| Structural Engineering and Mechanics Volume 54, Number 2, April7 2015, pages 337-362 DOI: https://doi.org/10.12989/sem.2015.54.2.337 | |
Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: a feasibility study | |
Yi-Qing Ni, Junfang Wang and Tommy H.T. Chan
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| Abstract [Buy Article] | |
| This paper presents a feasibility study on structural damage alarming and localization of longspan 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 multinovelty 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. | |
| Key Words | |
| structural health monitoring; damage alarming and localization; multi-novelty indices; auto- associative neural networks; cable-supported bridges | |
| Address | |
| Yi-Qing Ni, Junfang Wang: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Tommy H.T. Chan: School of Civil Engineering and Built Environment, Queensland University of Technology, Brisbane, Australia | |
