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Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members

After earthquakes, qualified inspectors typically conduct a semisystematic information gathering, physical inspection, and visual examination of the nation’s public facilities, buildings, and structures. Manual examinations, however, take a lot of time and frequently demand too much work. In additio...

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Main Authors: Barkhordari, Mohammad Sadegh, Barkhordari, Mohammad Mahdi, Armaghani, Danial Jahed, A. Rashid, Ahmad Safuan, Ulrikh, Dmitrii Vladimirovich
Format: Article
Language:English
Published: MDPI 2022
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/104410/1/AhmadSafuanARashid2022_HybridWaveletScatteringNetwork.pdf
http://eprints.utm.my/104410/
http://dx.doi.org/10.3390/su141912041
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spelling my.utm.1044102024-02-04T09:53:58Z http://eprints.utm.my/104410/ Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members Barkhordari, Mohammad Sadegh Barkhordari, Mohammad Mahdi Armaghani, Danial Jahed A. Rashid, Ahmad Safuan Ulrikh, Dmitrii Vladimirovich TA Engineering (General). Civil engineering (General) After earthquakes, qualified inspectors typically conduct a semisystematic information gathering, physical inspection, and visual examination of the nation’s public facilities, buildings, and structures. Manual examinations, however, take a lot of time and frequently demand too much work. In addition, there are not enough professionals qualified to assess such structural damage. As a result, in this paper, the efficiency of computer-vision hybrid models was investigated for automatically detecting damage to reinforced concrete elements. Data-driven hybrid models are generated by combining wavelet scattering network (WSN) with bagged trees (BT), random subspace ensembles (RSE), artificial neural networks (ANN), and quadratic support vector machines (SVM), named “BT-WSN”, “RSE-WSN”, “ANN-WSN”, and “SVM-WSN”. The hybrid models were trained on an image database containing 4585 images. In total, 15% of images with different sorts of damage were used to test the trained models’ robustness and adaptability; these images were not utilized in the training or validation phase. The WSN-SVM algorithm performed best in classifying the damage. It had the highest accuracy of the hybrid models, with a value of 99.1% in the testing phase. MDPI 2022-10 Article PeerReviewed application/pdf en http://eprints.utm.my/104410/1/AhmadSafuanARashid2022_HybridWaveletScatteringNetwork.pdf Barkhordari, Mohammad Sadegh and Barkhordari, Mohammad Mahdi and Armaghani, Danial Jahed and A. Rashid, Ahmad Safuan and Ulrikh, Dmitrii Vladimirovich (2022) Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members. Sustainability, 14 (19). pp. 1-15. ISSN 2071-1050 http://dx.doi.org/10.3390/su141912041 DOI:10.3390/su141912041
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Barkhordari, Mohammad Sadegh
Barkhordari, Mohammad Mahdi
Armaghani, Danial Jahed
A. Rashid, Ahmad Safuan
Ulrikh, Dmitrii Vladimirovich
Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members
description After earthquakes, qualified inspectors typically conduct a semisystematic information gathering, physical inspection, and visual examination of the nation’s public facilities, buildings, and structures. Manual examinations, however, take a lot of time and frequently demand too much work. In addition, there are not enough professionals qualified to assess such structural damage. As a result, in this paper, the efficiency of computer-vision hybrid models was investigated for automatically detecting damage to reinforced concrete elements. Data-driven hybrid models are generated by combining wavelet scattering network (WSN) with bagged trees (BT), random subspace ensembles (RSE), artificial neural networks (ANN), and quadratic support vector machines (SVM), named “BT-WSN”, “RSE-WSN”, “ANN-WSN”, and “SVM-WSN”. The hybrid models were trained on an image database containing 4585 images. In total, 15% of images with different sorts of damage were used to test the trained models’ robustness and adaptability; these images were not utilized in the training or validation phase. The WSN-SVM algorithm performed best in classifying the damage. It had the highest accuracy of the hybrid models, with a value of 99.1% in the testing phase.
format Article
author Barkhordari, Mohammad Sadegh
Barkhordari, Mohammad Mahdi
Armaghani, Danial Jahed
A. Rashid, Ahmad Safuan
Ulrikh, Dmitrii Vladimirovich
author_facet Barkhordari, Mohammad Sadegh
Barkhordari, Mohammad Mahdi
Armaghani, Danial Jahed
A. Rashid, Ahmad Safuan
Ulrikh, Dmitrii Vladimirovich
author_sort Barkhordari, Mohammad Sadegh
title Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members
title_short Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members
title_full Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members
title_fullStr Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members
title_full_unstemmed Hybrid wavelet scattering network-based model for failure identification of reinforced concrete members
title_sort hybrid wavelet scattering network-based model for failure identification of reinforced concrete members
publisher MDPI
publishDate 2022
url http://eprints.utm.my/104410/1/AhmadSafuanARashid2022_HybridWaveletScatteringNetwork.pdf
http://eprints.utm.my/104410/
http://dx.doi.org/10.3390/su141912041
_version_ 1792147733212037120
score 13.211869

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