Abstract

Irregular-shaped bridge is an important component of urban overpass and is prone to damage due to severe overloading and material deterioration. Structural damage detection is necessary to prevent bridge failure and guarantee the safe operation of urban traffic. For vibration-based damage detection methods, mode shape of full-scale structure is difficult to be measured with the limited number of sensors, while modal frequency can be obtained accurately and conveniently. This paper aims to propose a two-stage scheme for damage identification using the ratios of modal frequency changes and uniform load surface curvature difference (ULSCD) in damage region. FCM algorithm improved by PSO algorithm (FCM-PSO) is employed to locate damage and predict the damage extent. Firstly, the ratios of modal frequency changes from training cases are classified into several clusters based on FCM-PSO analysis. And the cluster centers for damage locations are constructed. Damage location can be identified by calculating the fuzzy memberships between identification indicator vector and cluster centers of damage locations. After obtaining the damage location, ULSCD values in damage region are established to assess damage severity based on the memberships in damage grades. Damage identification results for typical irregular-shaped bridge demonstrate that the two-stage damage identification method is efficient and accurate to identify the occurrence, location and extent of structural damage

Highlights

  • Bridge structures are prone to damage due to long-term deterioration under external environment factors and service load [1, 2]

  • In order to determine the clusters of damages, the fuzzy c-means (FCM) clustering has been applied to structural health monitoring (SHM) problems

  • An efficient two-stage damage identification approach is proposed, which employs the ratios of modal frequency changes and uniform load surface curvature difference (ULSCD) values combining with FCM algorithm improved by particle swarm optimization (PSO) algorithm (FCM-PSO) algorithm

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Summary

Introduction

Bridge structures are prone to damage due to long-term deterioration under external environment factors and service load [1, 2]. DAMAGE IDENTIFICATION FOR IRREGULAR-SHAPED BRIDGE BASED ON FUZZY C-MEANS CLUSTERING IMPROVED BY PARTICLE SWARM OPTIMIZATION ALGORITHM. A large number of sensors will increase the cost of bridge damage detection and restrict the practical application of VBDD technology These incomplete measurements will make the damage identification indices derived from mode shape inaccurate or useless [6]. Damage location is not identified accurately in symmetrical structure These difficulties of damage identification based on modal frequency can be overcome to some extent in irregular-shaped bridge. Since the first several natural frequencies and mode shape in local region can be obtained accurately and conveniently, the ratios of modal frequency changes and uniform load surface curvature difference (ULSCD) are selected to construct damage identification indices. Damage identification results indicate that the two-stage damage identification method is favorable to identify the damage for irregular-shaped bridge

The ratio of modal frequency changes
Uniform load surface curvature difference
FCM clustering algorithm
PSO algorithm
Clustering validity evaluation
Numerical simulation
Damage location
Damage severity
Determination of cluster centers
Damage location identification
Determination of damage grades
Damage grade identification
Damage severity identification with single damage location
Section 2
Findings
Conclusions
Full Text
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