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Title: Modal flexibility analysis of cable-stayed Ting Kau bridge for damage identification
Authors: Ni, YQ 
Zhou, HF
Chan, KC
Ko, JM 
Issue Date: 2008
Publisher: Wiley-Blackwell
Source: Computer-aided civil and infrastructure engineering, 2008, v. 23, no. 3, p. 223-236 How to cite?
Journal: Computer-aided civil and infrastructure engineering 
Abstract: The cable-stayed Ting Kau Bridge has been permanently instrumented with more than 230 sensors for long-term structural health monitoring. In this article, the feasibility of using the measured dynamic characteristics of the bridge for damage detection is studied. Making use of a validated three-dimensional (3D) finite element model (FEM), modal flexibility matrices of the bridge are constructed using a few truncated modes and incomplete modal vectors at the sensor locations. The relative flexibility change (RFC) between intact and damage states is then formulated as an index to locate damage. The applicability of this flexibility index for damage location in the cable-stayed bridge is examined by investigating various damage scenarios including those at stay cables, longitudinal stabilizing cables, bearings and supports, longitudinal girders and cross girders, and taking into account measurement noise in modal data. The influence of two ambient factors, that is, temperature change and traffic loading, on the damage detectability is also analyzed by approximately considering an equivalent alteration in the material and structural behaviors. It is revealed that in the absence of ambient effects the RFC index performs well for locating damage of different severities in single-damage cases. In multi-damage cases the RFC index may provide false-negative identification for damage at the members with low sensitivity. Eliminating ambient effects is requisite for reliable detection of damage at stay cables and cross girders. The capability of the RFC index for locating damage at cross girders is significantly dropped in the presence of measurement noise.
ISSN: 1093-9687
EISSN: 1467-8667
DOI: 10.1111/j.1467-8667.2008.00521.x
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