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.

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements.