Enhancing Knowledge of Construction Safety: A Semantic Network Analysis Approach

The construction industry is recognized as high-risk due to frequent accidents and injuries, prompting extensive research and bibliometric analysis of construction safety. However, little attention has been given to the evolution and interconnections of key research topics in this field. This study applies semantic network analysis (SNA) to examine relationships and trends in construction safety research over the past 30 years. SNA enables quantitative exploration of topic interrelationships that is difficult to achieve with other approaches. Chronological network graphs are evaluated using the number of nodes, edges, density, average clustering coefficient, and average path length. Prominent topics are identified through degree, betweenness, and eigenvector centrality measures. The analysis combines a global overview of the main network, a chronological perspective, and local examination of clusters based on five macro keywords: accident, safety management, worker behavior, machine learning, and safety training. Results show a shift from traditional concerns with mortality and injuries to contemporary issues, such as safety climate, worker behavior, and technological innovations, including building information modeling, machine learning, and real-time monitoring. Topics with lower centrality scores are identified as under-researched. Overall, SNA offers a comprehensive view of the construction safety knowledge system, guiding researchers toward emerging topics and helping practitioners prioritize resources and design integrated safety risk strategies.