Feasibility of Damage Detection of Tsing Ma Bridge using Vibration Measurements
Ko, Jan Ming , Ni, Yi-Qing , & Chan, Tommy H.T. (2000) Feasibility of Damage Detection of Tsing Ma Bridge using Vibration Measurements. In Ajtab, A.E. & Gosselin, S.R. (Eds.) Nondestructive Evaluation of Highways, Utilities, and Pipelines, 7-9 Mar, Newport Beach.
In this paper, we study the feasibility of vibration-based damage identification methods for the instrumented Tsing Ma Suspension Bridge with a main span of 1377m. Emphasis is placed on how to deal with the noise-corrupted/uncertain measurement data and how to use the series data from the on-line monitoring system for damage detection. Numerical simulation studies of using the noisy series measurement modal data for damage occurrence detection with the autoassociative neural network and for damage localization with the probabilistic neural network are presented. Five neural network based novelty detectors using only natural frequencies of the intact and damaged structure are first developed for the detection of damage occurrence in the Tsing Ma Bridge. The noisy/uncertain measurement data are produced by polluting the analytical natural frequencies with random noise. Numerical simulations of a series of damage scenarios show that when the maximum frequency change caused by damage exceeds a certain threshold, the occurrence of damage can be unambiguously flagged with the novelty detectors. A probabilistic neural network using noisy modal data (natural frequencies and incomplete modal vectors) is then constructed for the localization of damage occurring at the Tsing Ma Bridge deck. The main-span deck is divided into 16 segments and the damage in each segment is defined as a pattern class. The analytical modal data for each pattern class are artificially corrupted with random noise and then used as training samples to establish a three-layer probabilistic neural network for damage localization. A preliminary investigation shows that the damage to deck members can be localized only when the level of the corrupted noise is low.
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|Item Type:||Conference Paper|
|Additional Information:||For more information, please refer to the publisher’s website (see hypertext link) or contact the author.|
|Keywords:||suspension bridge, on, line monitoring system, damage detection feasibility, measurement noise, structural uncertainty, auto, associative neural network, probabilistic neural network|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300) > Mechanical Engineering not elsewhere classified (091399)|
Australian and New Zealand Standard Research Classification > TECHNOLOGY (100000)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Structural Engineering (090506)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Urban Development
|Copyright Owner:||Copyright 2000 International Society for Optical Engineering (SPIE)|
|Deposited On:||04 Mar 2008|
|Last Modified:||11 Aug 2011 02:30|
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