Identifying Overlapping Causal Factor Clusters of Fall from Height Accidents Based on Overlay Network Chain in Quotient Space

Fall from height (FFH) remains the predominant cause of fatalities and injuries in construction and industrial safety worldwide. Traditional accident cause analysis approaches often focus on single-factor identification or static statistical analysis, ignoring the hierarchical coupling and correlational relationship of multiple factors. To address this research gap, this study proposes a novel method based on an overlay network chain in quotient space to identify overlapping causal factor clusters in FFH accidents. First, a causal factor interaction network is constructed based on the similarity of causes. On this basis, an overlay network chain in quotient space is established to identify overlapping causal factor clusters. The proposed method is validated using real FFH accident data. Results demonstrate that this approach effectively identifies hidden high-risk overlapping clusters that are undetectable by traditional methods. Notably, Cluster C5—comprising PPE non-compliance, inadequate supervision, unverified qualifications, and insufficient training—exhibits the highest occurrence rate (0.072) and structural vulnerability (CVI = 9.5), identifying it as the most critical risk pattern. The identified clusters are highly consistent with practical engineering experience. This study provides a systematic, data-driven tool for FFH accident causation analysis, supporting targeted safety prevention and control strategies.