Probabilistic Reliability Analysis, Quantitative Safety Goals, and Nuclear Licensing in the United Kingdom

Although unpublicized, the use of quantitative safety goals and probabilistic reliability analysis for licensing nuclear reactors has become a reality in the United Kingdom. This conclusion results from an examination of the process leading to the licensing of the Sizewell B PWR in England. The licensing process for this reactor has substantial implications for nuclear safety standards in Britain, and is examined in the context of the growing trend towards quantitative safety goals in the United States.     

Strengthening Safety Compliance in Nuclear Power Operations: A Role‐Based Approach

Safety compliance is of paramount importance in guaranteeing the safe running of nuclear power plants. However, it depends mostly on procedures that do not always involve the safest outcomes. This article introduces an empirical model based on the organizational role theory to analyze the influence of legitimate sources of expectations (procedures formalization and leadership) on workers’ compliance behaviors. The sample was composed of 495 employees from two Spanish nuclear power plants. Structural equation analysis showed that, in spite of some problematic effects of proceduralization (such as role conflict and role ambiguity), procedure formalization along with an empowering leadership style lead to safety compliance by clarifying a worker's role in safety. Implications of these findings for safety research are outlined, as well as their practical implications.     

Evaluating the Cost,Safety, and Proliferation Risks of Small Floating Nuclear Reactors

It is hard to see how our energy system can be decarbonized if the world abandons nuclear power, but equally hard to introduce the technology in nonnuclear energy states. This is especially true in countries with limited technical, institutional, and regulatory capabilities, where safety and proliferation concerns are acute. Given the need to achieve serious emissions mitigation by mid‐century, and the multidecadal effort required to develop robust nuclear governance institutions, we must look to other models that might facilitate nuclear plant deployment while mitigating the technology's risks. One such deployment paradigm is the build‐own‐operate‐return model. Because returning small land‐based reactors containing spent fuel is infeasible, we evaluate the cost, safety, and proliferation risks of a system in which small modular reactors are manufactured in a factory, and then deployed to a customer nation on a floating platform. This floating small modular reactor would be owned and operated by a single entity and returned unopened to the developed state for refueling. We developed a decision model that allows for a comparison of floating and land‐based alternatives considering key International Atomic Energy Agency plant‐siting criteria. Abandoning onsite refueling is beneficial, and floating reactors built in a central facility can potentially reduce the risk of cost overruns and the consequences of accidents. However, if the floating platform must be built to military‐grade specifications, then the cost would be much higher than a land‐based system. The analysis tool presented is flexible, and can assist planners in determining the scope of risks and uncertainty associated with different deployment options.     

Risk Management and Decision Rules for Light Water Reactors

A quantitative approach to risk management for accidents in light water nuclear power reactors is proposed to serve as one focus for discussion. In this proposal risk management is divided into two major tasks: the predominantly social and political task of setting the safety goals and the technical task of estimating the risks and deciding whether the safety goals have been met. The proposed safety goals include limits on the following: risk to the individual of early death or delayed cancer death; overall societal risk of early or delayed death; and the frequency of core melt accidents as well as the frequency of containment failure, given a core melt. Also included are a small element of risk aversion and an "as low as reasonably achievable" (ALARA) approach with a cost-effectviness criterion which includes both economic and health effects.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Compressed Natural Gas Bus Safety: A Quantitative Risk Assessment

This study assesses the fire safety risks associated with compressed natural gas (CNG) vehicle systems, comprising primarily a typical school bus and supporting fuel infrastructure. The study determines the sensitivity of the results to variations in component failure rates and consequences of fire events. The components and subsystems that contribute most to fire safety risk are determined. Finally, the results are compared to fire risks of the present generation of diesel-fueled school buses. Direct computation of the safety risks associated with diesel-powered vehicles is possible because these are mature technologies for which historical performance data are available. Because of limited experience, fatal accident data for CNG bus fleets are minimal. Therefore, this study uses the probabilistic risk assessment (PRA) approach to model and predict fire safety risk of CNG buses. Generic failure data, engineering judgments, and assumptions are used in this study. This study predicts the mean fire fatality risk for typical CNG buses as approximately 0.23 fatalities per 100-million miles for all people involved, including bus passengers. The study estimates mean values of 0.16 fatalities per 100-million miles for bus passengers only. Based on historical data, diesel school bus mean fire fatality risk is 0.091 and 0.0007 per 100-million miles for all people and bus passengers, respectively. One can therefore conclude that CNG buses are more prone to fire fatality risk by 2.5 times that of diesel buses, with the bus passengers being more at risk by over two orders of magnitude. The study estimates a mean fire risk frequency of 2.2 x 10(-5) fatalities/bus per year. The 5% and 95% uncertainty bounds are 9.1 x 10(-6) and 4.0 x 10(-5), respectively. The risk result was found to be affected most by failure rates of pressure relief valves, CNG cylinders, and fuel piping.     

Modeling intelligent adversaries for terrorism risk assessment: some necessary conditions for adversary models

Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often-implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis.     

Using Probability Boxes to Model Elicited Information: A Case Study

Governments are responsible for making policy decisions, often in the face of severe uncertainty about the factors involved. Expert elicitation can be used to fill information gaps where data are not available, cannot be obtained, or where there is no time for a full‐scale study and analysis. Various features of distributions for variables may be elicited, for example, the mean, standard deviation, and quantiles, but uncertainty about these values is not always recorded. Distributional and dependence assumptions often have to be made in models and although these are sometimes elicited from experts, modelers may also make assumptions for mathematical convenience (e.g., assuming independence between variables). Probability boxes (p‐boxes) provide a flexible methodology to analyze elicited quantities without having to make assumptions about the distribution shape. If information about distribution shape(s) is available, p‐boxes can provide bounds around the results given these possible input distributions. P‐boxes can also be used to combine variables without making dependence assumptions. This article aims to illustrate how p‐boxes may help to improve the representation of uncertainty for analyses based on elicited information. We focus on modeling elicited quantiles with nonparametric p‐boxes, modeling elicited quantiles with parametric p‐boxes where the elicited quantiles do not match the elicited distribution shape, and modeling elicited interval information.     

Analysis of Uncertainties in Interactive Probabilistic Safety Analysis (PSA) Models

Probabilistic safety analysis (PSA) has been used in nuclear, chemical, petrochemical, and several other industries. The probability and/or frequency results of most PSAs are based on average component unavailabilities during the mission of interest. While these average results are useful, they provide no indication of the significance of the facility's current status when one or more components are known to be out of service. Recently, several interactive computational models have been developed for nuclear power plants to allow the user to specify the plant's status at a particular time (i.e., to specify equipment known to be out of service) and then to receive updated PSA information. As with conventional PSA results, there are uncertainties associated with the numerical updated results. These uncertainties stem from a number of sources, including parameter uncertainty (uncertainty in equipment failure rates and human error probabilities). This paper presents an analysis of the impact of parameter uncertainty on updated PSA results.     

Conundrums with Uncertainty Factors

The practice of uncertainty factors as applied to noncancer endpoints in the IRIS database harkens back to traditional safety factors. In the era before risk quantification, these were used to build in a “margin of safety.” As risk quantification takes hold, the safety factor methods yield to quantitative risk calculations to guarantee safety. Many authors believe that uncertainty factors can be given a probabilistic interpretation as ratios of response rates, and that the reference values computed according to the IRIS methodology can thus be converted to random variables whose distributions can be computed with Monte Carlo methods, based on the distributions of the uncertainty factors. Recent proposals from the National Research Council echo this view. Based on probabilistic arguments, several authors claim that the current practice of uncertainty factors is overprotective. When interpreted probabilistically, uncertainty factors entail very strong assumptions on the underlying response rates. For example, the factor for extrapolating from animal to human is the same whether the dosage is chronic or subchronic. Together with independence assumptions, these assumptions entail that the covariance matrix of the logged response rates is singular. In other words, the accumulated assumptions entail a log‐linear dependence between the response rates. This in turn means that any uncertainty analysis based on these assumptions is ill‐conditioned; it effectively computes uncertainty conditional on a set of zero probability. The practice of uncertainty factors is due for a thorough review. Two directions are briefly sketched, one based on standard regression models, and one based on nonparametric continuous Bayesian belief nets.     

Competitive Goals and Plant Investment in Environment and Safety Practices: Moderating Effect of National Culture

Operations managers clearly play a critical role in targeting plant‐level investments toward environment and safety practices. In principle, a “rational” response would be to align this investment with senior management's competitive goals for operational performance. However, operations managers also are influenced by contingent factors, such as their national culture, thus creating potential tension that might bias investment away from a simple rational response. Using data from 1,453 plants in 24 countries, we test the moderating influence of seven of the national cultural characteristics on investment at the plant level in environment and safety practices. Four of the seven national cultural characteristics from GLOBE (i.e., uncertainty avoidance, in‐group collectivism, future orientation and performance orientation) shifted investment away from an expected “rational” response. Positive bias was evident when the national culture favored consistency and formalized procedures and rewarded performance improvement. In contrast, managers exhibited negative bias when familial groups and local coalitions were powerful, or future outcomes—rather than current actions—were more important. Overall, this study highlights the critical importance of moving beyond a naïve expectation that plant‐level investment will naturally align with corporate competitive goals for environment and safety. Instead, the national culture where the plant is located will influence these investments, and must be taken into account by senior management.     

Quantitative Risk Assessment of Listeria monocytogenes in French Cold-Smoked Salmon: II. Risk Characterization

A model for the assessment of exposure to Listeria monocytogenes from cold-smoked salmon consumption in France was presented in the first of this pair of articles (Pouillot et al ., 2007, Risk Analysis, 27:683–700). In the present study, the exposure model output was combined with an internationally accepted hazard characterization model, adapted to the French situation, to assess the risk of invasive listeriosis from cold-smoked salmon consumption in France in a second-order Monte Carlo simulation framework. The annual number of cases of invasive listeriosis due to cold-smoked salmon consumption in France is estimated to be 307, with a very large credible interval ([10; 12,453]), reflecting data uncertainty. This uncertainty is mainly associated with the dose-response model. Despite the significant uncertainty associated with the predictions, this model provides a scientific base for risk managers and food business operators to manage the risk linked to cold-smoked salmon contaminated with L. monocytogenes. Under the modeling assumptions, risk would be efficiently reduced through a decrease in the prevalence of L. monocytogenes or better control of the last steps of the cold chain (shorter and/or colder storage during the consumer step), whereas reduction of the initial contamination levels of the contaminated products and improvement in the first steps of the cold chain do not seem to be promising strategies. An attempt to apply the recent risk-based concept of FSO (food safety objective) on this example underlines the ambiguity in practical implementation of the risk management metrics and the need for further elaboration on these concepts.     

Framework for Evaluation of Physiologically-Based Pharmacokinetic Models for Use in Safety or Risk Assessment

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic models, raise the issue of how to evaluate whether the models are adequate for proposed uses, including safety or risk assessment. A six-step process for model evaluation is described. It relies on multidisciplinary expertise to address the biological, toxicological, mathematical, statistical, and risk assessment aspects of the modeling and its application. The first step is to have a clear definition of the purpose(s) of the model in the particular assessment; this provides critical perspectives on all subsequent steps. The second step is to evaluate the biological characterization described by the model structure based on the intended uses of the model and available information on the compound being modeled or related compounds. The next two steps review the mathematical equations used to describe the biology and their implementation in an appropriate computer program. At this point, the values selected for the model parameters (i.e., model calibration) must be evaluated. Thus, the fifth step is a combination of evaluating the model parameterization and calibration against data and evaluating the uncertainty in the model outputs. The final step is to evaluate specialized analyses that were done using the model, such as modeling of population distributions of parameters leading to population estimates for model outcomes or inclusion of early pharmacodynamic events. The process also helps to define the kinds of documentation that would be needed for a model to facilitate its evaluation and implementation.     

Safety sans Frontières: An International Safety Culture Model

The management of safety culture in international and culturally diverse organizations is a concern for many high‐risk industries. Yet, research has primarily developed models of safety culture within Western countries, and there is a need to extend investigations of safety culture to global environments. We examined (i) whether safety culture can be reliably measured within a single industry operating across different cultural environments, and (ii) if there is an association between safety culture and national culture. The psychometric properties of a safety culture model developed for the air traffic management (ATM) industry were examined in 17 European countries from four culturally distinct regions of Europe (North, East, South, West). Participants were ATM operational staff (n = 5,176) and management staff (n = 1,230). Through employing multigroup confirmatory factor analysis, good psychometric properties of the model were established. This demonstrates, for the first time, that when safety culture models are tailored to a specific industry, they can operate consistently across national boundaries and occupational groups. Additionally, safety culture scores at both regional and national levels were associated with country‐level data on Hofstede's five national culture dimensions (collectivism, power distance, uncertainty avoidance, masculinity, and long‐term orientation). MANOVAs indicated safety culture to be most positive in Northern Europe, less so in Western and Eastern Europe, and least positive in Southern Europe. This indicates that national cultural traits may influence the development of organizational safety culture, with significant implications for safety culture theory and practice.     

A Comparison Between Probabilistic and Dempster‐Shafer Theory Approaches to Model Uncertainty Analysis in the Performance Assessment of Radioactive Waste Repositories

Model uncertainty is a primary source of uncertainty in the assessment of the performance of repositories for the disposal of nuclear wastes, due to the complexity of the system and the large spatial and temporal scales involved. This work considers multiple assumptions on the system behavior and corresponding alternative plausible modeling hypotheses. To characterize the uncertainty in the correctness of the different hypotheses, the opinions of different experts are treated probabilistically or, in alternative, by the belief and plausibility functions of the Dempster‐Shafer theory. A comparison is made with reference to a flow model for the evaluation of the hydraulic head distributions present at a radioactive waste repository site. Three experts are assumed available for the evaluation of the uncertainties associated with the hydrogeological properties of the repository and the groundwater flow mechanisms.     

Safety culture: Key theoretical issues

The organizational preconditions to major systems failures are seen as increasingly important for risk management. However, existing empirical attempts to study safety culture and its relationship to organizational outcomes have remained fragmented and underspecified in theoretical terms. This is despite the existence of a number of well-developed theories of organizationally induced accidents and disasters. Reasons for this disfunction of theory and practice are first considered. The paper then outlines four key theoretical questions for safety culture researchers: the fact that culture acts simultaneously as a precondition both for safe operations and for the oversight of incubating hazards (the paradox of 'safety' culture); the challenge of dealing with complex and ill-structured hazardous situations where decision makers are faced with deep forms of uncertainty represented by incompleteness of knowledge or ignorance; the need to consider the construction of risk perceptions in workgroups, and to view risk acceptability as the outcome of a process of social negotiation; and the fact that institutional politics and power are critical for determining the achievement of safety culture goals, and in particular that of organizational learning.     

辱虐管理、心理安全感知与员工建言

基于中国组织情境中辱虐管理对员工建言行为影响的研究,认为企业上级领导实施辱虐管理,首先会削弱员工的心理安全感,继而影响员工建言行为,心理安全感知是辱虐管理与员工建言之间的关系的中介。此外,研究结论还表明,辱虐管理对心理安全感知的削弱作用会受到员工不确定性规避特质的影响,对于高不确定性规避的员工而言,企业上级领导实施辱虐管理,此类员工感受到的"心理不安全"更强。最后,以来自新疆维吾尔自治区2家石化企业中的258名员工和102名上级领导的配对数据验证了以上理论假设。     

Modeling Microbial Growth Within Food Safety Risk Assessments

Risk estimates for food-borne infection will usually depend heavily on numbers of microorganisms present on the food at the time of consumption. As these data are seldom available directly, attention has turned to predictive microbiology as a means of inferring exposure at consumption. Codex guidelines recommend that microbiological risk assessment should explicitly consider the dynamics of microbiological growth, survival, and death in foods. This article describes predictive models and resources for modeling microbial growth in foods, and their utility and limitations in food safety risk assessment. We also aim to identify tools, data, and knowledge sources, and to provide an understanding of the microbial ecology of foods so that users can recognize model limits, avoid modeling unrealistic scenarios, and thus be able to appreciate the levels of confidence they can have in the outputs of predictive microbiology models. The microbial ecology of foods is complex. Developing reliable risk assessments involving microbial growth in foods will require the skills of both microbial ecologists and mathematical modelers. Simplifying assumptions will need to be made, but because of the potential for apparently small errors in growth rate to translate into very large errors in the estimate of risk, the validity of those assumptions should be carefully assessed. Quantitative estimates of absolute microbial risk within narrow confidence intervals do not yet appear to be possible. Nevertheless, the expression of microbial ecology knowledge in "predictive microbiology" models does allow decision support using the tools of risk assessment.     

Safety culture: Key theoretical issues

Abstract

The organizational preconditions to major systems failures are seen as increasingly important for risk management. However, existing empirical attempts to study safety culture and its relationship to organizational outcomes have remained fragmented and underspecified in theoretical terms. This is despite the existence of a number of well-developed theories of organizationally induced accidents and disasters. Reasons for this disfunction of theory and practice are first considered. The paper then outlines four key theoretical questions for safety culture researchers: the fact that culture acts simultaneously as a precondition both for safe operations and for the oversight of incubating hazards (the paradox of ‘safety’ culture); the challenge of dealing with complex and ill-structured hazardous situations where decision makers are faced with deep forms of uncertainty represented by incompleteness of knowledge or ignorance; the need to consider the construction of risk perceptions in workgroups, and to view risk acceptability as the outcome of a process of social negotiation; and the fact that institutional politics and power are critical for determining the achievement of safety culture goals, and in particular that of organizational learning.     

Influence of Safety Measures on the Risks of Transporting Dangerous Goods Through Road Tunnels

Quantitative risk assessment (QRA) models are used to estimate the risks of transporting dangerous goods and to assess the merits of introducing alternative risk reduction measures for different transportation scenarios and assumptions. A comprehensive QRA model recently was developed in Europe for application to road tunnels. This model can assess the merits of a limited number of "native safety measures." In this article, we introduce a procedure for extending its scope to include the treatment of a number of important "nonnative safety measures" of interest to tunnel operators and decisionmakers. Nonnative safety measures were not included in the original model specification. The suggested procedure makes use of expert judgment and Monte Carlo simulation methods to model uncertainty in the revised risk estimates. The results of a case study application are presented that involve the risks of transporting a given volume of flammable liquid through a 10-km road tunnel.