Damage detection of cable-stayed bridges using frequency domain analysis and clustering

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 is 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 is carried out between the popular FDD method and two newer AFDD and TDD methods which have improved some of the FDD drawbacks. Then, the most effective method is selected. Among these procedures, FDD is successfully implemented in signal-based procedures. However, the two newer ones have not adequately investigated in comparison to FDD. In the second part, by using competitive neural network for clustering, a new damage index is introduced by calculation of the Euclidian distances of cluster centers. Results show that the proposed damage detection algorithm can differentiate healthy and damage states with acceptable accuracy.