Design and application of intelligent monitoring system for road and bridge based on Internet of Things technology

With the continuous improvement of the networked layout of transportation infrastructure, the structural safety and operation efficiency of roads and bridges, as the core hubs, have become key factors affecting the sustainable development of transportation. Traditional monitoring methods have limitations such as weak real-time performance, insufficient data coverage, and delayed anomaly warnings, which render them unable to meet the requirements of precise management under complex conditions. Therefore, this paper designs an intelligent monitoring system for roads and bridges based on Internet of Things technology. This system integrates a multi-source sensor fusion architecture, edge-cloud collaborative computing, adaptive data processing algorithms, and an improved attention-mechanism Long Short-Term Memory (LSTM) anomaly warning model, achieving full-dimensional, high-precision, and real-time monitoring of bridge structural strain, vibration, settlement, and environmental parameters. The system constructs a five-level architecture comprising perception, transmission, processing, warning, and application, and introduces a sensor node dynamic deployment model, a multi-modal data weighted fusion algorithm, and a load-adaptive scheduling mechanism. This approach effectively solves problems such as heterogeneous data transmission conflicts, edge-node computing power bottlenecks, and low accuracy of anomaly identification under complex conditions. Experimental results show that the average error of sensor data collection is controlled within 0.32%, the data transmission delay is as low as 18.7 ms, and the anomaly warning accuracy reaches 97.6%, which is 15.3%, 42.6%, and 21.8% higher than those of traditional monitoring systems, respectively. In actual bridge operation scenarios, the system can effectively identify potential risks such as crack expansion and structural settlement, shorten the fault response time to within 3 min, and maintain a stable operation rate of 99.2% even under extreme weather conditions, such as heavy rain and strong winds, as well as under high-traffic conditions. This system provides an intelligent and scalable technical solution for the full life-cycle management of roads and bridges, applicable to the regular monitoring and emergency response of large-scale transportation infrastructure such as expressway bridges, urban overpasses, and long tunnels.