Human and organizational failures analysis in process industries using FBN-HFACS model: Learning from a toxic gas leakage accident

Abstract

This study used a hybrid technique of Fuzzy sets theory (FST), Bayesian network (BN), and Human Factors Analysis and Classification System (HFACS) to investigate a toxic gas leakage accident quantitatively. HFACS is one of the most popular techniques for assessing the contribution of human and organizational factors in industrial accidents. However, the technique is qualitative and unable to prioritize contributing factors. FST and BN are robust and flexible tools to quantify HFACS. The contributing factors were extracted from the interviews with all key persons and later were classified based on the HFACS taxonomy. The BN model was constructed based on HFACS structure and identified contributing factors. FST was used for estimating the effect rate of contributing factors on the accident. The conversion scale six was employed to gather experts’ opinions, the similarity aggregation method was used for aggregating these opinions, and defuzzification was conducted using the center of gravity method. The improvement index was calculated using the dynamic nature of the BN model for all contributing factors. According to the results, conflicts among several units within the organization, poor safety culture, using substandard equipment, lack of proper inspection on newly-purchased equipment, and several safety violations were the most critical factors contributing to the accident. The improvement index is a function of effect rate, structure of the BN, and organizational level within which the failure occurred, so it is superior to the effect rate for prioritizing contributing factors.