Structural Damage Classification of Large-Scale Bridges Using Convolutional Neural Networks and Time Domain Responses-Reference-Cited by-同舟云学术

Structural Damage Classification of Large-Scale Bridges Using Convolutional Neural Networks and Time Domain Responses

Published:2024-08 Issue:4 Volume:38 Page:
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Chencho ¹,Li Jun²^ORCID,Hao Hong³,Li Ling⁴

Affiliation:

1. Research Associate, Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin Univ., Kent St., Bentley, WA 6102, Australia.

2. Professor, Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin Univ., Kent St., Bentley, WA 6102, Australia (corresponding author). ORCID: .

3. Professor, Earthquake Engineering Research and Test Center, Guangzhou Univ., Guangzhou, China; Professor, Centre for Infrastructural Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin Univ., Kent St., Bentley, WA 6102, Australia.

4. Professor, School of Electrical Engineering, Computing and Mathematical Sciences, Curtin Univ., Kent St., Bentley, WA 6102, Australia.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JPCFEV.CFENG-4676

Reference33 articles.

1. 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data

2. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

3. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications

4. Structural health monitoring using extremely compressed data through deep learning

5. Neural Networks for Pattern Recognition

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