Design of building structure health monitoring model based on IoT and MEMS sensors

Traditional structural health monitoring methods often rely on manual inspection, which is not only inefficient but also suffers from data collection delays, subjective error sensitivity, and the inability to continuously track subtle structural changes in complex buildings over time. Therefore, this study innovatively designed a structural health monitoring system based on Internet of Things (IoT) and Micro Electro Mechanical Systems (MEMS) sensors. The system has a unique three-layer architecture, including a perception layer for collecting data through MEMS sensors, a network layer for low-power wireless data transmission, and an application layer for cloud based data analysis and visualization. Wavelet transform is also used for signal denoising to reduce external interference. Performance testing shows that the self-made system exhibits excellent performance in bridge vibration monitoring, with time-domain and frequency-domain analysis verifying the accuracy of vibration data. The relative error in frequency identification is only 1.38%. In actual testing, the frequency error of each measuring point is controlled within 3%, and the average relative error is 1.4–1.6%. The research designed building structural health monitoring models have low cost, high accuracy, and reliability, and have broad application prospects in vibration monitoring of building bridges.