To pass a box, you need to get 3 of the points included in that box. Below is an example of a Case Study mark scheme to illustrate what I mean by points and boxes. Step 2: Understand what each of ...
Tips and advice for case study? : r/ICAEW
These can be useful to practise more exams on your actual case study if you want, but pricey unless you can split it between a few people. Most important thing is learn your case study inside out. Make lists of useful contextual points from it and read the example papers on ICAEW website for past sittings and the examiners comments. 2.
Case Study module study resources
The Case Study exam will assess your understanding of providing advice on complex business issues in the form of a written report. The scenario may be based on a variety of different organisational structures or operations, and you will be provided with advance information ahead of the exam. The exam is four hours long and will consist of three ...
ACA Aponix Portfolio Company Case Studies
This case study describes how a large telecommunications company concerned about cybersecurity threats such as ransomware attacks and business email compromise partnered with ACA Aponix to identify and create a plan to remediate risks. This case study explains how a large cap private equity firm used ACA's M&A diligence and advisory services to ...
Arresting failure propagation in buildings through collapse ...
A design approach arrests collapse propagation in buildings after major initial failures by ensuring that specific elements fail before the failure of the most important components for global ...
Wegovy users keep weight off for four years, Novo Nordisk study says
Patients in the trial, called Select, lost an average of nearly 10% of their total body weight after 65 weeks on Wegovy. That percentage weight-loss was roughly sustained year-on-year until the ...
Flood vulnerability assessment in rural and urban informal ...
Flood vulnerability assessment (FVA) informs the disaster risk reduction and preparedness process in both rural and urban areas. However, many flood-vulnerable regions like Malawi still lack FVA supporting frameworks in all phases (pre-trans-post disaster). Partly, this is attributed to lack of the evidence-based studies to inform the processes. This study was therefore aimed at assessing ...
Spore variability in Hepaticae: a case study on four short-lived
Introduction . In the present study, we tested the variability of spore morphology in freshly collected plants of Riccia bifurca Hoffm., R. glauca L., R. sorocarpa Bisch. and R. warnstorfii Limpr. ex Warnst. at the individual and population scale, and examined the taxonomical power of distinct characters that are used in the literature for species delimitation in the genus Riccia.
Advanced Level
The Case Study tests the knowledge, skills and experience you have gained to date. The Advanced Level exams are fully open book, replicating a real-life scenario where all the resources are at your fingertips. The exams are available to sit every July and November.
Land
The urban texture is the physical manifestation of the urban form's evolution. In the rapid process of urbanization, protecting and reshaping the urban texture has become an essential means to sustain the overall form and vitality of cities. Previous studies in this field have primarily relied on image analysis or typological methods, lacking a quantitative approach to identify and analyze ...
ACA exam FAQs
Certificate Level exam results are only available online. You can access these on the exams results page. By text. Register to receive your exam results by text message as part of the online exam application process or using the results notification link of the online exam applications system. The deadline for applications for receiving results ...
Introduction to the Case Study exam
This series of Case Study exam resources will cover everything you need to know to prepare for the Case Study exam. This includes: Introduction to the Case Study exam. How to approach the advance information. Using the advance information during the Case Study exam. A guide on Requirements one, two and three. An overview of the Executive Summary.
Getting your results
You can receive your exam results by text, via your online training file or by calling us. Certificate Level exam results. You can access your results 24 hours after your exam via your online training file under the 'Examinations' tab. However, please note that if you receive your exam results on the same day as the ACA Professional or Advanced Level exam results are released, they will be ...

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