DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation

Historical data analysis shows that escalation accidents, so‐called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent‐based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent‐based modeling technique explains the domino effects from a bottom‐up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher‐level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large‐scale complicated cases.     

Risk Management of Domino Effects Considering Dynamic Consequence Analysis

Domino effects are low‐probability high‐consequence accidents causing severe damage to humans, process plants, and the environment. Because domino effects affect large areas and are difficult to control, preventive safety measures have been given priority over mitigative measures. As a result, safety distances and safety inventories have been used as preventive safety measures to reduce the escalation probability of domino effects. However, these safety measures are usually designed considering static accident scenarios. In this study, we show that compared to a static worst‐case accident analysis, a dynamic consequence analysis provides a more rational approach for risk assessment and management of domino effects. This study also presents the application of Bayesian networks and conflict analysis to risk‐based allocation of chemical inventories to minimize the consequences and thus to reduce the escalation probability. It emphasizes the risk management of chemical inventories as an inherent safety measure, particularly in existing process plants where the applicability of other safety measures such as safety distances is limited.     

Cellular Automata‐Based Systematic Risk Analysis Approach for Emergency Response

Emergency response is directly related to the allocation of emergency rescue resources. Efficient emergency response can reduce loss of life and property, limit damage from the primary impact, and minimize damage from derivative impacts. An appropriate risk analysis approach in the event of accidents is one rational way to assist emergency response. In this article, a cellular automata‐based systematic approach for conducting risk analysis in emergency response is presented. Three general rules, i.e., diffusive effect, transporting effect, and dissipative effect, are developed to implement cellular automata transition function. The approach takes multiple social factors such as population density and population sensitivity into consideration and it also considers risk of domino accidents that are increasing due to increasing congestion in industrial complexes of a city and increasing density of human population. In addition, two risk indices, i.e., individual risk and aggregated weighted risk, are proposed to assist decision making for emergency managers during emergency response. Individual risk can be useful to plan evacuation strategies, while aggregated weighted risk can help emergency managers to allocate rescue resources rationally according to the degree of danger in each vulnerable area and optimize emergency response programs.     

Risk assessment for handling hazardous substances within the European industry: Available methodologies and research streams

After the Seveso disaster occurred more than 40 years ago, there has been an increasing awareness of the potential impacts that similar accident events can occur in a wide range of process establishments, where the handling and production of hazardous substances pose a real threat to society and the environment. In these industrial sites denominated “Seveso sites,” the urgent need for an effective strategy emerged markedly to handle hazardous activities and to ensure safe conditions. Since then, the main challenging research issues have focused on how to prevent such accident events and how to mitigate their consequences leading to the development of many risk assessment methodologies. In recent years, researchers and practitioners have tried to provide useful overviews of the existing risk assessment methodologies proposing several reviews. However, these reviews are not exhaustive because they are either dated or focus only on one specific topic (e.g., liquefied natural gas, domino effect, etc.). This work aims to overcome the limitations of the current reviews by providing an up-to-date and comprehensive overview of the risk assessment methodologies for handling hazardous substances within the European industry. In particular, we have focused on the current techniques for hazards and accident scenarios identification, as well as probability and consequence analyses for both onshore and offshore installations. Thus, we have identified the research streams that have characterized the activities of researchers and practitioners over the years, and we have then presented and discussed the different risk assessment methodologies available concerning the research stream that they belong to.     

Domino Effect Analysis Using Bayesian Networks

A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier‐studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility.     

Application of Graph Theory to Cost‐Effective Fire Protection of Chemical Plants During Domino Effects

In the present study, we have introduced a methodology based on graph theory and multicriteria decision analysis for cost‐effective fire protection of chemical plants subject to fire‐induced domino effects. By modeling domino effects in chemical plants as a directed graph, the graph centrality measures such as out‐closeness and betweenness scores can be used to identify the installations playing a key role in initiating and propagating potential domino effects. It is demonstrated that active fire protection of installations with the highest out‐closeness score and passive fire protection of installations with the highest betweenness score are the most effective strategies for reducing the vulnerability of chemical plants to fire‐induced domino effects. We have employed a dynamic graph analysis to investigate the impact of both the availability and the degradation of fire protection measures over time on the vulnerability of chemical plants. The results obtained from the graph analysis can further be prioritized using multicriteria decision analysis techniques such as the method of reference point to find the most cost‐effective fire protection strategy.     

Lessons learned from Toulouse and Buncefield disasters: from risk analysis failures to the identification of atypical scenarios through a better knowledge management

The recent occurrence of severe major accidents has brought to light flaws and limitations of hazard identification (HAZID) processes performed for safety reports, as in the accidents at Toulouse (France) and Buncefield (UK), where the accident scenarios that occurred were not captured by HAZID techniques. This study focuses on this type of atypical accident scenario deviating from normal expectations. The main purpose is to analyze the examples of atypical accidents mentioned and to attempt to identify them through the application of a well-known methodology such as the bow-tie analysis. To these aims, the concept of atypical event is accurately defined. Early warnings, causes, consequences, and occurrence mechanisms of the specific events are widely studied and general failures of risk assessment, management, and governance isolated. These activities contribute to outline a set of targeted recommendations, addressing transversal common deficiencies and also demonstrating how a better management of knowledge from the study of past events can support future risk assessment processes in the identification of atypical accident scenarios. Thus, a new methodology is not suggested; rather, a specific approach coordinating a more effective use of experience and available information is described, to suggest that lessons to be learned from past accidents can be effectively translated into actions of prevention.     

Benchmarking Safety Climate in Hazardous Environments: A Longitudinal, Interorganizational Approach

Safety climate is an important element of organizational reliability. This study applied benchmarking strategies for monitoring safety climate across nine North Sea oil and gas installations that were surveyed in consecutive years. Examination of absolute changes in safety climate complemented the benchmarking approach. Discriminant function analyses (DFA) identified the elements of safety climate predictive of self-reported accidents; correlational analyses were applied to the scale scores and accident proportions across the year period. Absolute improvements were substantial, with safety climate profiles converging in the second year. Large relative improvements were also observed. DFA highlighted perceived management commitment to safety and willingness to report accidents as significant predictors of personal accident involvement. Changes in perceived management commitment to safety were closely associated with changes in safety behavior.     

Do Temporary Contracts Increase Work Accidents? A Microeconometric Comparison between Italy and Spain

Abstract. This paper analyses the effect of contract type on the rate of work‐related accidents in Italy and Spain, using the 1999 Labour Force Survey ‘ad hoc module’. We find that, once personal and job characteristics of workers are controlled for, the differences in the probability of suffering a work accident between open‐ended and temporary workers vanish. Furthermore, following a novel decomposition analysis by Yun, we obtain that personal and job characteristics tend to increase the probability of having an accident for temporary workers, but the specific influence of contract type favours the latter, who show a lower probability on this account.     

Coordinability and Consistency in Accident Causation and Prevention: Formal System Theoretic Concepts for Safety in Multilevel Systems

Although a “system approach” to accidents in sociotechnical systems has been frequently advocated, formal system theoretic concepts remain absent in the literature on accident analysis and system safety. To address this gap, we introduce the notions of coordinability and consistency from the hierarchical and multilevel systems theory literature. We then investigate the applicability and the importance of these concepts to accident causation and safety. Using illustrative examples, including the worst disaster in aviation history, and recent incidents in the United States of aircraft clipping each other on the tarmac, we propose that the lack of coordinability is a fundamental failure mechanism causing or contributing to accidents in multilevel systems. We make a similar case for the lack of consistency. Coordinability and consistency become ingredients for accident prevention, and their absence fundamental failure mechanisms that can lead to system accidents. Finally, using the concepts introduced in this work, we identify several venues for further research, including the development of a theory of coordination in multilevel systems, the investigation of potential synergies between coordinability, consistency, and the high reliability organizations paradigm, and the possibility of reframing the view that “sloppy management is the root cause of many industrial accidents” as one of lack of coordinability and/or consistency between management and operations. By introducing and expanding on the concepts of coordinability and consistency, we hope to contribute to the thinking about, and the to language of, accident causation, and prevention and to add to the intellectual toolkit of safety professionals and academics.     

A Unified Approach for Calculating Core Melt Frequency Caused by Internal and External Initiating Events

A unified approach for calculating the core melt frequency of a specific reactor caused by both internal and external accident initiators is demonstrated. Two classes of internal initiators are examined: transients, of which turbine trip is the chosen example, and loss-of-coolant events of various sizes. The concepts of hazard and fragility analysis first proposed for seismic risk analysis are linked to the frequencies of internal initiating events, and to the plant response as a function of the event intensity. Uncertainties are propagated using discrete probability distribution (DPD) arithmetic. Advantages of this approach include mathematical and conceptual consistency, and an improved uncertainty analysis, which are important considerations if risk studies are to be utilized in decision-making based on quantitative safety goals.     

Limitations on the Usefulness of Risk Assessment

Quantitative risk assessment (QRA) is now regarded as an essential component in the analysis of risks arising from installations classified as major hazards. The purpose of this paper is to discuss the value of the results in decision-making in practical situations. The use made of QRA in three contrasting cases which came to extensive public attention in the U.K. is examined. The first concerned an extension of domestic development near a chemical factory; the second an extension to a large petrochemical complex; and the third to a proposal to build a pressurized water reactor. The two public inquiries concerned with the chemical industry accepted standards of individual risk which were comparable to the risks from everyday accidents; the evidence of societal risk that could arise from major accidents at the petrochemical complex was compared with that of a local natural hazard — flooding. Higher standards of individual safety were set in the inquiry into the PWR proposal, and the definition of societal risk was debated at length. The QRA results were analyzed to show that risks arising from accidents were lower than those from normal operations, but they were used explicitly as a check on the overall safety of the design and of the operational and licensing organization. Such qualitative examination will always be required in addition to QRA. All these inquiries were faced with considerable technical argument. There is a need for the full details of risk calculations to be clear. The usefulness of QRA as an input to decision-making would be much enhanced if the technical points at issue could be clarified outside a formal public inquiry. In addition, there are some technical questions which apply to many installations. There should be better mechanisms of technical debate to achieve a measure of agreement on the optimum methods of calculation in these cases, and some possibilities are explored.     

Probabilistic Analysis of Dam Accidents Worldwide: Risk Assessment for Dams of Different Purposes in OECD and Non-OECD Countries with Focus on Time Trend Analysis

This study presents probabilistic analysis of dam accidents worldwide in the period 1911–2016. The accidents are classified by the dam purpose and by the country cluster, where they occurred, distinguishing between the countries of the Organization for Economic Cooperation and Development (OECD) and nonmember countries (non-OECD without China). A Bayesian hierarchical approach is used to model distributions of frequency and severity for accidents. This approach treats accident data as a multilevel system with subsets sharing specific characteristics. To model accident probabilities for a particular dam characteristic, this approach samples data from the entire data set, borrowing the strength across data set and enabling to model distributions even for subsets with scarce data. The modelled frequencies and severities are combined in frequency-consequence curves, showing that accidents for all dam purposes are more frequent in non-OECD (without China) and their maximum consequences are larger than in OECD countries. Multipurpose dams also have higher frequencies and maximum consequences than single-purpose dams. In addition, the developed methodology explicitly models time dependence to identify trends in accident frequencies over the analyzed period. Downward trends are found for almost all dam purposes confirming that technological development and implementation of safety measures are likely to have a positive impact on dam safety. The results of the analysis provide insights for dam risk management and decision-making processes by identifying key risk factors related to country groups and dam purposes as well as changes over time.     

Human and organizational factors in offshore safety

The role of human and organizational factors in predicting accidents and incidents has become of major interest to the UK offshore oil and gas industry. Some of these factors had been measured in an earlier study focusing on the role of risk perception in determining accident involvement. The current study sought to extend the methodology by focusing on perceptions of organizational factors that could have an impact on safety. A self-report questionnaire was developed and distributed to 11 installations operating on the UK Continental Shelf. A total of 722 were returned (33% response rate) from a representative sample of the offshore workforce on these installations. The study investigated the underlying structure and content of offshore employees' attitudes to safety, feelings of safety and satisfaction with safety measures. Correlations and step-wise regression analysis were used to test the relationships between measures. The results suggest that 'unsafe' behaviour is the 'best' predictor of accidents/near misses as measured by self-report data and that unsafe behaviour is, in turn, driven by perceptions of pressure for production.     

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.     

Risk Prevention and Policy-Making in Automatic Systems

Accidents with automatic production systems are reported to be on the order of one in a hundred or thousand robot-years, while fatal accidents are found to occur one or two orders of magnitude less frequently. Traditions in occupational safety tend to seek for safety targets in terms of zero severe accidents for automatic systems. Decision-making requires a risk assessment balancing potential risk reduction measures and costs within the cultural environment of a production company. This paper presents a simplified procedure which acts as a decision tool. The procedure is based on a risk concept approaching prevention both in a deterministic and in a probabilistic manner. Eight accident scenarios are shown to represent the potential accident processes involving robot interactions with people. Seven prevention policies are shown to cover the accident scenarios in principle. An additional probabilistic approach may indicate which extra safety measures can be taken against what risk reduction and additional costs. The risk evaluation process aims at achieving a quantitative acceptable risk level. For that purpose, three risk evaluation methods are discussed with respect to reaching broad consensus on the safety targets.     

Human and organizational factors in offshore safety

The role of human and organizational factors in predicting accidents and incidents has become of major interest to the UK offshore oil and gas industry. Some of these factors had been measured in an earlier study focusing on the role of risk perception in determining accident involvement. The current study sought to extend the methodology by focusing on perceptions of organizational factors that could have an impact on safety. A self-report questionnaire was developed and distributed to 11 installations operating on the UK Continental Shelf. A total of 722 were returned (33% response rate) from a representative sample of the offshore workforce on these installations. The study investigated the underlying structure and content of offshore employees' attitudes to safety, feelings of safety and satisfaction with safety measures. Correlations and step-wise regression analysis were used to test the relationships between measures. The results suggest that 'unsafe' behaviour is the 'best' predictor of accidents/near misses as measured by self-report data and that unsafe behaviour is, in turn, driven by perceptions of pressure for production.     

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.