Data-driven time-variant reliability assessment of bridge girders based on deep learning
| Autor(en): |
Qingkai Xiao
(School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
Liu Yiping
(School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
Chengbin Chen
(School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
Licheng Zhou
(School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
Liu Zejia
(School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
Zhenyu Jiang
(School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
Bao Yang
(School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
Liqun Tang
(School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China)
|
|---|---|
| Medium: | Fachartikel |
| Sprache(n): | Englisch |
| Veröffentlicht in: | Mechanics of Advanced Materials and Structures, September 2023, n. 26, v. 31 |
| Seite(n): | 1-13 |
| DOI: | 10.1080/15376494.2023.2253548 |
- Über diese
Datenseite - Reference-ID
10776773 - Veröffentlicht am:
12.05.2024 - Geändert am:
13.01.2025
Structurae kooperiert mit
