A Novel Damage Detection Approach Based on Feature Extraction and Selection Using Machine Learning Without Signal Processing: A Case Study on the Tianjin Yonghe Bridge-Reference-Cited by-同舟云学术

A Novel Damage Detection Approach Based on Feature Extraction and Selection Using Machine Learning Without Signal Processing: A Case Study on the Tianjin Yonghe Bridge

Published:2023-09-25 Issue:6 Volume:47 Page:3649-3661
ISSN:2228-6160
Container-title:Iranian Journal of Science and Technology, Transactions of Civil Engineering
language:en
Short-container-title:Iran J Sci Technol Trans Civ Eng

Author:

Afsharmovahed Mohammad Hossein,Ghodrati Amiri Gholamreza^ORCID,Darvishan Ehsan

Publisher

Springer Science and Business Media LLC

Subject

Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s40996-023-01228-1.pdf

Reference24 articles.

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3. Alves VN, de Oliveira MM, Ribeiro D, Calçada R, Cury A (2020) Model-based damage identification of railway bridges using genetic algorithms. Eng Fail Anal 118:104845. https://doi.org/10.1016/j.engfailanal.2020.104845

4. Arangio S, Bontempi F (2015) Structural health monitoring of a cable-stayed bridge with Bayesian neural networks. Struct Infrastruct Eng. https://doi.org/10.1080/15732479.2014.951867

5. Aravanis TCI, Sakellariou JS, Fassois SD (2022) Damage precise localization under varying operating conditions via the vibration-data-based Functional Model method: Formulation and experimental validation. J Sound Vib. https://doi.org/10.1016/j.jsv.2022.117078

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