Bridge Inspection Strategy Analysis through Human-Drone Interaction Games

Bridge inspections are characterized by their labor-intensive nature and inherent risks, relying predominantly on engineers’ visual analysis. Although the integration of drones has alleviated the safety concerns associated with human labor, the accurate identification of defects in vital elements continues to necessitate inspectors’ specialized knowledge. Aggregating multi-inspector experiences can improve the localization of critical defects. The challenge lies in capturing and explaining drone trajectories into reusable and explainable strategies. This paper presents a framework to capture inspectors’ strategies by analyzing drone control in bridge inspection simulations. It gathers and scrutinizes inspectors’ drone control histories to understand their intentions. Due to the vast search space of inspection strategies in dynamic, uncertain contexts, imitation and reinforcement learning are utilized to learn reusability and explainability. Experiments demonstrate that drone trajectories aligned with bridge elements can explain inspection knowledge. Inspectors with explainable patterns, such as the human attention between the different spans inside the span, achieve better defect detection performance (correlation coefficient of 0.5). This framework promotes inspector-drone collaboration that adaptively supports human inspectors, resulting in more reliable inspections.