Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: A feasibility study

Ni, Yi-Qing, Wang, Junfang, & Chan, Tommy H.T. (2015) Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: A feasibility study. Structural Engineering and Mechanics, 54(2), pp. 337-362.

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Abstract

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

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ID Code: 82945
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: structural health monitoring, damage alarming and localization, multi-novelty indices, auto-associative neural networks, cable supported bridges
DOI: 10.12989/sem.2015.54.2.337
ISSN: 1225-4568
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Structural Engineering (090506)
Divisions: Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Techno Press
Deposited On: 30 Mar 2015 04:35
Last Modified: 10 Dec 2015 12:47

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