Program Results

Introduction to the event

Regular structural health inspections are a critical component of building safety assessment. However, traditional inspection methods remain highly labor-intensive. In recent years, numerous studies have demonstrated the effectiveness of unmanned aerial vehicles (UAVs) in this field, significantly improving inspection efficiency while reducing the risk of exposing personnel to hazardous environments such as bridges, wind turbine towers, and dams. Despite these advances, UAV-based inspections still rely heavily on human involvement, including manual operation or pre-defined flight path planning. Such dependence inevitably introduces human error and can lead to blind spots in structural coverage, which poses challenges for both UAV control and path planning. To address these issues, this study proposes a fully automated crack inspection framework. By leveraging deep reinforcement learning, we train an autonomous agent capable of adaptively following crack patterns to maximize inspection efficiency, while also learning to decide the appropriate stopping time to terminate the search in order to mitigate UAV battery usages. Figure 1 shows an example of surface cracks, and Figure 2 demonstrates that the trained agent is able to explore the existence of cracks and navigate itself without human operation, by using only partially observable states indicated in yellow boxes. Our proposed approach substantially reduces the time and labor costs associated with structural health monitoring, while enabling more frequent inspections. Ultimately, this contributes to earlier detection of potential structural problems and improved safety and durability of critical infrastructure.

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