A CPWB-DEMATEL-Based Risk Assessment Method for AR-Assisted Overhead Crane Inspection

Applying augmented reality (AR) glasses to overhead crane inspection can significantly improve the efficiency and accuracy of inspection operations. However, this technology also introduces new and complex risks stemming from the intricate interactions between people, machines, the environment, and management factors. Because traditional risk assessment methods implicitly assume factor independence, they struggle to capture this complexity. To overcome this deficiency, this study develops a Consequence-Prioritized Weighted Borda integrated with DEMATEL (CPWB-DEMATEL) hybrid framework specifically designed for risk assessment in AR-assisted inspection environments. First, an application-oriented risk identification framework is proposed by expanding SHELL model from a traditional four-dimension structure to include dedicated Personnel (P) and Management (M) dimensions, which leads to the systematic identification of 27 critical risk factors. Second, the weighted Borda severity ranking is combined with DEMATEL network centrality, ensuring that the comprehensive risk priority reflects both high inherent severity and high systemic influence. At the same time, this integration further enables a critical risk propagation path identification procedure that extracts multi-hop transmission chains from the DEMATEL influence matrix, revealing system-level risk propagation structures. Third, a case study conducted at a special equipment inspection agency validated the applicability of the framework. The results show that operator/rigger violations, signal personnel coordination errors, multi-task coordination failures, inadequate AR process supervision, and wire rope recognition errors are the most critical risk factors, with management deficiencies and communication-related factors also playing significant roles in risk propagation. Comparative analyses and sensitivity tests confirm the stability and robustness of the derived risk rankings across various parameter settings.