Modularity-Driven Keyword Co-Occurrence Network for Mining Statistical Associations in Construction Safety Accidents

To address the limitations of traditional construction safety accident analysis, which relies on manually defined causal relationships, requires extensive data annotation, and struggles to identify latent risks from Chinese unstructured texts, this study proposes an unsupervised and data-driven framework, termed CESA-Miner, for mining statistical association patterns among construction safety accidents. The proposed framework adopts a modularity-driven keyword optimization strategy to automatically identify a stable set of risk-related features. Based on this, an accident risk weighted co-occurrence network is constructed, where statistical associations are represented through keyword co-occurrence patterns and network community structures. Community detection algorithms are then applied to identify accident clusters and their underlying relationships. Using a dataset of 1368 official construction accident reports, the results show that the network modularity increases from 0.173 to 0.683, indicating significantly improved structural quality and community separability. In the absence of explicit ground truth, structural quality is evaluated using network modularity as a proxy metric. Compared with conventional clustering-based and embedding-based approaches, the proposed method yields a more structurally distinct network community organization and offers a complementary structure-aware perspective for characterizing accident relationships. The framework enables large-scale intelligent analysis of accident texts without requiring manual annotation, providing data-driven support for latent risk identification and statistical pattern analysis in construction safety.