A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network-Reference-Cited by-同舟云学术

A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network

Published:2022-03-13 Issue:2 Volume:7 Page:136-150
ISSN:2470-5314
Container-title:Journal of Structural Integrity and Maintenance
language:en
Short-container-title:Journal of Structural Integrity and Maintenance

Author:

Gharehbaghi Vahid Reza¹,Kalbkhani Hashem²,Noroozinejad Farsangi Ehsan³,Yang T.Y.⁴,Nguyen Andy¹,Mirjalili Seyedali⁵⁶,Málaga-Chuquitaype Christian⁷

Affiliation:

1. School of Civil Engineering and Surveying, University of Southern Queensland, Springfield Campus, Springfield, QLD, Australia

2. Department of Electrical Engineering, Urmia University of Technology, Urmia, Iran

3. Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran

4. Department of Civil Engineering, University of British Columbia, Vancouver, Canada

5. Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Brisbane, Australia

6. Yonsei Frontier Lab, Yonsei University, Seoul, Korea

7. Department of Civil and Environmental Engineering, Imperial College London, London, UK

Publisher

Informa UK Limited

Subject

Mechanical Engineering,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/24705314.2021.2018840

Reference32 articles.

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

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

3. Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

4. Material and Structural Destruction of Concrete Elements in the Industrial Environment

5. Wind Power Forecasting Methods Based on Deep Learning: A Survey

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