Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks

In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow‐tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures.     

Planning Replacement of Natural Gas Distribution Systems Under Constraints on Acceptable Risk from Explosions

Natural gas distribution systems in the United States were developed primarily in the first half of this century, utilizing materials such as cast iron and then steel. Over time, cast iron and steel pipe sections became weak from corrosion and are subject to failure which in turn can lead to explosions and possible injury and loss of life. Gas utilities maintain system integrity through repair-replacement programs where pipe sections are prioritized for replacement in any given year through cost-benefit analysis; however, the total annual amount to be budgeted for replacement is left to engineering judgment. This approach has left some utilities vulnerable to criticism that their current replacement rate on cast iron pipe is not great enough and that public safety is being compromised. This paper addresses the problem situation by formulating a linear programming replacement decision model which augments cost-benefit analysis with explicit constraints on acceptable risk to human life from fire/explosion. The model is illustrated for a hypothetical utility.     

Cost‐Benefit Analysis of Passive Fire Protections in Road LPG Transportation

The cost‐benefit evaluation of passive fire protection adoption in the road transport of liquefied petroleum gas (LPG) was investigated. In a previous study, mathematical simulations of real scale fire scenarios proved the effectiveness of passive fire protections in preventing the “fired” boiling liquid expanding vapor explosion (BLEVE), thus providing a significant risk reduction. In the present study the economical aspects of the adoption of fire protections are analyzed and an approach to cost‐benefit analysis (CBA) is proposed. The CBA model is based on the comparison of the risk reduction due to fire protections (expressed in monetary terms by the value of a statistical life) and the cost of the application of fire protections to a fleet of tankers. Different types of fire protections were considered, as well as the possibility to apply protections to the entire fleet or only to a part of it. The application of the proposed model to a real‐life case study is presented and discussed. Results demonstrate that the adoption of passive fire protections on road tankers, though not compulsory in Europe, can be economically feasible, thus representing a concrete measure to achieve control of the “major hazard accidents” cited by the European legislation.     

A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents

Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation‐based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source‐to‐source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.     

Lean Six Sigma implementation: modelling the interaction among the enablers

Abstract

Lean Six Sigma (LSS) methodology has been acquiring a prominent position in organisations. The aim of this study is to demonstrate an approach to LSS implementation in organisations using the development of a hypothetical model based on interpretive structural modelling (ISM) and fuzzy Matriced Impacts Croisés Multiplication Appliquée á un Classement (fuzzy MICMAC) analysis phenomenon. Seventy Lean Six Sigma enablers (LSSEs) have been identified through extensive literature review and out of which 40 most important LSSEs were finalised through opinions of experts both from industry and academia. Furthermore, the valuable expert opinions have been applied to determine contextual relationships between these significant LSSEs and a hierarchical model has been created based on an ISM. The fuzzy MICMAC analysis has also been utilised to classify the enablers based on the dependence and driving power, and validate the created ISM-based model. The developed hierarchical model will assist to understand interrelationships and interdependencies among the identified LSSEs. Having high driving and low dependence power, the LSSEs have strategic significance because of their driving character. On the other hand, having high dependence and low driving power, LSSEs are more performance orientated. The mutual influence, driving and dependence power of LSSEs render valuable information to top management to distinguish between independent and dependent LSSEs. An organisation desiring of adopting LSS may get benefited by the understanding of LSSEs and their interactions.     

Bayesian Data Analysis of Severe Fatal Accident Risk in the Oil Chain

We analyze the risk of severe fatal accidents causing five or more fatalities and for nine different activities covering the entire oil chain. Included are exploration and extraction, transport by different modes, refining and final end use in power plants, heating or gas stations. The risks are quantified separately for OECD and non‐OECD countries and trends are calculated. Risk is analyzed by employing a Bayesian hierarchical model yielding analytical functions for both frequency (Poisson) and severity distributions (Generalized Pareto) as well as frequency trends. This approach addresses a key problem in risk estimation—namely the scarcity of data resulting in high uncertainties in particular for the risk of extreme events, where the risk is extrapolated beyond the historically most severe accidents. Bayesian data analysis allows the pooling of information from different data sets covering, for example, the different stages of the energy chains or different modes of transportation. In addition, it also inherently delivers a measure of uncertainty. This approach provides a framework, which comprehensively covers risk throughout the oil chain, allowing the allocation of risk in sustainability assessments. It also permits the progressive addition of new data to refine the risk estimates. Frequency, severity, and trends show substantial differences between the activities, emphasizing the need for detailed risk analysis.     

A cross-validation of a structural equation model of accidents: organizational and psychological variables as predictors of work safety

A study to establish a structural equation model of accidents is presented. The eight variables included in the model were those associated with accidents in previous research. The reliability of questionnaires to measure those variables and a structural equation model relating them are provided. The model explaining the occurrence of accidents was cross-validated through three Spanish samples (n=123; 182; 124).The model presented a good fit to data in two samples, while a third had unclear fit indices. Therefore, most relationships among variables did not vary among these three samples. The role of the safety variables in the model is discussed. The discussion is also focused on risk measurement difficulties commonly reported in the literature. Finally, some suggestions for future safety research with structural equation modelling techniques are provided.     

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

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

An Integrated Risk Assessment Model of Township‐Scaled Land Subsidence Based on an Evidential Reasoning Algorithm and Fuzzy Set Theory

Land subsidence risk assessment (LSRA) is a multi‐attribute decision analysis (MADA) problem and is often characterized by both quantitative and qualitative attributes with various types of uncertainty. Therefore, the problem needs to be modeled and analyzed using methods that can handle uncertainty. In this article, we propose an integrated assessment model based on the evidential reasoning (ER) algorithm and fuzzy set theory. The assessment model is structured as a hierarchical framework that regards land subsidence risk as a composite of two key factors: hazard and vulnerability. These factors can be described by a set of basic indicators defined by assessment grades with attributes for transforming both numerical data and subjective judgments into a belief structure. The factor‐level attributes of hazard and vulnerability are combined using the ER algorithm, which is based on the information from a belief structure calculated by the Dempster‐Shafer (D‐S) theory, and a distributed fuzzy belief structure calculated by fuzzy set theory. The results from the combined algorithms yield distributed assessment grade matrices. The application of the model to the Xixi‐Chengnan area, China, illustrates its usefulness and validity for LSRA. The model utilizes a combination of all types of evidence, including all assessment information—quantitative or qualitative, complete or incomplete, and precise or imprecise—to provide assessment grades that define risk assessment on the basis of hazard and vulnerability. The results will enable risk managers to apply different risk prevention measures and mitigation planning based on the calculated risk states.     

Risk Analysis of Finnish Peacekeeping in Kosovo

The research team interviewed over 90 Finnish battalion members in Kosovo, visited 22 units or posts, registered its observations, and made any necessary measurements. Key persons were asked to list the most important risks for occupational safety and health in their area of responsibility. Altogether, 106 accidents and 40 cases of disease resulted in compensation claims in 2000. The risks to the peacekeeping force were about twice those of the permanent staff of military trainees in Finland. Altogether, 21 accidents or cases of disease resulted in sick leave for at least 3 months after service. One permanent injury resulted from an explosion. Biological, chemical, and physical factors caused 8 to 9 occupational illnesses each. Traffic accidents, operational factors, and munitions and mines were evaluated to be the three most important risk factors, followed by occupational hygiene, living conditions (mold, fungi, dust), and general hygiene. Possible fatal risks, such as traffic accidents and munitions and explosives, received a high ranking in both the subjective and the objective evaluations. One permanent injury resulted from an explosion, and two traffic accidents involved a fatality, although not of a peacekeeper. The reduction of sports and military training accidents, risk-control programs, and, for some tasks, better personal protection is considered a development challenge for the near future.     

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.     

Managing the Fukushima Challenge

The Fukushima Daiichi accident raises a fundamental question: Can science and technology prevent the inevitability of serious accidents, especially those with low probabilities and high consequences? This question reminds us of a longstanding challenge with the trans‐sciences, originally addressed by Alvin Weinberg well before the Three Mile Island and Chernobyl accidents. This article, revisiting Weinberg's issue, aims at gaining insights from the accident with a special emphasis on the sociotechnical or human behavioral aspects lying behind the accident's causes. In particular, an innovative method for managing the challenge is explored referring to behavioral science approaches to a decision‐making process on risk management; such as managing human behavioral risks with information asymmetry, seeking a rational consensus with communicative action, and pursuing procedural rationality through interactions with the outer environment. In short, this article describes the emerging need for Japan to transform its national safety management institutions so that these might be based on interactive communication with parties inside and outside Japan.     

Energy Choices and Risk Beliefs: Is It Just Global Warming and Fear of a Nuclear Power Plant Accident?

A survey of 3,200 U.S. residents focused on two issues associated with the use of nuclear and coal fuels to produce electrical energy. The first was the association between risk beliefs and preferences for coal and nuclear energy. As expected, concern about nuclear power plant accidents led to decreased support for nuclear power, and those who believed that coal causes global warming preferred less coal use. Yet other risk beliefs about the coal and nuclear energy fuel cycles were stronger or equal correlates of public preferences. The second issue is the existence of what we call acknowledged risk takers, respondents who favored increased reliance on nuclear energy, although also noting that there could be a serious nuclear plant accident, and those who favored greater coal use, despite acknowledging a link to global warming. The pro‐nuclear group disproportionately was affluent educated white males, and the pro‐coal group was relatively poor less educated African‐American and Latino females. Yet both shared four similarities: older age, trust in management, belief that the energy facilities help the local economy, and individualistic personal values. These findings show that there is no single public with regard to energy preferences and risk beliefs. Rather, there are multiple populations with different viewpoints that surely would benefit by hearing a clear and comprehensive national energy life cycle policy from the national government.     

An Ecological Model for Quantitative Risk Assessment for Schistosomiasis: The Case of a Patchy Environment in the Coastal Tropical Area of Northeastern Brazil

We developed a stochastic model for quantitative risk assessment for the Schistosoma mansoni (SM) parasite, which causes an endemic disease of public concern. The model provides answers in a useful format for public health decisions, uses data and expert opinion, and can be applied to any landscape where the snail Biomphalaria glabrata is the main intermediate host (South and Central America, the Caribbean, and Africa). It incorporates several realistic and case‐specific features: stage‐structured parasite populations, periodic praziquantel (PZQ) drug treatment for humans, density dependence, extreme events (prolonged rainfall), site‐specific sanitation quality, environmental stochasticity, monthly rainfall variation, uncertainty in parameters, and spatial dynamics. We parameterize the model through a real‐world application in the district of Porto de Galinhas (PG), one of the main touristic destinations in Brazil, where previous studies identified four parasite populations within the metapopulation. The results provide a good approximation of the dynamics of the system and are in agreement with our field observations, i.e., the lack of basic infrastructure (sanitation level and health programs) makes PG a suitable habitat for the persistence and growth of a parasite metapopulation. We quantify the risk of SM metapopulation explosion and quasi‐extinction and the time to metapopulation explosion and quasi‐extinction. We evaluate the sensitivity of the results under varying scenarios of future periodic PZQ treatment (based on the Brazilian Ministry of Health's plan) and sanitation quality. We conclude that the plan might be useful to slow SM metapopulation growth but not to control it. Additional investments in better sanitation are necessary.     

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.     

Incremental Sampling Methodology: Applications for Background Screening Assessments

This article presents the findings from a numerical simulation study that was conducted to evaluate the performance of alternative statistical analysis methods for background screening assessments when data sets are generated with incremental sampling methods (ISMs). A wide range of background and site conditions are represented in order to test different ISM sampling designs. Both hypothesis tests and upper tolerance limit (UTL) screening methods were implemented following U.S. Environmental Protection Agency (USEPA) guidance for specifying error rates. The simulations show that hypothesis testing using two‐sample t ‐tests can meet standard performance criteria under a wide range of conditions, even with relatively small sample sizes. Key factors that affect the performance include unequal population variances and small absolute differences in population means. UTL methods are generally not recommended due to conceptual limitations in the technique when applied to ISM data sets from single decision units and due to insufficient power given standard statistical sample sizes from ISM.     

Risk Assessment of Human Toxoplasmosis Associated with the Consumption of Pork Meat in Italy

Toxoplasmosis is a cosmopolitan disease and has a broad range of hosts, including humans and several wild and domestic animals. The human infection is mostly acquired through the consumption of contaminated food and pork meat has been recognized as one of the major sources of transmission. There are, however, certain fundamental differences between countries; therefore, the present study specifically aims to evaluate the exposure of the Italian population to Toxoplasma gondii through the ingestion of several types of pork meat products habitually consumed in Italy and to estimate the annual number of human infections within two subgroups of the population. A quantitative risk assessment model was built for this reason and was enriched with new elements in comparison to other similar risk assessments in order to enhance its accuracy. Sensitivity analysis and two alternative scenarios were implemented to identify the factors that have the highest impact on risk and to simulate different plausible conditions, respectively. The estimated overall average number of new infections per year among adults is 12,513 and 92 for pregnant women. The baseline model showed that almost all these infections are associated with the consumption of fresh meat cuts and preparations (mean risk of infection varied between 4.5 × 10⁻⁵ and 5.5 × 10⁻⁵) and only a small percentage is due to fermented sausages/salami. On the contrary, salt‐cured meat products seem to pose minor risk but further investigations are needed to clarify still unclear aspects. Among all the considered variables, cooking temperature and bradyzoites’ concentration in muscle impacted most the risk.     

Assessment and Management of Risk in the Transport of Dangerous Materials: The Case of Chlorine Transport in France

The purpose of this paper is to provide the main results of a study concerning the risk of chlorine transport by train in France. The specific problem of chlorine transport is presented in the framework of a general model for assessing the risk in the transport of dangerous materials. The probability of accidents followed with a chlorine release involving fatalities are put in perspective with other risks having potential health effects on the public. Two types of application of the model are envisaged in relation to the management of risk: the selection of protective measures through a cost-effectiveness approach and the use of the model for a better planning of decisions in an accident situation.     

Who by Accident? The Social Morphology of Car Accidents

Prior studies in the sociology of accidents have shown that different social groups have different rates of accident involvement. This study extends those studies by implementing Bourdieu's relational perspective of social space to systematically explore the homology between drivers’ social characteristics and their involvement in specific types of motor vehicle accident. Using a large database that merges official Israeli road‐accident records with socioeconomic data from two censuses, this research maps the social order of road accidents through multiple correspondence analysis. Extending prior studies, the results show that different social groups indeed tend to be involved in motor vehicle accidents of different types and severity. For example, we find that drivers from low socioeconomic backgrounds are overinvolved in severe accidents with fatal outcomes. The new findings reported here shed light on the social regularity of road accidents and expose new facets in the social organization of death.     

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.