This study investigates the causal mechanisms underlying prefabricated building construction safety accidents (PBCSA) and aims to provide a systematic framework for identifying key risk factors and propagation pathways in complex construction environments.
Based on 267 accident cases, 5 accident types and 101 causal factors were extracted. A weighted causal network was constructed using association rule mining (ARM), followed by structural analysis of network properties. A Multivariate Feature Graph Convolutional Network (MF-GCN) was developed to identify critical causal nodes, and a depth-first search algorithm was applied to extract representative causal pathways.
The constructed causal network exhibits clear small-world characteristics and a heavy-tailed degree distribution, indicating a structured rather than random risk formation mechanism. MF-GCN significantly outperforms traditional centrality measures in identifying key causal factors, with root and direct causes accounting for approximately 75% of the most influential nodes. Risk propagation is primarily driven by a small number of recurrent causal chains, where factors such as improper lifting, inadequate risk assessment and insufficient safety distance act as key bridging nodes across accident types.
This study integrates the “2–4” accident classification model with ARM and graph learning, providing a unified framework for causal structure discovery in construction safety systems. The proposed MF-GCN enhances the identification of influential risk nodes in complex accident networks, while the extracted causal chains reveal cross-type risk propagation mechanisms, offering actionable insights for proactive safety management.
