Low-Cost Damage Detection of Cable-Stayed Bridges Using Signal Processing and Machine Learning

Document Type : Research Article

Author

Assistant Professor, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

Abstract

Today, it is possible to detect damage in the early stages with the aim of structural health monitoring (SHM) techniques and prevent financial losses and loss of lives. However, high prices of SHM systems has caused that such systems do not gain popularity in our country. The aim of this study is providing a low-cost damage detection technique for bridges based on signal processing and machine learning. In order to reduce expenses, the number of sensors to monitor the vibration of the structure was decreased. Since sensor number reduction can lead to a drop in damage detection accuracy, most up to date signal processing methods were used. In the first step of the paper, several time-frequency signal processing techniques were compared and EWT was selected as the best method. In the next step, after decomposition of signals by time-frequency techniques, a new damage index was introduced base on cross wavelet transform (CWT) and then calculated damaged indices were classified using support vector machine (SVM) to be able to distinguish healthy and damage states. Results showed that the proposed method can detect damage with high accuracy.

Keywords

References

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Amirkabir Journal of Civil Engineering
Volume 52, Issue 1
April 2020
Pages 257-274

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