Fuzzy‐Logic‐Based Safety Verification Framework for Nuclear Power Plants

This article presents a practical implementation of a safety verification framework for nuclear power plants (NPPs) based on fuzzy logic where hazard scenarios are identified in view of safety and control limits in different plant process values. Risk is estimated quantitatively and compared with safety limits in real time so that safety verification can be achieved. Fuzzy logic is used to define safety rules that map hazard condition with required safety protection in view of risk estimate. Case studies are analyzed from NPP to realize the proposed real‐time safety verification framework. An automated system is developed to demonstrate the safety limit for different hazard scenarios.     

The Implementation and Maintenance of a Behavioral Safety Process in a Petroleum Refinery

A values-centered and team-based behavioral safety process was implemented in a petroleum oil refinery. Employee teams defined the refinery's safety values and related practices, which were used to guide the process design and implementation. The process included (a) a safety assessment; (b) the clarification of safety-related values and related practices; (c) process design and executive overview; (d) safety-observation training; (e) feedback, recognition, and celebration; and (f) process maintenance. Data that span 20 years are presented to show the long-term impact on recordable incidents, lost-time cases, and direct costs of injuries. The implementation was associated with an 81% decrease in recordable incidents, a 79% decrease in lost-time cases, and a 97% savings in annual workers' compensation costs over an eight-year period. An interview with the vice president of the refinery is presented to highlight the process's influence on the refinery's safety culture.     

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.     

Risk‐Based Sampling: I Don't Want to Weight in Vain

Recently, there has been considerable interest in developing risk‐based sampling for food safety and animal and plant health for efficient allocation of inspection and surveillance resources. The problem of risk‐based sampling allocation presents a challenge similar to financial portfolio analysis. Markowitz (1952) laid the foundation for modern portfolio theory based on mean‐variance optimization. However, a persistent challenge in implementing portfolio optimization is the problem of estimation error, leading to false “optimal” portfolios and unstable asset weights. In some cases, portfolio diversification based on simple heuristics (e.g., equal allocation) has better out‐of‐sample performance than complex portfolio optimization methods due to estimation uncertainty. Even for portfolios with a modest number of assets, the estimation window required for true optimization may imply an implausibly long stationary period. The implications for risk‐based sampling are illustrated by a simple simulation model of lot inspection for a small, heterogeneous group of producers.     

Measuring impact of Lean implementation on construction safety performance: a structural equation model

Abstract

The construction industry is a risky endeavour. Therefore, it is essential to develop significant safety measures for lowering risks and ensuring safety during projects. However, current safety applications are yet to be developed given the increasing number of workplace accidents. Among these, Lean applications have the potential to improve safety standards and enhance safety performance. Within this context, this study develops components for Lean implementation and construction safety performance, and it presents a conceptual framework to study the relationship between them. A questionnaire was designed and given to Lean practitioners to quantify the impact of the components. Structural Equation Modelling was used to analyse the framework and validate the relationship between Lean implementation and safety performance. The results of the study indicate that there is a significant relationship between Lean implementation and safety performance. The findings also imply that firms that perform well in implementing Lean practices achieve better safety performance. The primary contribution of this research is to prove that Lean practices are closely related to safety performance, with the aim of encouraging construction practitioners to work in a safer environment by using well-established Lean practices. Moreover, the study aims to expand the use of Lean practices to achieve better safety performance by revealing the strong ties between safety and Lean. Finally, this study can guide construction practitioners to adopt Lean techniques and apply them in their projects to enhance safety performance.     

Disentangling the Governance Configurations of Strategic Spatial Plan-Making in European Urban Regions

This paper is aimed at assessing and disentangling how territorial governance configurations influence contemporary practices in strategic plan-making in 14 European urban regions. The findings allow us to distinguish three dominant practices: i) plan-making shaped by consensus building and multilevel government cooperation; ii) plan-making shaped by the involvement of profit-oriented actors and negotiations, and iii) plan-making shaped by consolidated planning practice. To truly grasp plan-making practice, it is necessary to scrutinize who rules and who is ruled while negotiations are unfolding, as well as the extent to which private actors’ interests influence how spatial development goals and strategies are defined.     

基于贝叶斯网络的航空安全中不安全信息分析

针对航空安全中收集到的不安全信息,本文将安全检查发现的问题进行了分类,建立了民航安全检查的故障树模型,同时利用故障树模型进行了最小割集、结构重要度的定性分析。然后利用贝叶斯网络的学习能力结合故障树模型最终构建了民航安全检查贝叶斯网络模型,基于某航空公司安全检查发现问题的数据并利用贝叶斯网络的双向推理计算得到各影响因素对不安全事件的影响程度和不安全事件发生时各影响因素的重要程度。基于民航安全检查贝叶斯网络模型分析,本文得出个体原因、设施不完善、信息沟通因素最易导致工作流程管理中发现问题;管理流程、知识技能不足最易导致人员管理中发现问题;个体原因、培训、设备故障最易导致设备管理中发现问题。     

Sensitivity Analysis for Critical Control Points Determination and Uncertainty Analysis to Link FSO and Process Criteria: Application to Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

Microbiological food safety is an important economic and health issue in the context of globalization and presents food business operators with new challenges in providing safe foods. The hazard analysis and critical control point approach involve identifying the main steps in food processing and the physical and chemical parameters that have an impact on the safety of foods. In the risk‐based approach, as defined in the Codex Alimentarius, controlling these parameters in such a way that the final products meet a food safety objective (FSO), fixed by the competent authorities, is a big challenge and of great interest to the food business operators. Process risk models, issued from the quantitative microbiological risk assessment framework, provide useful tools in this respect. We propose a methodology, called multivariate factor mapping (MFM), for establishing a link between process parameters and compliance with a FSO. For a stochastic and dynamic process risk model of in soft cheese made from pasteurized milk with many uncertain inputs, multivariate sensitivity analysis and MFM are combined to (i) identify the critical control points (CCPs) for throughout the food chain and (ii) compute the critical limits of the most influential process parameters, located at the CCPs, with regard to the specific process implemented in the model. Due to certain forms of interaction among parameters, the results show some new possibilities for the management of microbiological hazards when a FSO is specified.     

Risk-Based Objectives for the Allocation of Chemical, Biological, and Radiological Air Emissions Sensors

This article addresses the problem of allocating devices for localized hazard protection across a region. Each identical device provides only local protection, and the devices serve localities that are exposed to nonidentical intensities of hazard. A method for seeking the optimal allocation Policy Decisions is described, highlighting the potentially competing objectives of maximizing local risk reductions and coverage risk reductions. The metric for local risk reductions is the sum of the local economic risks avoided. The metric for coverage risk reductions is adapted from the p-median problem and equal to the sum of squares of the distances from all unserved localities to their closest associated served locality. Three graphical techniques for interpreting the Policy Decisions are presented. The three linked graphical techniques are applied serially. The first technique identifies Policy Decisions that are nearly Pareto optimal. The second identifies locations where sensor placements are most justified, based on a risk-cost-benefit analysis under uncertainty. The third displays the decision space for any particular policy decision. The method is illustrated in an application to chemical, biological, and/or radiological weapon sensor placement, but has implications for disaster preparedness, transportation safety, and other arenas of public safety.     

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.     

Cost‐Benefit Analysis for Optimization of Risk Protection Under Budget Constraints

Cost‐benefit analysis (CBA) is commonly applied as a tool for deciding on risk protection. With CBA, one can identify risk mitigation strategies that lead to an optimal tradeoff between the costs of the mitigation measures and the achieved risk reduction. In practical applications of CBA, the strategies are typically evaluated through efficiency indicators such as the benefit‐cost ratio (BCR) and the marginal cost (MC) criterion. In many of these applications, the BCR is not consistently defined, which, as we demonstrate in this article, can lead to the identification of suboptimal solutions. This is of particular relevance when the overall budget for risk reduction measures is limited and an optimal allocation of resources among different subsystems is necessary. We show that this problem can be formulated as a hierarchical decision problem, where the general rules and decisions on the available budget are made at a central level (e.g., central government agency, top management), whereas the decisions on the specific measures are made at the subsystem level (e.g., local communities, company division). It is shown that the MC criterion provides optimal solutions in such hierarchical optimization. Since most practical applications only include a discrete set of possible risk protection measures, the MC criterion is extended to this situation. The findings are illustrated through a hypothetical numerical example. This study was prepared as part of our work on the optimal management of natural hazard risks, but its conclusions also apply to other fields of risk management.     

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.     

A Reliability‐Based Capability Approach

This article proposes a rigorous mathematical approach, named a reliability‐based capability approach (RCA), to quantify the societal impact of a hazard. The starting point of the RCA is a capability approach in which capabilities refer to the genuine opportunities open to individuals to achieve valuable doings and beings (such as being mobile and being sheltered) called functionings. Capabilities depend on what individuals have and what they can do with what they have. The article develops probabilistic predictive models that relate the value of each functioning to a set of easily predictable or measurable quantities (regressors) in the aftermath of a hazard. The predicted values of selected functionings for an individual collectively determine the impact of a hazard on his/her state of well‐being. The proposed RCA integrates the predictive models of functionings into a system reliability problem to determine the probability that the state of well‐being is acceptable, tolerable, or intolerable. Importance measures are defined to quantify the contribution of each functioning to the state of well‐being. The information from the importance measures can inform decisions on optimal allocation of limited resources for risk mitigation and management.     

Strategic Integration and Devolvement of Human Resource Management in the UK Manufacturing Sector

This paper analyses the practices of ‘integration’ of HRM into the corporate strategy and ‘devolvement’ of responsibility for HRM to line managers in six British manufacturing industries. The findings are based on a questionnaire survey, in‐depth interviews and cognitive mapping methodologies. The results show that over 50% of the firms under study practise a high level of strategic integration. On the other hand, over 61% of the sample firms practise a low level of devolvement practices. Interestingly, both the practices of integration of HRM into the corporate strategy and devolvement of HRM to line managers are more determined by a number of organizational policies than traditional contingent variables. The adoption of the mixed methodology has been useful. The findings contribute to strategic HRM literature, and also have some key messages for policy‐makers in the field. The cognitive maps developed in the paper could be used to give feedback and training to managers.     

Implementation of Equity in Resource Allocation for Regional Earthquake Risk Mitigation Using Two‐Stage Stochastic Programming

This article presents a new methodology to implement the concept of equity in regional earthquake risk mitigation programs using an optimization framework. It presents a framework that could be used by decisionmakers (government and authorities) to structure budget allocation strategy toward different seismic risk mitigation measures, i.e., structural retrofitting for different building structural types in different locations and planning horizons. A two‐stage stochastic model is developed here to seek optimal mitigation measures based on minimizing mitigation expenditures, reconstruction expenditures, and especially large losses in highly seismically active countries. To consider fairness in the distribution of financial resources among different groups of people, the equity concept is incorporated using constraints in model formulation. These constraints limit inequity to the user‐defined level to achieve the equity‐efficiency tradeoff in the decision‐making process. To present practical application of the proposed model, it is applied to a pilot area in Tehran, the capital city of Iran. Building stocks, structural vulnerability functions, and regional seismic hazard characteristics are incorporated to compile a probabilistic seismic risk model for the pilot area. Results illustrate the variation of mitigation expenditures by location and structural type for buildings. These expenditures are sensitive to the amount of available budget and equity consideration for the constant risk aversion. Most significantly, equity is more easily achieved if the budget is unlimited. Conversely, increasing equity where the budget is limited decreases the efficiency. The risk‐return tradeoff, equity‐reconstruction expenditures tradeoff, and variation of per‐capita expected earthquake loss in different income classes are also presented.     

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.     

Strategies for Knowledge Creation in Firms *

We extend the knowledge‐based view by providing an explanation of how firms develop the capability to create knowledge. We take the view that firms are distributed knowledge systems composed of individuals who embody knowledge, and theoretically identify and empirically test the existence and effectiveness of two strategies – organization and project team – that promote their interactions to develop this capability. On the one hand, building on what we call the organization‐level innovation literature, we identify the organization strategy, which suggests investment in organization‐level integrative management practices to facilitate interactions to create knowledge among individuals situated in different parts of the system, independently of when a knowledge‐creation task is established and individuals are organized to create knowledge. On the other hand, building on what we call the team‐level innovation literature, we identify the project team strategy, which suggests investment in project team‐level integrative management practices to facilitate interactions to create knowledge among individuals once a knowledge‐creation task is defined and individuals are placed into teams to create knowledge. The two strategies are substitute approaches for the development of the capability, although the organization strategy appears to better predict outcomes of the capability. However, this approach might be more costly, so not all managers will choose to follow it.     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Out of mind,out of sight? Leading distributed workers to ensure health and safety

ABSTRACT

Current frameworks of leadership are based on face-to-face interaction. A growing number of workers work away from their main location of work; this makes it challenging for leaders to ensure the health and safety of distributed workers. In the present study, we explore the relationship between line managers’ health and safety leadership and distributed workers’ health and safety behaviours. We also explore the organisational procedures and practices that may enhance the impact of health and safety leadership. We included a broad range of distributed workers (in analyses, minimum N?=?626) from 11 organisations. We found that health-and-safety-specific leadership was positively related to distributed workers’ self-rated health, safety compliance and safety proactivity. These relationships were augmented by distributed workers’ sense of being included in the workplace. Knowledge sharing among colleagues was associated with safety compliance when health-and-safety-specific leadership was low. Our results indicate that one way of addressing the challenges of distributed working may be through line managers putting health and safety on the agenda.     

Assessing Potential Human Health Hazards and Benefits from Subtherapeutic Antibiotics in the United States: Tetracyclines as a Case Study

Many scientists, activists, regulators, and politicians have expressed urgent concern that using antibiotics in food animals selects for resistant strains of bacteria that harm human health and bring nearer a “postantibiotic era” of multidrug resistant “super‐bugs.” Proposed political solutions, such as the Preservation of Antibiotics for Medical Treatment Act (PAMTA), would ban entire classes of subtherapeutic antibiotics (STAs) now used for disease prevention and growth promotion in food animals. The proposed bans are not driven by formal quantitative risk assessment (QRA), but by a perceived need for immediate action to prevent potential catastrophe. Similar fears led to STA phase‐outs in Europe a decade ago. However, QRA and empirical data indicate that continued use of STAs in the United States has not harmed human health, and bans in Europe have not helped human health. The fears motivating PAMTA contrast with QRA estimates of vanishingly small risks. As a case study, examining specific tetracycline uses and resistance patterns suggests that there is no significant human health hazard from continued use of tetracycline in food animals. Simple hypothetical calculations suggest an unobservably small risk (between 0 and 1.75E‐11 excess lifetime risk of a tetracycline‐resistant infection), based on the long history of tetracycline use in the United States without resistance‐related treatment failures. QRAs for other STA uses in food animals also find that human health risks are vanishingly small. Whether such QRA calculations will guide risk management policy for animal antibiotics in the United States remains to be seen.