Damage Detection of Cable-Stayed Bridges Using Frequency Domain Analysis and Clustering

Document Type : Research Article

Author

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

Abstract

 Cable-stayed bridges are vital structures which need significant maintenance and repair costs every year. Therefore, health monitoring of such structures can mitigate human and financial losses. In this paper, a damage detection method for cable-stayed bridges was proposed using signal processing and clustering. Since the accuracy of signal processing can considerably affect the accuracy of damage detection results, in the first part of the paper, a comparison was carried out between the popular FDD method and two newer AFDD and TDD methods, which were improved some of the FDD drawbacks. Then, the most effective method was selected. Among these procedures, FDD was successfully implemented in signal-based procedures. However, the two newer ones had not adequately investigated in comparison to FDD. In the second part, by using competitive neural network for clustering, a new damage index was introduced by calculation of the Euclidian distances of cluster centers. Results showed that the proposed damage detection algorithm can differentiate healthy and damage states with acceptable accuracy.

Keywords

References

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Amirkabir Journal of Civil Engineering
Volume 51, Issue 4
November and December 2019
Pages 767-780

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