Establishment of Regional Bridge Degradation Model for Shandong Province
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Détails bibliographiques
| Auteur(s): |
Shanshan Wang
(Shandong Hi-speed Group Co. LTD, Jinan, Shandong, 250014, China)
Lanxin Luo (Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China) Zhiqiang Shang (Shandong Hi-speed Group Co. LTD Innovation Research Institute, Jinan, Shandong, 250014, China) Yangchun Wang (Shandong High-speed Engineering Inspection Co., Ltd, Jinan, Shandong, 250014, China) |
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| Médium: | papier de conférence | ||||
| Langue(s): | anglais | ||||
| Conférence: | IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022 | ||||
| Publié dans: | IABSE Congress Nanjing 2022 | ||||
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| Page(s): | 1575-1581 | ||||
| Nombre total de pages (du PDF): | 7 | ||||
| DOI: | 10.2749/nanjing.2022.1575 | ||||
| Abstrait: |
In this paper, a neural network-based regional bridge condition degradation model establishment method is proposed for the problem of regional bridge network level assessment and management and maintenance strategy optimization. First, a subset of features for bridge condition prediction is extracted from the road network database, and a suitable secondary transcoding technique is selected to accommodate the training of artificial neural networks; then, a cost-sensitive training error is introduced to obtain the optimal bridge degradation model through model selection. To verify the feasibility of the method, a case study of a small road network in the main highway section of Shandong Province was selected to obtain the degradation model of the bridge group in the region, which provides a basis for the future maintenance strategy of the regional road network. |
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| Copyright: | © 2022 International Association for Bridge and Structural Engineering (IABSE) | ||||
| License: | Cette oeuvre ne peut être utilisée sans la permission de l'auteur ou détenteur des droits. |
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