Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Guanghua Tang, Xiaoying Ye, Xianhong Zhu
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414396177