Research on Tunnel Maintenance Decision-Making System Based on IoT Perception

This paper tackles the limitations of traditional tunnel health monitoring systems, which are confined to the stages of data collection and early warning. It proposes a tunnel maintenance decision-making system rooted in IoT (Internet of Things) perception. By leveraging an IoT sensor network to acquire real-time structural index data of the tunnel, the system performs intelligent analysis on the data to accurately assess and warn of the tunnel's health status. It further establishes a maintenance decision support model to deliver intelligent decision-making—recommending maintenance plans and predicting optimal maintenance timings. Research findings demonstrate that the system significantly enhances the scientific validity and cost-effectiveness of tunnel maintenance decisions, reducing life-cycle maintenance costs.