The Bridge Data Diagnosis Research Based on Structural Health Monitoring System

The research on the bridge structural monitoring focuses mainly on the identification of the structure damage position and degree. However, these researches are based on the data of the structural health monitoring system. In the actual environment, the abnormal data caused by the failure monitoring system can often make the false prognosis, increasing the false alarm rate. Meanwhile, the bridge may have serious structural damage from the unexpected events caused by some external loads. They are not conducive to the bridge safety maintenance and management. In order to ensure the bridge safety and improve the effectiveness of the bridge structure monitoring, it is necessary to diagnose these special events. In the paper, kernel principal component analysis (KPCA) and hyperspherical support vector machine method are employed to separate the general monitoring data from the event data. The acceleration sensor data in Jiangyin Bridge is used to validate the effectiveness of the method under the ship collision, typhoons, sensor installed noise, and sensor step signals.