Machine learning-based bridge cable damage detection under stochastic effects of corrosion and fire
This paper proposes a novel machine learning-based cable damage detection model to investigate the upper and lower bounds of bridges’ cable damage degrees under the effects of corrosion and fire. In the proposed approach, the surrogate model for bridge cable damage detection under stochastic effects of corrosion and fire was established by combining machine learning and finite-element analysis to estimate the remaining life of cables. Then the accuracy and generalization performance of three typical machine learning methods for cable damage prediction are compared, such as Back Propagation neural network(BPNN), Radial Basis Function neural network(RBFNN) and Least Square-Support Vector Machine (LS-SVM). It is conducted that LS-SVM owns better prediction accuracy for cable damage under the coupling effects of corrosion and fire than the others. Additionally, the LS-SVM surrogate model combined with stochastic analysis and time-dependent deterioration model of steel wires under corrosion and fire is used to obtain the upper and lower bounds of cable damage under coupling effect of corrosion and fire. The proposed surrogate model can assist management in diagnosing and evaluating cable damage more quickly, efficiently, and flexibly once the real-time monitoring data is obtained. In addition, the surrogate model can guide bridge maintenance in advance.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01410296
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Feng, Jinpeng
- Gao, Kang
- Gao, Wei
- Liao, Yuchen
- Wu, Gang
- Publication Date: 2022-8-1
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 114421
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Serial:
- Engineering Structures
- Volume: 264
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0141-0296
- Serial URL: http://www.sciencedirect.com/science/journal/01410296
Subject/Index Terms
- TRT Terms: Bridge cables; Corrosion; Fire; Machine learning; Stochastic processes; Structural deterioration and defects
- Subject Areas: Bridges and other structures; Highways; Maintenance and Preservation;
Filing Info
- Accession Number: 01851927
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 20 2022 10:42AM