Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well‐established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents’ relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor‐based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.     

Development of Economic Consequence Methodology for Process Risk Analysis

A comprehensive methodology for economic consequence analysis with appropriate models for risk analysis of process systems is proposed. This methodology uses loss functions to relate process deviations in a given scenario to economic losses. It consists of four steps: definition of a scenario, identification of losses, quantification of losses, and integration of losses. In this methodology, the process deviations that contribute to a given accident scenario are identified and mapped to assess potential consequences. Losses are assessed with an appropriate loss function (revised Taguchi, modified inverted normal) for each type of loss. The total loss is quantified by integrating different loss functions. The proposed methodology has been examined on two industrial case studies. Implementation of this new economic consequence methodology in quantitative risk assessment will provide better understanding and quantification of risk. This will improve design, decision making, and risk management strategies.     

Public Response to a Near‐Miss Nuclear Accident Scenario Varying in Causal Attributions and Outcome Uncertainty

Many studies have investigated public reactions to nuclear accidents. However, few studies focused on more common events when a serious accident could have happened but did not. This study evaluated public response (emotional, cognitive, and behavioral) over three phases of a near‐miss nuclear accident. Simulating a loss‐of‐coolant accident (LOCA) scenario, we manipulated (1) attribution for the initial cause of the incident (software failure vs. cyber terrorist attack vs. earthquake), (2) attribution for halting the incident (fail‐safe system design vs. an intervention by an individual expert vs. a chance coincidence), and (3) level of uncertainty (certain vs. uncertain) about risk of a future radiation leak after the LOCA is halted. A total of 773 respondents were sampled using a 3 × 3 × 2 between‐subjects design. Results from both MANCOVA and structural equation modeling (SEM) indicate that respondents experienced more negative affect, perceived more risk, and expressed more avoidance behavioral intention when the near‐miss event was initiated by an external attributed source (e.g., earthquake) compared to an internally attributed source (e.g., software failure). Similarly, respondents also indicated greater negative affect, perceived risk, and avoidance behavioral intentions when the future impact of the near‐miss incident on people and the environment remained uncertain. Results from SEM analyses also suggested that negative affect predicted risk perception, and both predicted avoidance behavior. Affect, risk perception, and avoidance behavior demonstrated high stability (i.e., reliability) from one phase to the next.     

Multiattribute Risk Analysis in Nuclear Emergency Management

Radiation protection authorities have seen a potential for applying multiattribute risk analysis in nuclear emergency management and planning to deal with conflicting objectives, different parties involved, and uncertainties. This type of approach is expected to help in the following areas: to ensure that all relevant attributes are considered in decision making; to enhance communication between the concerned parties, including the public; and to provide a method for explicitly including risk analysis in the process. A multiattribute utility theory analysis was used to select a strategy for protecting the population after a simulated nuclear accident. The value-focused approach and the use of a neutral facilitator were identified as being useful.     

Human Error Contribution in Collision and Grounding of Oil Tankers

The purpose of this article is to present a quantitative analysis of the human failure contribution in the collision and/or grounding of oil tankers, considering the recommendation of the “Guidelines for Formal Safety Assessment” of the International Maritime Organization. Initially, the employed methodology is presented, emphasizing the use of the technique for human error prediction to reach the desired objective. Later, this methodology is applied to a ship operating on the Brazilian coast and, thereafter, the procedure to isolate the human actions with the greatest potential to reduce the risk of an accident is described. Finally, the management and organizational factors presented in the “International Safety Management Code” are associated with these selected actions. Therefore, an operator will be able to decide where to work in order to obtain an effective reduction in the probability of accidents. Even though this study does not present a new methodology, it can be considered as a reference in the human reliability analysis for the maritime industry, which, in spite of having some guides for risk analysis, has few studies related to human reliability effectively applied to the sector.     

Quantitative Risk Analysis on the Transport of Dangerous Goods Through a Bi‐Directional Road Tunnel

A quantitative risk analysis (QRA) regarding dangerous goods vehicles (DGVs) running through road tunnels was set up. Peak hourly traffic volumes (VHP), percentage of heavy goods vehicles (HGVs), and failure of the emergency ventilation system were investigated in order to assess their impact on the risk level. The risk associated with an alternative route running completely in the open air and passing through a highly populated urban area was also evaluated. The results in terms of social risk, as F/N curves, show an increased risk level with an increase the VHP, the percentage of HGVs, and a failure of the emergency ventilation system. The risk curves of the tunnel investigated were found to lie both above and below those of the alternative route running in the open air depending on the type of dangerous goods transported. In particular, risk was found to be greater in the tunnel for two fire scenarios (no explosion). In contrast, the risk level for the exposed population was found to be greater for the alternative route in three possible accident scenarios associated with explosions and toxic releases. Therefore, one should be wary before stating that for the transport of dangerous products an itinerary running completely in the open air might be used if the latter passes through a populated area. The QRA may help decisionmakers both to implement additional safety measures and to understand whether to allow, forbid, or limit circulation of DGVs.     

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.     

Decision Making During Nuclear Power Plant Incidents—A New Approach to the Evaluation of Precursor Events

Renewed interest in precursor analysis has shown that the evaluation of near misses is an interdisciplinary effort, fundamental within the life of an organization for reducing operational risks and enabling accident prevention. The practice of precursor analysis has been a part of nuclear power plant regulation in the United States for over 25 years. During this time, the models used in the analysis have evolved from simple risk equations to quite complex probabilistic risk assessments. But, one item that has remained constant over this time is that the focus of the analysis has been on modeling the scenario using the risk model (regardless of the model sophistication) and then using the results of the model to determine the severity of the precursor incident. We believe that evaluating precursors in this fashion could be a shortcoming since decision making during the incident is not formally investigated. Consequently, we present the idea for an evaluation procedure that enables one to integrate current practice with the evaluation of decisions made during the precursor event. The methodology borrows from technologies both in the risk analysis and the decision analysis realms. We demonstrate this new methodology via an evaluation of a U.S. precursor incident. Specifically, the course of the incident is represented by the integration of a probabilistic risk assessment model (i.e., the risk analysis tool) with an influence diagram and the corresponding decision tree (i.e., the decision analysis tools). The results and insights from the application of this new methodology are discussed.     

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness,Planning, and Response for Severe Nuclear Power Plant Accidents

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.     

Speaking the Truth in Maritime Risk Assessment

Several major risk studies have been performed in recent years in the maritime transportation domain. These studies have had significant impact on management practices in the industry. The first, the Prince William Sound risk assessment, was reviewed by the National Research Council and found to be promising but incomplete, as the uncertainty in its results was not assessed. The difficulty in assessing this uncertainty is the different techniques that need to be used to model risk in this dynamic and data-scarce application area. In previous articles, we have developed the two pieces of methodology necessary to assess uncertainty in maritime risk assessment, a Bayesian simulation of the occurrence of situations with accident potential and a Bayesian multivariate regression analysis of the relationship between factors describing these situations and expert judgments of accident risk. In this article, we combine the methods to perform a full-scale assessment of risk and uncertainty for two case studies. The first is an assessment of the effects of proposed ferry service expansions in San Francisco Bay. The second is an assessment of risk for the Washington State Ferries, the largest ferry system in the United States.     

Distribution Analyzer and Risk Evaluator (DARE) Using Fault Trees

Risk and uncertainty are integral parts of modern technology, and they must be managed effectively to allow the development of reliable, high-quality products. Because so many facets of technology and society involve risk and uncertainty, it is essential that risk management be handled in a systematic manner. Fault-tree analysis is one of the principal methods used in the analysis of systems'safety. Its detailed and systematic deductive structure makes it a valuable tool for design and diagnostic purposes. Point probability and the minimization of the expected failure probability have, until recently, dominated fault-tree analysis. A methodology that incorporates uncertainty analysis, conditional expected risk, and multiple objectives with fault-tree analysis is presented. A computer software package termed the "Distribution Analyzer and Risk Evaluator (DARE) Using Fault Trees," which translates the new methodology into a working decision-support system, is developed. DARE Using Fault Trees is a flexible computer code that is capable of analyzing the risk of the overall system in terms of the probability density function of failure probability. Emphasis is placed on the uncertainty and risk of extreme events. A comparative study between existing codes for fault-tree analysis and DARE demonstrates the strengths of the methodology. A case study for NASA's solid rocket booster is used to perform the comparative analysis.     

A Methodology for Determining Interactions in Probabilistic Safety Assessment Models by Varying One Parameter at a Time

In risk analysis problems, the decision‐making process is supported by the utilization of quantitative models. Assessing the relevance of interactions is an essential information in the interpretation of model results. By such knowledge, analysts and decisionmakers are able to understand whether risk is apportioned by individual factor contributions or by their joint action. However, models are oftentimes large, requiring a high number of input parameters, and complex, with individual model runs being time consuming. Computational complexity leads analysts to utilize one‐parameter‐at‐a‐time sensitivity methods, which prevent one from assessing interactions. In this work, we illustrate a methodology to quantify interactions in probabilistic safety assessment (PSA) models by varying one parameter at a time. The method is based on a property of the functional ANOVA decomposition of a finite change that allows to exactly determine the relevance of factors when considered individually or together with their interactions with all other factors. A set of test cases illustrates the technique. We apply the methodology to the analysis of the core damage frequency of the large loss of coolant accident of a nuclear reactor. Numerical results reveal the nonadditive model structure, allow to quantify the relevance of interactions, and to identify the direction of change (increase or decrease in risk) implied by individual factor variations and by their cooperation.     

AGAPE-ET: A Methodology for Human Error Analysis of Emergency Tasks

This article introduces a human error analysis or human reliability analysis methodology, AGAPE-ET (A Guidance And Procedure for Human Error Analysis for Emergency Tasks), for analyzing emergency tasks in nuclear power plants. The AGAPE-ET method is based on a simplified cognitive model and a set of performance-influencing factors (PIFs). At each cognitive function, error-causing factors (ECF) or error-likely situations have been identified considering the characteristics of the performance of each cognitive function and the influencing mechanism of the PIFs on the cognitive function. Then, a human error analysis procedure based on the error analysis factors is organized to cue or guide the analyst in conducting the human error analysis. The method can be characterized by the structured identification of weak points of the task required to be performed and by the efficient analysis process such that the analyst has only to carry out the analysis with the necessary cognitive functions. Through the application, AGAPE-ET showed its usefulness, which effectively identifies the vulnerabilities with respect to cognitive performance as well as task execution, and that helps the analyst directly draw specific error reduction measures through the analysis.     

Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network

Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow-ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.     

Cost-Benefit Analysis of All-Terrain Vehicles at the CPSC1

The present value of the costs of injuries and deaths associated with each unit of a product is a useful number in conducting cost-benefit analysis at the CPSC. Before a remedy is known, this value indicates the approximate amount that it is worth spending on a fix; similarly, if a fix is proposed, this number indicates whether it is cost-justified. The method of analysis is applied to all-terrain vehicles. Injury costs per vehicle are estimated. Determinants of accidents are estimated using a logit regression model applied to surveys of users and of accident victims; this indicates factors associated with accidents, and is used to estimate marginal probabilities. Costs of various vehicle components are estimated using a hedonic price index. This information (injury costs, accident probabilities, and component cost) is combined in a benefit–cost analysis of proposed remedies.     

Treatment of Uncertainty in Performance Assessments for Complex Systems

When viewed at a high level, performance assessments (PAs) for complex systems involve two types of uncertainty: stochastic uncertainty, which arises because the system under study can behave in many different ways, and subjective uncertainty, which arises from a lack of knowledge about quantities required within the computational implementation of the PA. Stochastic uncertainty is typically incorporated into a PA with an experimental design based on importance sampling and leads to the final results of the PA being expressed as a complementary cumulative distribution function (CCDF). Subjective uncertainty is usually treated with Monte Carlo techniques and leads to a distribution of CCDFs. This presentation discusses the use of the Kaplan/Garrick ordered triple representation for risk in maintaining a distinction between stochastic and subjective uncertainty in PAs for complex systems. The topics discussed include (1) the definition of scenarios and the calculation of scenario probabilities and consequences, (2) the separation of subjective and stochastic uncertainties, (3) the construction of CCDFs required in comparisons with regulatory standards (e.g., 40 CFR Part 191, Subpart B for the disposal of radioactive waste), and (4) the performance of uncertainty and sensitivity studies. Results obtained in a preliminary PA for the Waste Isolation Pilot Plant, an uncertainty and sensitivity analysis of the MACCS reactor accident consequence analysis model, and the NUREG-1150 probabilistic risk assessments are used for illustration.     

Variability of Site-Reactor Risk

Reactor accident consequence models have been developed (for example, the CRAC model of the Reactor Safety Study (RSS), WASH-1400) to predict the offsite health and economic consequences of severe accidents at a reactor site with generic demographic and meteorological characteristics. Application of a revised RSS accident consequence model, CRAC2, to 91 existing sites results in a band of risk curves around the earlier WASH-1400 average reactor/site predictions. This paper examines these calculations and important model assumptions such as population distribution, emergency response, and meteorological data with respect to their effects on site risk extremes—that is, the combination of high consequence/low probability events.     

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.     

The Health Impact of Major Nuclear Accidents: The Case Of Greece

An assessment of the radiological consequences that would result for the population of Greece from postulated major nuclear accidents in the Kozloduy nuclear power station in Bulgaria is performed. Kozloduy lies at a distance of 225 km from the northern borders of Greece and contains six reactors, all of the Russian WWER type. The postulated accidents that are classified as level 7 accidents on the International Nuclear Event Scale, involve significant releases of radioactive materials into the environment, and widespread health and environmental effects. The analysis is performed by the MACCS code. The estimated consequences are compared to the corresponding actual impact of the Chernobyl accident in Greece. The results of the analysis indicate that, under the conservative assumptions adopted, the radiological consequences of the most severe accidents considered would be about 1.5 orders of magnitude larger than the actual radiological consequences of the Chernobyl accident.