Uncertainties in Nuclear Probabilistic Risk Analyses

There are many uncertainties in a probabilistic risk analysis (PRA). We identify the different types of uncertainties and describe their implications. We then summarize the uncertainty analyses which have performed in current PRAs and characterize results which have been obtained. We draw conclusions regarding interpretations of uncertainties, areas having largest uncertainties, and needs which exist in uncertainty analysis. We finally characterize the robustness of various utilizations of PRA results.     

Probabilistic Risk Analysis for a High-Level Radioactive Waste Repository

A probabilistic risk analysis (PRA) for a high-level radioactive waste repository is very important since it gives an estimate of its health impacts, allowing comparisons to be made with the health impacts of competing technologies. However, it is extremely difficult to develop a credible PRA for a specific repository site because of large uncertainties in future climate, hydrology, geological processes, etc. At best, such a PRA would not be understandable to the public. An alternative proposed here is to develop a PRA for an average U.S. site, taking all properties of the site to be the U.S. average. The results are equivalent to the average results for numerous randomly selected sites. Such a PRA is presented here; it is easy to understand, and it is not susceptible to substantial uncertainty. Applying the results to a specific repository site then requires only a simple, intuitively acceptable "leap of faith" in assuming that with large expenditures of effort and money, experts can select a site that would be at least as secure as a randomly selected site.     

Combining Probability Distributions From Experts in Risk Analysis

This paper concerns the combination of experts' probability distributions in risk analysis, discussing a variety of combination methods and attempting to highlight the important conceptual and practical issues to be considered in designing a combination process in practice. The role of experts is important because their judgments can provide valuable information, particularly in view of the limited availability of hard data regarding many important uncertainties in risk analysis. Because uncertainties are represented in terms of probability distributions in probabilistic risk analysis (PRA), we consider expert information in terms of probability distributions. The motivation for the use of multiple experts is simply the desire to obtain as much information as possible. Combining experts' probability distributions summarizes the accumulated information for risk analysts and decision-makers. Procedures for combining probability distributions are often compartmentalized as mathematical aggregation methods or behavioral approaches, and we discuss both categories. However, an overall aggregation process could involve both mathematical and behavioral aspects, and no single process is best in all circumstances. An understanding of the pros and cons of different methods and the key issues to consider is valuable in the design of a combination process for a specific PRA. The output, a combined probability distribution, can ideally be viewed as representing a summary of the current state of expert opinion regarding the uncertainty of interest.     

Probabilistic Risk Assessment of Wastes Buried in the Ground

The differences between probabilistic risk assessment (PRA) and safety analysis (SA) are discussed, and it is shown that PRA is more suitable than SA for determining the acceptability of a technology. Since a PRA by the fault tree-event tree analysis method used for reactor safety studies does not seem to be practical for buried waste, an alternative approach is suggested using geochemical analogs. This method is illustrated for the cases of high-level and low-level radioactive waste and for chemical carcinogens released in coal burning.     

External Initiators in Probabilistic Reactor Accident Analysis—Earthquakes, Fires, Floods, Winds

This article discusses the methodologies presently available for analyzing the contribution of "external initiators" to overall risks in the context of PRA (probabilistic risk assessment) of large commercial nuclear power reactors. "External initiators" include earthquakes, fires and floods inside the plant, external floods, high winds, aircraft, barge, and ship collisions, noxious or explosive gases offsite, and so on. These are in contrast to "internal initiators" such as active or passive plant equipment failures, human errors, and loss of electrical power. The ability to consider external initiators within PRA has undergone major advances in recent years. In general, uncertainties associated with the calculated risks from external initiators are much larger than those associated with internal initiators. The principal uncertainties lie with development of hazard curves (such as the frequency of occurrence of an event exceeding a given size: for example, the likelihood of a hurricane with winds exceeding 125 knots). For assessment of earthquakes, internal fires and floods, and high winds, the methodology is reasonably mature for qualitative assessment but not for quantitative application. The risks from other external initiators are generally considered to be low, either because of the very long recurrence time associated with the events or because the plants are judged to be well designed to withstand them.     

Applications of probabilistic risk assessments: the selection of appropriate tools

Probabilistic risk assessment (PRA) is an important methodology for assessing the risks of complex technologies. This paper discusses the strengths and weaknesses of PRA. Its application is explored in three different settings: adversarial policy processes, regulatory/licensing procedures, and plant safety audits. It is concluded that PRA is a valuable tool for auditing safety precautions of existing or planned technologies, especially when it is carried out as an interactive process involving designers and plant personnel who are familiar with actual, everyday operations. PRA has not proven to be as well-suited in providing absolute risk estimates in public-policy debates concerning the acceptability of a technology, or for the licensing and regulatory procedures. The reasons for this are discussed.     

The Repeatability of Uncertainty and Sensitivity Analyses for Complex Probabilistic Risk Assessments

The performance of a probabilistic risk assessment (PRA) for a nuclear power plant is a complex undertaking, involving the assembly of an accident frequency analysis, an accident progression analysis, a source term analysis, and a consequence analysis. Each of these analyses is, in itself, quite complex. Uncertainties enter into a PRA from each of these analyses. An important focus in recent PRAs has been to incorporate these uncertainties at each stage of the analysis, propagate the subsequent uncertainties through the entire analysis, and include uncertainty in the final results. Monte Carlo procedures based on Latin hypercube sampling provide one way to perform propagations of this type. In this paper, the results of two complete and independent Monte Carlo calculations for a recently completed PRA for a nuclear power plant are compared as a means of providing empirical evidence on the repeatability of uncertainty and sensitivity analyses for large-scale PRA calculations. These calculations use the same variables and analysis structure with two independently generated Latin hypercube samples. The results of the two calculations show a high degree of repeatability for the analysis of a very complex system.     

A Comparative Analysis of PRA and Intelligent Adversary Methods for Counterterrorism Risk Management

In counterterrorism risk management decisions, the analyst can choose to represent terrorist decisions as defender uncertainties or as attacker decisions. We perform a comparative analysis of probabilistic risk analysis (PRA) methods including event trees, influence diagrams, Bayesian networks, decision trees, game theory, and combined methods on the same illustrative examples (container screening for radiological materials) to get insights into the significant differences in assumptions and results. A key tenent of PRA and decision analysis is the use of subjective probability to assess the likelihood of possible outcomes. For each technique, we compare the assumptions, probability assessment requirements, risk levels, and potential insights for risk managers. We find that assessing the distribution of potential attacker decisions is a complex judgment task, particularly considering the adaptation of the attacker to defender decisions. Intelligent adversary risk analysis and adversarial risk analysis are extensions of decision analysis and sequential game theory that help to decompose such judgments. These techniques explicitly show the adaptation of the attacker and the resulting shift in risk based on defender decisions.     

A Review of Plant Specific PRAs

A large number of PRA studies have been completed for specific plants at specific sites. From these studies, taken individually or collectively, many significant insights have evolved into items important to risk and safety. The content of this paper is primarily based on the material contained in the EPRI funded review of five PRA studies: Big Rock Point, Zion, Limerick, Grand Gulf, and Arkansas Nuclear One. The first three were the utility sponsored studies publicly available at the time of project initiation while the other two were deemed representative of the NRC's RSSMAP and IREP programs respectively. The results of PRA studies are usually expressed in terms of core melt frequencies, radionuclide release frequencies, and frequencies of occurrence of different reactor accident consequences (e.g., early and latent fatalities) depending on the level of PRA. These subjects are prominently addressed in this paper. One of the results of a PRA study is identification of a relatively small number of accident sequences that represent the dominant contributors to core melt. An analysis of the salient features of the dominant accident sequences from eleven PRA's yielded a characterization of accident sequence categories discussed at some length. Impact of external events is discussed very briefly. Next to an explicit quantification of public risk or core melt frequency, the identification of specific safety concerns and the evaluation of possible solutions to implement risk management are probably the best recognized and most widely used applications of PRA. Several illustrative examples are briefly discussed. Human interactions are extremely important contributors to safety and reliability of the plants. A review of PRA studies concluded that it was necessary to account for five types of human interactions; some of which may mitigate while others may exacerbate an accident sequence.     

A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review

This article presents a discourse on the incorporation of organizational factors into probabilistic risk assessment (PRA)/probabilistic safety assessment (PSA), a topic of debate since the 1980s that has spurred discussions among industry, regulatory agencies, and the research community. The main contributions of this article include (1) identifying the four key open questions associated with this topic; (2) framing ongoing debates by considering differing perspectives around each question; (3) offering a categorical review of existing studies on this topic to justify the selection of each question and to analyze the challenges related to each perspective; and (4) highlighting the directions of research required to reach a final resolution for each question. The four key questions are: (I) How significant is the contribution of organizational factors to accidents and incidents? (II) How critical, with respect to improving risk assessment, is the explicit incorporation of organizational factors into PRA? (III) What theoretical bases are needed for explicit incorporation of organizational factors into PRA? (IV) What methodological bases are needed for the explicit incorporation of organizational factors into PRA? Questions I and II mainly analyze PRA literature from the nuclear domain. For Questions III and IV, a broader review and categorization is conducted of those existing cross-disciplinary studies that have evaluated the effects of organizational factors on safety (not solely PRA-based) to shed more light on future research needs.     

How Probabilistic Risk Assessment Can Mislead Terrorism Risk Analysts

Traditional probabilistic risk assessment (PRA), of the type originally developed for engineered systems, is still proposed for terrorism risk analysis. We show that such PRA applications are unjustified in general. The capacity of terrorists to seek and use information and to actively research different attack options before deciding what to do raises unique features of terrorism risk assessment that are not adequately addressed by conventional PRA for natural and engineered systems—in part because decisions based on such PRA estimates do not adequately hedge against the different probabilities that attackers may eventually act upon. These probabilities may differ from the defender's (even if the defender's experts are thoroughly trained, well calibrated, unbiased probability assessors) because they may be conditioned on different information. We illustrate the fundamental differences between PRA and terrorism risk analysis, and suggest use of robust decision analysis for risk management when attackers may know more about some attack options than we do.     

An Improved Framework for Uncertainty Analysis: Accounting for Unsuspected Errors

I use an analogy with the history of physical measurements, population and energy projections, and analyze the trends in several data sets to quantify the overconfidence of the experts in the reliability of their uncertainty estimates. Data sets include (i) time trends in the sequential measurements of the same physical quantity; (ii) national population projections; and (iii) projections for the U.S., energy sector. Probabilities of large deviations for the true values are parametrized by an exponential distribution with the slope determined by the data. Statistics of past errors can be used in probabilistic risk assessment to hedge against unsuspected uncertainties and to include the possibility of human error into the framework of uncertainty analysis. By means of a sample Monte Carlo simulation of cancer risk caused by ingestion of benzene in soil, I demonstrate how the upper 95th percentiles of risk are changed when unsuspected uncertainties are included. I recommend to inflate the estimated uncertainties by default safety factors determined from the relevant historical data sets.     

Integrating Software into PRA: A Test-Based Approach

Probabilistic risk assessment (PRA) is a methodology to assess the probability of failure or success of a system's operation. PRA has been proved to be a systematic, logical, and comprehensive technique for risk assessment. Software plays an increasing role in modern safety critical systems. A significant number of failures can be attributed to software failures. Unfortunately, current probabilistic risk assessment concentrates on representing the behavior of hardware systems, humans, and their contributions (to a limited extent) to risk but neglects the contributions of software due to a lack of understanding of software failure phenomena. It is thus imperative to consider and model the impact of software to reflect the risk in current and future systems. The objective of our research is to develop a methodology to account for the impact of software on system failure that can be used in the classical PRA analysis process. A test-based approach for integrating software into PRA is discussed in this article. This approach includes identification of software functions to be modeled in the PRA, modeling of the software contributions in the ESD, and fault tree. The approach also introduces the concepts of input tree and output tree and proposes a quantification strategy that uses a software safety testing technique. The method is applied to an example system, PACS.     

Managing Safety‐Related Disruptions: Evidence from the U.S. Nuclear Power Industry

Low‐probability, high‐impact events are difficult to manage. Firms may underinvest in risk assessments for low‐probability, high‐impact events because it is not easy to link the direct and indirect benefits of doing so. Scholarly research on the effectiveness of programs aimed at reducing such events faces the same challenge. In this article, we draw on comprehensive industry‐wide data from the U.S. nuclear power industry to explore the impact of conducting probabilistic risk assessment (PRA) on preventing safety‐related disruptions. We examine this using data from over 25,000 monthly event reports across 101 U.S. nuclear reactors from 1985 to 1998. Using Poisson fixed effects models with time trends, we find that the number of safety‐related disruptions reduced between 8% and 27% per month in periods after operators submitted their PRA in response to the Nuclear Regulatory Commission's Generic Letter 88‐20, which required all operators to conduct a PRA. One possible mechanism for this is that the adoption of PRA may have increased learning rates, lowering the rate of recurring events by 42%. We find that operators that completed their PRA before Generic Letter 88‐20 continued to experience safety improvements during 1990–1995. This suggests that revisiting PRA or conducting it again can be beneficial. Our results suggest that even in a highly safety‐conscious industry as nuclear utilities, a more formal approach to quantifying risk has its benefits.     

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.     

A Nuclear Utility's Views on the Use of Probabilistic Risk Assessment

Probabilistic risk assessment (PRA) is a relatively new tool in the nuclear industry. The Reactor Safety Study started the present trend of conducting PRAs for nuclear power plants when it was published in 1975. Now, nine years later, those in the industry currently using PRA techniques are frequently asked the same question: Why should the nuclear utility industry, with so many accepted analytical tools already available, invest the time and manpower to develop a new technique with so many uncertainties?     

ASME Risk-Based Inservice Inspection and Testing: An Outlook to the Future

Significant research work has been completed in the development of risk-based inservice inspection (ISI) and testing (IST) technology for nuclear power plant applications through the ASME Center For Research and Technology Development. This paper provides technology that has been developed for these engineering applications. The technology includes risk-based ranking methods, beginning with the use of plant probabilistic risk assessment (PRA), for the determination of risk-significant and less risk-significant components for inspection and the determination of similar populations for pumps and valves for inservice testing. Decision analysis methods are outlined for developing ISI and IST programs. This methodology integrates nondestructive examination data, structural reliability/risk assessment results, PRA results, failure data, and expert opinion to evaluate the effectiveness of ISI programs. Similarly, decision analysis uses the output of failure mode and causes analysis in combination with data, expert opinion, and PRA results to evaluate the effectiveness of IST programs. Results of pilot applications of these ASME methods to actual nuclear plant systems and components are summarized. The results of this work are already being used to develop recommended changes in ISI and IST requirements by the ASME Section XI and the ASME Operation and Maintenance Code organizations. A perspective on Code and regulatory adoption is also outlined. Finally, the potential benefits to the nuclear industry in terms of safety, person-rem exposure, and costs are summarized.     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Anesthesia Patient Risk: A Quantitative Approach to Organizational Factors and Risk Management Options

The risk of death or brain damage to anesthesia patients is relatively low, particularly for healthy patients in modern hospitals. When an accident does occur, its cause is usually an error made by the anesthesiologist, either in triggering the accident sequence, or failing to take timely corrective measures. This paper presents a pilot study which explores the feasibility of extending probabilistic risk analysis (PRA) of anesthesia accidents to assess the effects of human and management components on the patient risk. We develop first a classic PRA model for the patient risk per operation. We then link the probabilities of the different accident types to their root causes using a probabilistic analysis of the performance shaping factors. These factors are described here as the "state of the anesthesiologist" characterized both in terms of alertness and competence. We then analyze the effects of different management factors that affect the state of the anesthesiologist and we compute the risk reduction benefits of several risk management policies. Our data sources include the published version of the Australian Incident Monitoring Study as well as expert opinions. We conclude that patient risk could be reduced substantially by closer supervision of residents, the use of anesthesia simulators both in training and for periodic recertification, and regular medical examinations for all anesthesiologists.     

Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model

The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on risk analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic risk analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard risk analysis with intelligent adversary risk analysis, describes the intelligent adversary risk analysis challenges, and presents a probabilistic defender–attacker–defender model to evaluate the baseline risk and the potential risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The risk management model is demonstrated with an illustrative bioterrorism problem with notional data.