F-BN-based fatigue risk early warning method for workers in micro pipe-jacking rock drilling operations

Abstract

In view of the multi-dimensional, dynamic and uncertain characteristics of the fatigue risk of micro-tunneling rock-drilling workers, an early-warning method was proposed based on the Fuzzy-Bayesian Network(F-BN) to achieve accurate assessment and real-time early-warning of fatigue risk. By comprehensively considering factors from four aspects: human, physical, environmental, and organizational management, 24 main risk factors are determined. The fuzzy multi-attribute decision-making method is used to integrate expert evaluation data, and in-depth analysis is carried out in combination with Bayesian network reasoning. The research shows that factors such as “unreasonable work organization and arrangement” and “inappropriate temperature, humidity or air pressure” are key causal factors with high posterior probabilities. They are closely related to fatigue risk and occupy a core position. The main transmission links with high intensity include excessive nervousness→psychological abnormality, overload→external manifestation, etc. The proposed method provides a theoretical basis for the occupational safety and health management of pipe-jacking construction.

Keywords

Bayesian network / pipe jacking / fatigue risk / risk early warning / fuzzy multi-criteria decision-making