The efficient delivery of potable water for a community through its distribution system has historically been the backbone of nearly all metropolitan developments. Much of these systems are comprised of pipe networks made of various materials including concrete, iron, PVC, and even steel. As these communities expand and urbanize, water demand and population density simultaneously increase. This develops higher strains and stresses in the community‟s water distribution network causing pipes to corrode, crack, or rupture prematurely while in service. As a result, the deterioration of water distribution systems in growing cities is increasingly becoming a major concern for our nation. There have been several publications on the subject of evaluating pipe conditions within a water distribution network that use statistical models, estimation, and other mathematical analyses. However, many of these publications are cumbersome and are difficult to understand from a non-engineering perspective. In order to simplify the evaluation process for all varying professions in a city‟s public works division, the primary objective of this study was to develop a user-friendly risk assessment model that was practical, cost effective, and easy to follow. This risk assessment model focuses primarily on the physical condition of pipes in a water distribution system. It assesses the installation year, age, material, and break history of these water mains. It does not consider pipe fittings, pumps, or other network components. A pipe‟s probability of failure is determined from its physical condition. Page v The model then considers various economic degrees of impact that may affect the rehabilitation or replacement of these water mains. These degrees of impact include raw material costs, customer criticality, land use, demand, pipe material, and traffic impact. By focusing on pipes having the highest probability of failure and considering their economic impacts, this model identifies and prioritizes the most vulnerable water mains that require immediate attention. In order to validate this developed risk assessment model, the method was applied to a real water distribution system. Data from the City of Arroyo Grande, California was used in conjunction with WaterCAD and geographic information systems (ArcGIS) software during analysis. Application of the risk assessment model identified six cast iron pipes in Arroyo Grande‟s water distribution system as having a high risk of failure. Of the city‟s 3,057 individual pipe segments, recognizing only five of these pipes as high risk indicated that the assessment model was functional. Developing and testing this risk assessment model with real city data effectively demonstrated its practicality and easy application to a real water distribution system. If utilized, city officials can quickly identify and prioritize pipes needing rehabilitation or replacement by using reliable, up-to-date water distribution data from their city with this risk assessment model. Furthermore, use of this model may also simplify allocation of capital funds for future pipe improvement projects as the city continues its urbanization.

Natural hazards are a growing risk across the globe. As regions have urbanized, single events impact greater proportions of the population, and the populations within those regions have become more dependent on infrastructure systems. Regional resilience has become closely tied to the performance of infrastructure. For a comprehensive risk assessment losses caused by lifeline outage must be considered alongside structural and nonstructural risks. Many well developed techniques quantify structural and nonstructural risk; however, there are insufficient procedures to determine the likelihood of lifeline outages. Including lifelines in seismic assessments will provide a comprehensive risk, improving a decision maker’s capacity to efficiently balance mitigation against the full spectrum of risks. An ideal lifeline risk assessment is infeasible due to the large geographic scale of lifeline systems and their system structure; these same characteristics also make them vulnerable to disruption in hazard events. Probabilistic methods provide solutions for their analysis, but many of the necessary analysis variables remain unknown. Continued research and increased collection of infrastructure data may improve the ability of advanced probabilistic methods to study and forecast performance of lifelines, but many inputs for a complete probabilistic model are likely to remain unknown. This thesis recognizes these barriers to assessment and proposes a methodology that uses consequences to simplify analysis of lifeline systems. Risk is often defined as the product of probability of failure and consequence. Many assessments study the probability of failure and then consider the consequence. This thesis proposes the opposite, studying consequence first. In a theoretical model where all information is available the difference in approach is irrelevant; the results are the same regardless of order. In the real world however, studying consequence first provides an opportunity to simplify the system assessment. The proposed methodology starts with stakeholders defining consequences that constitute ruin, and then the lifeline system is examined and simplified to components that can produce such consequences. Previously large and expansive systems can be greatly simplified and made more approachable systems to study. The simplified methodology does not result in a comprehensive risk assessment, rather it provides an abbreviated risk profile of catastrophic risk; risk that constitutes ruin. By providing an assessment of only catastrophic lifeline risk, the risk of greatest importance is measured, while smaller recoverable risk remains unknown. This methodology aligns itself with the principle of resilience, the ability to withstand shocks and rebound. Assessments can be used directly to consider mitigation options that directly address stakeholder resilience. Many of the same probabilistic issues remain, but by simplifying the process, abbreviated lifelines assessments are more feasible providing stakeholders with information to make decisions in an environment that currently is largely unknown.

Yes / System safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios. / This work was funded by the DEIS H2020 project (Grant Agreement 732242).

Yes / In a dynamic business environment like the energy sector, power plants face several complex risks, including both technical and non-technical risks. These risks are not isolated, as their impact may affect a series of interrelated risks. Those risks may change with time, which in turn, makes the strategic decision-making process less effective. Understanding the dynamic behaviour of a complex system is very important to achieve a more sustainable overall performance of the power plants. Thus, it is important to further develop a systematic risk assessment methodology that could help to identify and analyse the interdependencies among risks and to understand the dynamics of these risks in complex systems. This paper develops a system dynamics (SD) methodology to support the development of risk assessment models. This paper highlights the environmental perspective. The first step to develop a SD model will be applied, while the final SD model will be discussed in another paper.

The thesis describes a stochastic procedure developed for assessing risk and reducing uncertainty inherent in the investment decision making process. It is proposed that the two most important profitability financial ratios in relation to investment decisions are the return on equity and the return on assets respectively. In order to exploit their use as criteria for risk measurement and uncertainty reduction, a stochastic formulation is adopted in which these ratios are expressed in probabilistic terms. A density function to describe their behaviour is derived; it is found that density distribution analysis for both ratios indicate that the Weibull distribution apart from being the most flexible and adaptable model of all those considered, provides the best overall fit to the data. It is accordingly used in the latter part of the research for evaluating industrial sector and company investment risk.

Keeping users’ and organizations’ data secure is a challenging task. The situation is made more complicated due to the ever-increasing complex dependencies among IT systems. In this scenario, current approaches for risk assessment and mitigation rely on industry best practices based on qualitative assessments that do not provide any measure of their effectiveness. In this Thesis, we argue that the rich availability of data about IT infrastructures and adversaries must be employed to quantitatively measure the risk and the effectiveness of security mitigation strategies. Our goal is to show that quantitative measures of effectiveness and cost using security data are not only possible but also beneficial for both individual users and organizations to identify the most appropriate security plan. To this aim, we employed a heterogeneous set of security data spanning from blacklist feeds and software vulnerability repositories to web third-party dynamics, criminal forums, and threat intelligence reports. We use this data to model attackers and security mitigation strategies and evaluate their effectiveness in mitigating attacks. We start with an evaluation of filter lists of privacy extensions to protect individuals’ privacy when browsing the Web. We then consider the security of billions of users accessing the Top 5K Alexa domains and evaluated the effectiveness and cost of security mitigations at different levels of the Internet infrastructure. We then evaluate the accuracy of SOC analysts in investigating alerts related to cyber attacks targeting a network. Finally, we develop methodologies for the analysis of the effectiveness of ML models to detect criminal discussions in forums and software updates to protect against targeted attacks performed by nation-state groups.

Climate change is predicted to alter the rainfall patterns in the future, and extreme rain events with large rainfall volumes will become more frequent and intense which increases the flood risk. A clear trend can be seen, where more and more people decide to relocate from rural to urban areas. The concentration of people, infrastructure, businesses and social services in urban areas makes them particularly vulnerable to floods due to the large economic consequences that ensue. Analyzing the future flood risk is therefore of high importance in order to adapt to the changing climate and lower the consequences of future floods. Estimating the flood risk of an area is complicated and usually abstract, especially as i) the definition and understanding of risk varies a lot, ii) there is a large shortage of consistent data and iii) decision management plans are made several years ahead even though it’s hard to predict how cities will evolve. In a hydro-economical flood risk analysis, the risk is expressed in monetary terms, here in terms of Expected Annual Damage (EAD). In order to calculate the true risk of an area, EAD, for any given year there is a need of calculating all consequences for “all” events. With today’s technical limitations it is not possible to compute all possible events in an effective way, which makes the practice both time-consuming and expensive, therefore usually only a several events are considered. Among others, this paper aim to give a better understanding of how this several events should be picked to still get a good estimation. A flood risk assessment of the city Tierp, Sweden, was performed as part of the master thesis for use as decision support. The flood hazard was mapped through hydrodynamic modelling using the 2D-modelling program MIKE21 developed by DHI. The model simulated the future flood extent in the year 2100 by using precipitation corresponding to the return periods 1, 2, 5, 10, 20, 30, 50, 100, 200 and 500 years, with a climate factor of 1.25. The economic consequences were evaluated in the DHI program City Adaptation Decision Support System (CADSS) where the flood maps were overlaid with assets of different categories with assigned damage costs. The program allowed calculation of the flood risk in terms of an expected annual damage (EAD), and the choice and combination of return periods when calculating the EAD was assessed to see how it affects the outcome. Furthermore, sustainable drainage systems (SuDS) were implemented in the hydrodynamic model and a hydro-economic analysis was performed through a cost-benefit analysis to find the optimum design return period of the structures. The flood risk assessment showed that Tierp would face an EAD of 4 304 181 SEK in the year 2100 if climate change predictions proved accurate. The accuracy of the EAD calculation was found to increase with the number of included return periods, where the inclusion of lower return periods was seen to have a larger impact on the outcome compared to higher return periods. The hydro-economic optimization of mitigation structures concluded that the optimum design return period is 50 years, and the benefit of implementing SuDS of larger dimensions is minimal. However, more dimensions need to be included in the optimization to validate this result.  Flood risk assessments have a large potential for being used as decision support in Sweden, but lack of national damage cost data makes the result uncertain and difficult to validate. More research would also be required to better understand the relation between floods and the damage they cause for Swedish conditions. / I takt med klimatförändringarna förutspås extrema regnhändelser med stora regnmängder och hög intensitet att öka i framtiden. Samtidigt väljer allt fler människor att flytta från landsbygden in till städerna vilket bidrar till att exponering av sårbara värden som människor, infrastruktur och verksamheter koncentreras. Det i sin tur innebär att risken för översvämning ökar, då översvämningarna förväntas ske mer frekvent samtidigt som risken för att konsekvenser uppstår till följd av översvämningen ökar. För att förebygga översvämningar eller minska risken i framtiden krävs en hållbar stadsplanering, där första steget är att försöka estimera översvämningsrisken inom ett område. Uppskattning av översvämningsrisken är komplicerat. Dels eftersom i) definitionen och kunskapen av risk varierar mycket, ii) det råder stor brist på data och iii) planering och beslut görs flera år i förväg, samtidigt som det är svårt att förutsäga hur städerna kommer att utvecklas. I en ekonomisk riskanalys av översvämningar uttrycks risken i monetära termer som i denna rapport definierats som förväntad årlig skada (Expected Annual Damage, EAD). För att beräkna den verkliga risken för ett område, EAD, för ett visst år måste man beräkna alla konsekvenser för ”alla” händelser. På grund av dagens tekniska begränsningar är det inte möjligt att beräkna alla sannolika händelser på ett effektivt sätt vilket gör det både tidskrävande och dyrt. Syftet med denna rapport är därför bland annat att ge en bättre förståelse för hur ett urval av händelser kan göras för att ändå ge en bra uppskattning. En del av masteruppsatsen bestod i att kartlägga översvämningsrisken i staden Tierp, Sverige. Översvämningsrisken simulerades genom hydrodynamisk modellering med hjälp av 2D-modelleringsprogrammet MIKE 21, utvecklat av DHI. Med hjälp av modellen simulerades den framtida översvämningsutbredningen år 2100 vid nederbörd med återkomsttiderna 1, 2, 5, 10, 20, 30, 50, 100, 200 och 500 år, med en klimatfaktor på 1,25. De ekonomiska konsekvenserna utvärderades i DHI-programmet City Adaptation Decision Support System (CADSS) där översvämningskartor jämfördes med kategoriserade byggnader och dess respektive förväntade skadekostnader. Med hjälp av CADSS kunde översvämningsrisken i form av EAD beräknas, och kombinationen av olika återkomstperioder vid beräkningen av EAD kunde bedömas för att se hur det påverkar resultatet. Utöver detta implementerades hållbara skyfallslösningar i den hydrodynamiska modellen och en hydroekonomisk analys utfördes genom en kostnads-nyttoanalys för att hitta den optimala designåterkomsttiden för lösningarna. Bedömningen av översvämningsriskerna visade att Tierp skulle drabbas av en EAD på 4 304 181 SEK år 2100 om de förväntade klimatförändringarna visar sig vara korrekta. Noggrannheten i EAD-beräkningen visade sig öka med antalet inkluderade återkomsttider, där inkluderandet av lägre återkomsttider visade sig ha en större inverkan på resultatet jämfört med högre återkomsttider. Den hydroekonomiska optimeringen av begränsningsstrukturer ledde till slutsatsen att den optimala återkomstperioden är 50 år, och att nyttan av att införa skyfallslösningar dimensionerade för större återkomsttider är minimal. Fler dimensioner måste dock inkluderas i optimeringen för att bekräfta detta resultat.  Bedömningar av översvämningsrisker har en stor potential att användas som beslutsstöd i Sverige, men bristen på nationella uppgifter om skadekostnader gör resultatet osäkert och svårt att validera. Det krävs även mer forskning för att bättre förstå relationen mellan översvämningar och de skador de orsakar för svenska förhållanden.

The most common mode of levee failure, breach due to overtopping, is generally considered as a function of a complex set of contributing factors. The goal of this research is to enhance the state of the art and practice for performing levee overtopping risk assessment. For this purpose, a dataset of levee overtopping event records within the portfolio of levee systems maintained by the U.S. Army Corps of Engineers (USACE) is presented. The dataset is utilized with logistic regression analysis to develop a probabilistic model to calculate system response probabilities and assess risk related to levee overtopping. The presented dataset can be used for identifying key factors controlling overtopping behavior, validation of model results, and providing new insight into the phenomenon of levee overtopping. The proposed model offers a practical yet robust tool for levee risk analysis and can be readily employed by engineers and other stakeholders.

This research is to validate a risk assessment model?s ability to predict work-related musculoskeletal disorders (WMSDs). The model looks at the primary risk factors of repetition, force, deviated posture, tool design, duration, and frequency. The sum of the ratings for each factor is the risk assessment score for the person doing a certain job task. According to the score, the task is rated as a ?no risk? to an ?extreme risk? task. The data used for this research was from an epidemiological study preformed at a fish processing facility. To validate the model, the scores from the risk assessment model were compared to the operators? severity and frequency of pain in the median nerve distribution of the hand and to previous risk model. The statistical tests show that the risk assessment model can predict if the operator performing a task is at risk for forming a WMSD.

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