Damage detection of structural based on indirect vibration measurement results combined with Artificial Neural Network

Abstract

In Structural Health Monitoring (SHM), damage detection and maintenance are among the most critical factors. For surface damage, damage detection is simple and easy to perform. However, detecting and repairing is difficult for damage hidden deep in the structure. Using the structure's dynamic features, damage can be detected and repaired in time. With the development of sensor technology, indirect vibration measurement solutions give accurate results, minimizing errors by infinitely increasing the number of measurements. This solution offers a great opportunity to reduce the cost of structural health monitoring. Based on the large amount of data obtained from indirect monitoring, artificial intelligence technologies can be used to obtain a more comprehensive model of SHM. In this paper, the dynamic responses of the structure will be extracted and determined through a vehicle crossing the bridge. Based on the results of structural dynamic response, a finite element model is built and updated so that this model can represent the real structure. Damage cases will be analyzed and evaluated as input to train the Artificial neural network. The trained network can detect damage through regular health monitoring by indirect methods.