A Blockchain-Driven Framework for Deep Reinforcement Learning-Controlled UAV Bridge Health Monitoring

Structural health monitoring (SHM) of bridges is essential for maintaining safe and reliable transportation networks. This study integrates the EOSIO blockchain with unmanned aerial vehicles (UAVs) and deep reinforcement learning using the proximal policy optimization algorithm to optimize UAV-based bridge inspections and crack detection. The blockchain ensures secure and tamper-proof crack data storage, enhancing transparency and data integrity. The experimental results show a 28.57% latency improvement in low-traffic scenarios, demonstrating the method’s potential to improve SHM efficiency and support infrastructure modernization efforts.