Data-driven time-variant reliability assessment of bridge girders based on deep learning-Reference-Cited by-同舟云学术

Data-driven time-variant reliability assessment of bridge girders based on deep learning

Published:2023-09-06 Issue: Volume: Page:1-13
ISSN:1537-6494
Container-title:Mechanics of Advanced Materials and Structures
language:en
Short-container-title:Mechanics of Advanced Materials and Structures

Author:

Xiao Qingkai¹^ORCID,Yiping Liu¹^ORCID,Chen Chengbin¹^ORCID,Zhou Licheng¹^ORCID,Zejia Liu¹^ORCID,Jiang Zhenyu¹^ORCID,Yang Bao¹^ORCID,Tang Liqun¹^ORCID

Affiliation:

1. School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province, China

Science and Technology Program of Guangzhou, China

Publisher

Informa UK Limited

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science,General Mathematics,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15376494.2023.2253548

Reference64 articles.

1. Structural Health Monitoring in mainland China: Review and Future Trends

2. Error analysis for approximate structural life-cycle reliability and risk using machine learning methods

3. A hybrid relaxed first-order reliability method for efficient structural reliability analysis

4. Reliability analysis of bridge girders based on regular vine Gaussian copula model and monitored data

5. Dynamic real-time reliability prediction of bridge structures based on Copula–BHDLM and measured stress data

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