Wang, Y. and Zou, P. X. W. (2026) Uncovering key construction injury causal factors using topic modeling and word co-occurrence network analysis methods. Journal of Construction Engineering and Management, 152(10): 04026168, ISSN 0733-9364
Abstract
The construction industry suffers from a high rate of injuries, with unstructured textual injury reports containing rich but underutilized causal information. Traditional analytical methods struggle with the high dimensionality and semantic sparsity of this data, limiting the depth of causal insight. To address this gap, this research develops an analytical method that combines latent Dirichlet allocation (LDA) topic modeling and word co-occurrence network (WCN) analysis. Applied to 25,980 real-world injury reports, the proposed method first identified three coherent injury topics. Subsequently, WCN analysis with centrality metrics (betweenness, closeness) revealed not only explicit key factors but also situation-dependent implicit factors that act as critical intermediaries in specific injury pathways. Crucially, the validity and industry relevance of the findings were systematically benchmarked against the US Occupational Injury and Illness Classification System (OIICS). The contributions of this research are as follow: (1) Methodologically, it advances current analytical practices by developing and demonstrating an integrated framework that combines macro-topic discovery (via LDA) with micro-semantic relationship analysis (via WCN). This integration directly addresses the semantic sparsity of injury reports, transforming flat keyword lists into interpretable causal networks, (2) theoretically, it extends the understanding of injury causation by utilizing network centrality metrics to identify and quantify the role of situational, implicit factors that act as critical mediators within specific injury pathways, offering a systemic perspective beyond frequency-based counts, and (3) practically, it provides a structured, network-based visualization of injury factors and their strongest associations, offering an evidence-based complement to key factors identification methods for prioritizing accident prevention measures.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction management; construction safety; injury prevention; latent dirichlet allocation; network analysis; text mining; topic modeling |
| Index terms: | network analysis, construction industry, injury, mining, mediator, integration, intermediary, evidence, modelling, construction safety, accident prevention, illness, validity, identification method, visualization, prioritizing, prevention |
| Subjects: | environmental health, health conditions and diseases, data analysis and analytics, analytical methods, evaluation and assessment methods, geotechnical engineering, decision analysis, design practice, dispute resolution, industry analysis, financial risk, business, organizational analysis |
| Topics: | Legal Issues, Risk Management, Engineering Principles, Organizational Design, Research Practice, Design Practice, Sustainability, Cost Management, Health and Safety, Business Strategy |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here