Low cost damage detection of cable-stayed bridges using signal processing and machine learning

Today, it is possible to detect damage in the early stages with the aid of structural health monitoring (SHM) techniques to avoid financial losses and loss of lives. However, large expenses of SHM systems has caused low popularity of such systems in our country. The aim of this study is to provide a low-cost damage detection technique for bridges based on signal processing and machine learning. To reduce expenses, the number of sensors to monitor vibration of the structure is decreased to only one sensor. Since reduction of number of sensors can lead to drop in damage detection accuracy, most up to date signal processing methods are used. In the first step of the paper, several time-frequency signal processing techniques are compared and EWT is selected as the best signal processing method. In the next step, after decomposition of signals by time-frequency techniques, a new damage index is introduced base on cross wavelet transform (CWT) and then calculated damaged indices are classified using support vector machine (SVM) to be able to distinguish healthy and damage states. Results show that the proposed method can detect damage with high accuracy.