Prioritization of control measures in leakage scenario using Hendershot theory and FBWM-TOPSIS.

Currently, there is increasing concern about the safety and leakage of process industries. Therefore, the present study aims to prioritize control measures before and after the leakage scenario by using the Hendershot theory and MCDM techniques. In this study, two proactive and reactive layers were selected before and after leakage of tanks, respectively.

Quantitative risk estimation of CNG station by using fuzzy bayesian networks and consequence modeling.

As one of the potential explosions and inflammation, compressed natural gas (CNG) stations in urban areas cause irreparable losses and casualties. Estimating risk assessment in gas stress based on coherent uses can reduce accidents in urban areas. The purpose of the present study was to estimate a small risk estimation at one of the CNG multipurpose stations, LPG, using combined models of the Fuzzy Bayesian Network, Bow-tie Diagram, and consequence modeling.

Analyzing the influential factors of process safety culture by hybrid hidden content analysis and fuzzy DEMATEL.

Due to the complex nature of safety culture and process industries, several factors influence process safety culture. This paper presents a novel framework that combines the hidden content analysis method with Decision Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy logic to achieve a comprehensive set of influential factors and their relationship. The proposed methodology consists of two primary stages.

Dynamic risk assessment of storage tank using consequence modeling and fuzzy Bayesian network.

Accidents in process industries cause irreparable economic, human, financial and environmental losses annually. Accident assessment and analysis using modern risk assessment methods is a necessity for preventing these accidents. This study was conducted with the aim of Dynamic risk assessment of tank storage using modern methods and comparing them with traditional method.

Providing an approach to analyze the risk of central oxygen tanks in hospitals during the COVID-19 pandemic.

The central oxygen unit of hospitals is considered a high-risk unit, requiring high safety standards to maintain the integrity of the system during the COVID-19 pandemic. The linear reasoning assumption of conventional risk analysis methods cannot adequately describe these modern systems, which are characterized by tight connections and complex interactions between technical, human, and organizational aspects. Therefore, this study presents a new and comprehensive approach to oxygen tanks in hospitals during the COVID-19 pandemic.

A new approach to chemicals warehouse risk analysis using computational fluid dynamics simulation and fuzzy Bayesian network.

This study aims to assess the risk of chemicals warehouse using a Bayesian networks (BNs) and computational fluid dynamics (CFD). A methodology combining Bow-Tie (BT), fuzzy set theory (FST), and Bayesian network was employed, in which the BT was drawn for chemical spill scenarios. FST was utilized for the estimation of the basic events (BEs) occurrence probability, and the probability of interaction among a set of variables was obtained using BNs.

Modeling important factors on occupational accident severity factor in the construction industry using a combination of artificial neural network and genetic algorithm.

Background: Many occupational accidents annually occur worldwide. The construction industry injury is greater than the average injury to other industries. The severity of occupational accidents and the resulting injuries in these industries is very high and severe and several factors are involved in their occurrence.