LAUSR

Abstract Lacking a holistic framework for analyzing risk factors would result in the inaccurate assessment of safety performance and poor safety management. This research aims to establish a Bayesian-network (BN)-HFACS hybrid model to proactively predict safety performance in construction projects. First, a causation framework for analyzing the underlying factors influencing construction safety performance was established using the Human Factors Analysis and Classification System (HFACS). This causation framework incorporates 18 risk factors from organizational, environmental and human aspects that are categorized into five levels: L1: “unsafe acts of workers,” L2: “preconditions for unsafe acts,” L3: “unsafe supervision and monitoring,” L4: “adverse organizational influences,” and L5: “adverse environmental influences.” The relationships between these factors and project safety performance were then hypothesized in the BN-HFACS model, and validated by data collected with questionnaires. The proposed model was applied to a subway project with AgenaRisk software. This application demonstrated the model’s capabilities in systematically identifying risk factors, predicting the probabilities of safety states in project level and in the five specific cause levels, and diagnosing the most sensitive risk factor. This research contributes to safety assessment and management by modifying the original HFACS for the causation analysis of construction safety performance, and by establishing a BN model for quantifying the total influences of the risk factors at five distinct levels on project safety performance. The integration of HFACS and BNs may be instructive in other contexts where diverse safety risk factors are involved in a system and safety prediction of the system is necessary.