AI Insight
This study examines how artificial intelligence can enhance the competency of safety directors in Engineering-Procurement-Construction (EPC) projects, which are increasingly complex and risk-intensive. Using dynamic capability theory, synergy theory, and the Technology Acceptance Model (TAM), the authors developed a three-dimensional competency framework (sensing, seizing, and reconfiguring) and evaluated it through comparative experiments and Analytic Hierarchy Process (AHP) analysis. Results showed that an AI-empowered approach increased the Comprehensive Competency Index (CCI) by 45.9% compared to traditional methods, suggesting a measurable shift from experience-based to data-driven safety management.
Why it matters
The proposed tiered improvement strategy and quantitative evaluation system offer construction companies a structured pathway for selecting safety talent and implementing digital transformation, potentially reducing workplace incidents in large-scale infrastructure projects.
Understand the Science
by Jing Guan, Zhen chao Yang, Congcong Wang
With the widespread application of the engineering–procurement–construction (EPC) delivery model in large-scale infrastructure and complex industrial projects, the highly dynamic construction environment and the escalating complexity of risks pose heightened requirements for the competency of project safety directors. Conventional approaches that rely primarily on experience and manual inspections have become a bottleneck to further improvements in safety management capability. This paper investigates strategies to enhance the competency of safety directors in EPC projects through AI empowerment. Drawing on dynamic capability theory, synergy theory, and the Technology Acceptance Model (TAM), we develop a competency enhancement framework comprising three dimensions—sensing, seizing, and reconfiguring. A comparative experimental design and the Analytic Hierarchy Process (AHP) were employed for empirical evaluation. The results indicated that the Comprehensive Competency Index (CCI) under the AI-empowered mode increased by 45.9% relative to the traditional mode, enabling a shift in safety management from experience-driven practices to data- and intelligence-driven governance. Furthermore, this study proposes a tiered improvement strategy and a quantitative evaluation system, offering theoretical grounding and practical guidance for talent selection and digital-intelligent transformation in construction enterprises..