Probabilistic Machine Learning Approach to Bridge Fatigue Failure Analysis due to Vehicular Overloading
With the rapid development of freight transportation, truck overloading becomes very common and severe, posing a great threat to the safety of bridges, and it can even result in bridge failure. Traditional approaches investigating the overloading-induced fatigue damage on bridges, such as finite element analysis (FEA) and reliability analysis, have proven to be computationally expensive and model dependent. In this study, the prediction of fatigue failure probability of bridges due to traffic overloading was investigated by using the feedforward neural network and the Monte Carlo method. The results show that based on a finite set of training data for the bridge under consideration, the proposed machine-learning-based approach can assist in providing an instantaneous assessment of the fatigue failure probability with high accuracy.
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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:
- © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Yan, Wangchen
- Deng, Lu
- Zhang, Feng
- Li, Tiange
- Li, Shaofan
- Publication Date: 2019-8-15
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 91-99
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Serial:
- Engineering Structures
- Volume: 193
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0141-0296
- Serial URL: http://www.sciencedirect.com/science/journal/01410296
Subject/Index Terms
- TRT Terms: Failure; Highway bridges; Machine learning; Overweight loads; Trucks
- Subject Areas: Bridges and other structures; Freight Transportation; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01711778
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 22 2019 10:32AM