Human Factor Analysis (HFA) Based on a Complex Network and Its Application in Gas Explosion Accidents

Abstract

Humans are at the core of the social-technical system, and their behavioral errors affect the reliability and safety of the entire system in varying degrees. Occupational accidents and large-scale industrial accidents are often attributed to human errors, accounting for more than 80% of accidents. In view of the complexity of systems and the coupling of elements, a new HFA method is proposed based on a complex network. According to system safety theory, a complex network is regarded as a network composed of humans, matters, environments, and management, and the basic structure of the HFA network is summarized. On this basis, a system safety method of HFA is developed which proposes a universal human error causation model. Moreover, a network analysis method for human errors is also presented, which is a comprehensive analysis of human errors that have occurred. Finally, the above methods are applied to gas explosion accidents that occurred in China. Results show that the two methods proposed are universal to all fields, and their combination improves the effectiveness of human error management and promotes the targeted, proactive, systematic, and dynamic prevention of critical nodes and paths from a holistic perspective.

Analysis of Human-Related Incidents during Dynamic Positioning Operations

“…Zhang et al [17] proposed a method called Human Factor Analysis (HFA) based on a complex network to analyze the human factors involved in gas explosion accidents. The HFA method is designed to identify the critical human factors contributing to accidents and provide guidance for safety management.…”

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

“…In this paper, a complex network causation model is established from a multi-factor and multi-level perspective of accident occurrence. This model takes into account the internal node indicators and extracts the features of the most influential factors [23][24][25], providing a more comprehensive understanding of the causal relationship among factors. Additionally, a classification model of accident injury severity based on machine learning is constructed to enhance the safety and stability of road traffic passage.…”

“…The accidents occurred on the right side of the road r 18 The environmental temperature during the accidents was low temperature r 19 The environmental temperature during the accidents was moderate temperature r 20 The environmental temperature during the accidents was high temperature r 21 The environmental humidity during the accidents was dry r 22 The environmental humidity during the accidents was humid r 23 The environmental humidity during the accidents was wetter There was an intersection near the accidents r 35 There was no intersection near the accidents r 36 There was a reducer belt near the accidents r 37 There was no reducer belt near the accidents r 38 There was a deceleration sign near the accidents r 39 There was no deceleration sign near the accidents r 40 There was a railway near the accidents r 41 There was no railway near the accidents r 42 There was a road safety measure near the accidents r 43 There was no road safety measure near the accidents r 44 There was a station near the accidents r 45 There was no station near the accidents r 46 There was a stop sign near the accidents.…”

Research Paradigm of Network Approaches in Construction Safety and Occupational Health

“…Accident diagnosis and management [23] Fall from height; construction workers [24] Construction safety; predictive analysis; tunnel construction [28] CN Complex network Human error; safety assessment [32] explanation, prediction Near-miss; metro construction; safety management [42] Construction safety; subway construction [25] description, explanation Unsafe behaviors; accident prevention; urban railway construction [46] Safety management; design for safety (DFS); prevention through design (PTD); subway construction [69] Accident analysis; railway operational accident [70] Accident analysis; metro operation hazard network (MOHN) [71] Deep foundation pit; subway construction [17] Construction workers; unsafe behavior [72] Unsafe behavior; accident prevention; urban railway [73] Accident level; accident chain; construction [44] description, explanation, control Human factor analysis (HFA); occupational safety [48] Organizational synchronization; construction delay factors [74] CNN Convolutional neural network Fall prevention; personnel protective equipment [75] explanation, prediction, control Construction safety; guardrail detection [29] FNN Fuzzy neural network Worker-machine safety; intelligent assessment [76] explanation, prediction, control NN Neural network;…”