A Methodology for Risk Analysis Based on Hybrid Bayesian Networks: Application to the Regasification System of Liquefied Natural Gas Onboard a Floating Storage and Regasification Unit

This article presents an iterative six‐step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the world's capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty.     

Finding and fixing systems weaknesses: probabilistic methods and applications of engineering risk analysis.

Methods of engineering risk analysis are based on a functional analysis of systems and on the probabilities (generally Bayesian) of the events and random variables that affect their performances. These methods allow identification of a system's failure modes, computation of its probability of failure or performance deterioration per time unit or operation, and of the contribution of each component to the probabilities and consequences of failures. The model has been extended to include the human decisions and actions that affect components' performances, and the management factors that affect behaviors and can thus be root causes of system failures. By computing the risk with and without proposed measures, one can then set priorities among different risk management options under resource constraints. In this article, I present briefly the engineering risk analysis method, then several illustrations of risk computations that can be used to identify a system's weaknesses and the most cost-effective way to fix them. The first example concerns the heat shield of the space shuttle orbiter and shows the relative risk contribution of the tiles in different areas of the orbiter's surface. The second application is to patient risk in anesthesia and demonstrates how the engineering risk analysis method can be used in the medical domain to rank the benefits of risk mitigation measures, in that case, mostly organizational. The third application is a model of seismic risk analysis and mitigation, with application to the San Francisco Bay area for the assessment of the costs and benefits of different seismic provisions of building codes. In all three cases, some aspects of the results were not intuitively obvious. The probabilistic risk analysis (PRA) method allowed identifying system weaknesses and the most cost-effective way to fix them.     

Modeling Finite‐Time Failure Probabilities in Risk Analysis Applications

In this article, we introduce a framework for analyzing the risk of systems failure based on estimating the failure probability. The latter is defined as the probability that a certain risk process, characterizing the operations of a system, reaches a possibly time‐dependent critical risk level within a finite‐time interval. Under general assumptions, we define two dually connected models for the risk process and derive explicit expressions for the failure probability and also the joint probability of the time of the occurrence of failure and the excess of the risk process over the risk level. We illustrate how these probabilistic models and results can be successfully applied in several important areas of risk analysis, among which are systems reliability, inventory management, flood control via dam management, infectious disease spread, and financial insolvency. Numerical illustrations are also presented.     

A New Approach to Hazardous Materials Transportation Risk Analysis: Decision Modeling to Identify Critical Variables

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity , and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.     

Discrete Probability Distributions for Probabilistic Fracture Mechanics

Recently, discrete probability distributions (DPDs) have been suggested for use in risk analysis calculations to simplify the numerical computations which must be performed to determine failure probabilities. Specifically, DPDs have been developed to investigate probabilistic functions, that is, functions whose exact form is uncertain. The analysis of defect growth in materials by probabilistic fracture mechanics (PFM) models provides an example in which probabilistic functions play an important role. This paper compares and contrasts Monte Carlo simulation and DPDs as tools for calculating material failure due to fatigue crack growth. For the problem studied, the DPD method takes approximately one third the computation time of the Monte Carlo approach for comparable accuracy. It is concluded that the DPD method has considerable promise in low-failure-probability calculations of importance in risk assessment. In contrast to Monte Carlo, the computation time for the DPD approach is relatively insensitive to the magnitude of the probability being estimated.     

On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment

A central part of probabilistic public health risk assessment is the selection of probability distributions for the uncertain input variables. In this paper, we apply the first-order reliability method (FORM)^(1–3) as a probabilistic tool to assess the effect of probability distributions of the input random variables on the probability that risk exceeds a threshold level (termed the probability of failure) and on the relevant probabilistic sensitivities. The analysis was applied to a case study given by Thompson et al. ⁽⁴⁾ on cancer risk caused by the ingestion of benzene contaminated soil. Normal, lognormal, and uniform distributions were used in the analysis. The results show that the selection of a probability distribution function for the uncertain variables in this case study had a moderate impact on the probability that values would fall above a given threshold risk when the threshold risk is at the 50th percentile of the original distribution given by Thompson et al. ⁽⁴⁾ The impact was much greater when the threshold risk level was at the 95th percentile. The impact on uncertainty sensitivity, however, showed a reversed trend, where the impact was more appreciable for the 50th percentile of the original distribution of risk given by Thompson et al. ⁴ than for the 95th percentile. Nevertheless, the choice of distribution shape did not alter the order of probabilistic sensitivity of the basic uncertain variables.     

Information systems project failure – analysis of causal links using interpretive structural modelling

The analysis of the root causes of information systems project failure has been the subject of intense scrutiny for some time within industry and the academic community. Researchers have developed various models, notions of failure and categorisations to succinctly classify project failure into a set of key factors for organisations and project managers to focus on in their attempts to avoid failure. This study incorporates a technique titled: interpretive structural modelling as the methodology to formalise the relationships between the selected failure factors. This approach is positioned as a mechanism that can yield greater insights into the relationships between the factors surrounding project failure, thereby developing a better understanding of how these relationships can have a bearing on project outcomes. The findings identify key driving variables that are presented as having significant impact on the other factors within the model. A number of variables are also identified as being heavily dependent on other connected factors highlighting that a failure in one or more of these connected factors is likely to result in a failure in one or more of the dependent factors unless timely steps are taken to address these key issues. This research details a number of practical implications for senior management and project managers as well as the academic community. These considerations form an underlying thread within this study as specific practice-related implications are highlighted and discussed throughout the study.     

Application of Correspondence Analysis in the Assessment of Mine Tailings Dam Breakage Risk in the Mediterranean Region

A new statistical approach for preliminary risk evaluation of breakage in tailings dam is presented and illustrated by a case study regarding the Mediterranean region. The objective of the proposed method is to establish an empirical scale of risk, from which guidelines for prioritizing the collection of further specific information can be derived. The method relies on a historical database containing, in essence, two sets of qualitative data: the first set concerns the variables that are observable before the disaster (e.g., type and size of the dam, its location, and state of activity), and the second refers to the consequences of the disaster (e.g., failure type, sludge characteristics, fatalities categorization, and downstream range of damage). Based on a modified form of correspondence analysis, where the second set of attributes are projected as "supplementary variables" onto the axes provided by the eigenvalue decomposition of the matrix referring to the first set, a "qualitative regression" is performed, relating the variables to be predicted (contained in the second set) with the "predictors" (the observable variables). On the grounds of the previously derived relationship, the risk of breakage in a new case can be evaluated, given observable variables. The method was applied in a case study regarding a set of 13 test sites where the ranking of risk obtained was validated by expert knowledge. Once validated, the procedure was included in the final output of the e-EcoRisk UE project (A Regional Enterprise Network Decision-Support System for Environmental Risk and Disaster Management of Large-Scale Industrial Spills), allowing for a dynamic historical database updating and providing a prompt rough risk evaluation for a new case. The aim of this section of the global project is to provide a quantified context where failure cases occurred in the past for supporting analogue reasoning in preventing similar situations.     

A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system‐wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.     

销售成本信息不对称下供应商风险规避的回购契约

在价格随机条件下,销售成本信息不对称且供应商规避风险时,本文探讨回购契约协调供应链的最优决策。在前提假设的基础上构建新的回购契约模型,求解并用算例进行仿真验证,考虑信息不对称与风险规避共同发生耦合作用后对供应链相关决策变量的影响。研究结果表明：在价格随机条件下,不管信息是否对称,只要供应商有风险规避意识,供应链相关决策变量均发生分岔突变;不管市场价格是否随机,也不管供应商是否风险规避,只要零售商隐瞒私人销售成本信息,就会给自己带来额外的收益,但会给供应商与整个供应链带来损害;供应链上的信息越不对称,在分岔突变区域,相关决策变量的振荡幅度越大。分岔突变现象是市场价格随机和供应商风险规避耦合作用后特有的现象;零售商能够利用信息不对称给自己带来额外的好处,但会损害供应商和供应链的利益;供应商防范零售商这种损人利己行为的最好对策,就是通过设计一种合作机制,以最低成本的方式来促使零售商将销售成本信息公开化;另外,供应商以平稳的心态(风险中性)应对外部风险,更有利于提高其自身决策的水平。     

Adoption of Individual Flood Damage Mitigation Measures in New York City: An Extension of Protection Motivation Theory

This study offers insights into factors of influence on the implementation of flood damage mitigation measures by more than 1,000 homeowners who live in flood‐prone areas in New York City. Our theoretical basis for explaining flood preparedness decisions is protection motivation theory, which we extend using a variety of other variables that can have an important influence on individual decision making under risk, such as risk attitudes, time preferences, social norms, trust, and local flood risk management policies. Our results in relation to our main hypothesis are as follows. Individuals who live in high flood risk zones take more flood‐proofing measures in their home than individuals in low‐risk zones, which suggests the former group has a high threat appraisal. With regard to coping appraisal variables, we find that a high response efficacy and a high self‐efficacy play an important role in taking flood damage mitigation measures, while perceived response cost does not. In addition, a variety of behavioral characteristics influence individual decisions to flood‐proof homes, such as risk attitudes, time preferences, and private values of being well prepared for flooding. Investments in elevating one's home are mainly influenced by building code regulations and are negatively related with expectations of receiving federal disaster relief. We discuss a variety of policy recommendations to improve individual flood preparedness decisions, including incentives for risk reduction through flood insurance, and communication campaigns focused on coping appraisals and informing people about flood risk they face over long time horizons.     

A Quality Risk Management Model Approach for Cell Therapy Manufacturing

International regulatory authorities view risk management as an essential production need for the development of innovative, somatic cell‐based therapies in regenerative medicine. The available risk management guidelines, however, provide little guidance on specific risk analysis approaches and procedures applicable in clinical cell therapy manufacturing. This raises a number of problems. Cell manufacturing is a poorly automated process, prone to operator‐introduced variations, and affected by heterogeneity of the processed organs/tissues and lot‐dependent variability of reagent (e.g., collagenase) efficiency. In this study, the principal challenges faced in a cell‐based product manufacturing context (i.e., high dependence on human intervention and absence of reference standards for acceptable risk levels) are identified and addressed, and a risk management model approach applicable to manufacturing of cells for clinical use is described for the first time. The use of the heuristic and pseudo‐quantitative failure mode and effect analysis/failure mode and critical effect analysis risk analysis technique associated with direct estimation of severity, occurrence, and detection is, in this specific context, as effective as, but more efficient than, the analytic hierarchy process. Moreover, a severity/occurrence matrix and Pareto analysis can be successfully adopted to identify priority failure modes on which to act to mitigate risks. The application of this approach to clinical cell therapy manufacturing in regenerative medicine is also discussed.     

Factors in Risk Perception

Risk perception is a phenomenon in search of an explanation. Several approaches are discussed in this paper. Technical risk estimates are sometimes a potent factor in accounting for perceived risk, but in many important applications it is not. Heuristics and biases, mainly availability, account for only a minor portion of risk perception, and media contents have not been clearly implicated in risk perception. The psychometric model is probably the leading contender in the field, but its explanatory value is only around 20% of the variance of raw data. Adding a factor of "unnatural risk" considerably improves the psychometric model. Cultural Theory, on the other hand, has not been able to explain more than 5–10% of the variance of perceived risk, and other value scales have similarly failed. A model is proposed in which attitude, risk sensitivity, and specific fear are used as explanatory variables; this model seems to explain well over 30–40% of the variance and is thus more promising than previous approaches. The model offers a different type of psychological explanation of risk perception, and it has many implications, e.g., a different approach to the relationship between attitude and perceived risk, as compared with the usual cognitive analysis of attitude.     

The Emergence of Flood Insurance in Canada: Navigating Institutional Uncertainty

Flood insurance has remained unavailable in Canada based on an assessment that it lacks economic viability. In response to Canada's costliest flood event to date in 2013, the Canadian insurance industry has started to develop a framework to expand existing property insurance to cover flood damage. Research on flood insurance has overlooked why and how insurance systems transition to expand insurance coverage without evidence of economic viability. This article will address this gap through a case study on the emergence of flood insurance in Canada, and the approach to its expansion. Between 2013 and 2016, insurance industry officials representing over 60% of premiums collected in Canada were interviewed. These interviews revealed that flood insurance is being expanded in response to institutional pressure, specifically external stakeholder expectations that the insurance industry will adopt a stronger role in managing flood risk through coverage of flood damage. Further evidence of this finding is explored by assessing the emergence of a unique flood insurance model that involves a risk‐adjusted and optional product along with an expansion of government policy supporting flood risk mitigation. This approach attempts to balance industry concerns about economic viability with institutional pressure to reduce flood risk through insurance. This analysis builds on existing research by providing the first scholarly analysis of flood insurance in Canada, important “empirical” teeth to existing conceptual analysis on the availability of flood insurance, and the influence of institutional factors on risk analysis within the insurance sector.     

Sensitivity Analysis of a Two‐Dimensional Quantitative Microbiological Risk Assessment: Keeping Variability and Uncertainty Separated

The aim of quantitative microbiological risk assessment is to estimate the risk of illness caused by the presence of a pathogen in a food type, and to study the impact of interventions. Because of inherent variability and uncertainty, risk assessments are generally conducted stochastically, and if possible it is advised to characterize variability separately from uncertainty. Sensitivity analysis allows to indicate to which of the input variables the outcome of a quantitative microbiological risk assessment is most sensitive. Although a number of methods exist to apply sensitivity analysis to a risk assessment with probabilistic input variables (such as contamination, storage temperature, storage duration, etc.), it is challenging to perform sensitivity analysis in the case where a risk assessment includes a separate characterization of variability and uncertainty of input variables. A procedure is proposed that focuses on the relation between risk estimates obtained by Monte Carlo simulation and the location of pseudo‐randomly sampled input variables within the uncertainty and variability distributions. Within this procedure, two methods are used—that is, an ANOVA‐like model and Sobol sensitivity indices—to obtain and compare the impact of variability and of uncertainty of all input variables, and of model uncertainty and scenario uncertainty. As a case study, this methodology is applied to a risk assessment to estimate the risk of contracting listeriosis due to consumption of deli meats.     

Beyond demand and control: The contribution of organizational risk factors in assessing the psychological well-being of health care employees

Abstract

The job demand–control(–support) model is frequently used as a theoretical framework in studies on determinants of psychological well-being. Consequently, these studies are confined to the impact of job characteristics on worker outcomes. In the present study the relation between work conditions and outcomes (job satisfaction, emotional exhaustion, psychological distress, and somatic complaints) is examined from a broader organizational perspective. This paper reports on an analysis that examines both the unique and the additional contribution of organizational characteristics to well-being indicators, beyond those attributed to job characteristics. A total of 706 care staff from three public residential institutions for people with mental or physical disabilities in the Netherlands took part in this research. To assess organizational risk factors a measurement instrument was developed, the organizational Risk Factors Questionnaire (ORFQ), based on the safety-critical factors of the Tripod accident causation model. Factor analyses and reliability testing resulted in a 52-item scale consisting of six reliable sub-scales: staffing resources, communication, social hindrance, training opportunities, job skills, and material resources. These organizational risk factors explained important parts of the variance in each of the outcome measures, beyond that accounted for by demographic variables and job demand–control–support (JDCS) measures. Communication and training opportunities were of central importance to carers’ job satisfaction. Social hindrance, job skills, and material resources explained a substantial amount of unique variance on the negative outcomes investigated.     

One Agency's Use of Risk Assessment and Risk Communication1

Few organizations have the courage to evaluate their own use of risk assessment (identifying hazards and estimating their probability and magnitude) and risk communication (interacting with internal and external stakeholder groups about risks). The USDA Animal and Plant Health Inspection Service (APHIS) wants to enhance its overall risk analysis process for managing a wide range of risks to animals, plants, and human health. We gathered survey data for a baseline of APHIS professionals’ understanding and use of risk assessment and risk communication. APHIS professionals spend a surprisingly large share of their time communicating about risks. They perceive that risk estimates influence decisions, but that risk estimates should have more influence. Respondents reported little opposition to APHIS risk management decisions, and little use of channels such as USDA Extension Service for disseminating risk messages. Substantial variance across responses is explained mostly by differences in the roles of the 11 work units (now 10) within the agency. Location also contributes to the variance. Demographic variables seem less important.     

基于CAPM的BOT项目“有限追索权”融资决策模型

为研究BOT项目有限追索权融资中贷款资金与股本资金在贷方和项目公司之间合理分配问题(即BOT最优融资结构),本文考虑项目公司和贷方根据CAPM方法进行投资决策,通过分析它们投资策略在利益上的冲突关系而建立一个BOT融资模型,并且用博弈论方法研究模型最优解的存在性及其性质。研究结果不仅为项目公司和贷方提供了对BOT项目融资决策的理论方法,而且为政府对BOT项目的管理提供了重要的理论工具。     

Risk assessment based on a STPA–FMEA method: A case study of a sweeping robot

Despite rapid developments in the quality and safety of consumer products, the rise of intelligent household appliances, such as sweeping robots, has introduced new safety concerns. Considering “person–product–environment” elements and the complex systems of emerging consumer products, this study presents a new method of risk assessment for consumer products: systems theoretic process analysis (STPA)–failure mode and effects analysis (FMEA). As a case study, this method is applied to the safety control of a sweeping robot. The results suggest that this method can identify all the possible failure modes and injury scenarios among the product components, and the safety constraints in the hierarchical control structure of the interactive system. Moreover, the STPA–FMEA method combines user and environmental factors with the value of product risk events, based on the risk priority number (RPN). This provides an accurate and orderly system to reduce or eliminate the root causes of accidents and injuries. Finally, analysis of unsafe control behavior and its causes can be used to suggest improved safety constraints, which can effectively reduce the risk of some injury scenarios. This paper presents a new method of risk assessment for consumer products and a general five-level complex index system.