Research advances in machine learning for structural state identification and condition assessment

HUANG Yong; BAO Yuequan; LI Hui

doi:10.6052/1000-0992-23-018

Volume 53 Issue 4

Dec. 2023

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Huang Y, Bao Y Q, Li H. Research advances in machine learning for structural state identification and condition assessment. Advances in Mechanics, 2023, 53(4): 774-792 doi: 10.6052/1000-0992-23-018

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Huang Y, Bao Y Q, Li H. Research advances in machine learning for structural state identification and condition assessment. Advances in Mechanics, 2023, 53(4): 774-792 doi: 10.6052/1000-0992-23-018

Huang Y, Bao Y Q, Li H. Research advances in machine learning for structural state identification and condition assessment. Advances in Mechanics, 2023, 53(4): 774-792 doi: 10.6052/1000-0992-23-018

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Research advances in machine learning for structural state identification and condition assessment

doi: 10.6052/1000-0992-23-018

School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China

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Corresponding author: baoyuequan@hit.edu.cn
Received Date: 2023-05-23
Accepted Date: 2023-12-15

Available Online: 2023-12-19

Publish Date: 2023-12-30

Abstract

Abstract

Structural health monitoring (SHM) has become an important technique to ensure the safety of major engineering structures by sensing, collecting, transmitting and processing multivariate data, through the installation of multiple types of sensors on large engineering structures. With the wide application of SHM system, a huge amount of monitoring data is generated, and how to identify and evaluate the structural condition and safety through monitoring data is one of the core scientific problems. Due to the complexity of civil engineering structures, the core difficulty of state identification and assessment is the optimization and solution of high-dimensional problems. Machine learning has a strong capability in solving high-dimensional problems, providing new ideas for the solution of this problem. This paper focuses on the research progress of machine learning in structural modal identification, damage identification and reliability assessment, and discusses the future development trend in these research directions.
- structural health monitoring,
- modal identification,
- damage identification,
- reliability evaluation,
- machine learning

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References(56)

References

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