Research on health monitoring and intelligent early warning system of long longitudinal slope beam bridge in mountainous area based on AI

Due to the complex terrain and changeable climate, long longitudinal slope beam bridges in mountainous areas are easily affected by various natural and human factors, resulting in structural damage and performance degradation of bridges. The traditional bridge health monitoring method relies on manual inspection, which has some problems such as low efficiency and high cost. Therefore, it is particularly important to develop an intelligent monitoring system that can monitor the bridge status in real time, accurately evaluate the health status of the bridge and issue early warning in time. In this paper, a health monitoring and intelligent early warning system based on AI is proposed. The system adopts four-layer architecture design, including perception layer, transmission layer, processing layer and application layer. The structural response data are collected in real time by various sensors installed in key parts of the bridge, and the data are processed, analyzed and damaged by AI algorithm to evaluate the health status of the bridge. The experimental results show that the system can effectively identify bridge damage, the early warning response time is less than five seconds, the false alarm rate is less than 5%, and the false alarm rate is 0%, which meets the real-time requirements of bridge safety monitoring in mountainous areas.