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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI
2022
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/104410/1/AhmadSafuanARashid2022_HybridWaveletScatteringNetwork.pdf |
| _version_ | 1796867651865149440 |
|---|---|
| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T21:30:29Z |
| format | Article |
| id | utm.eprints-104410 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:30:29Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-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 |
| 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 |
| title | 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_short | 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 |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/104410/1/AhmadSafuanARashid2022_HybridWaveletScatteringNetwork.pdf |
| work_keys_str_mv | AT barkhordarimohammadsadegh hybridwaveletscatteringnetworkbasedmodelforfailureidentificationofreinforcedconcretemembers AT barkhordarimohammadmahdi hybridwaveletscatteringnetworkbasedmodelforfailureidentificationofreinforcedconcretemembers AT armaghanidanialjahed hybridwaveletscatteringnetworkbasedmodelforfailureidentificationofreinforcedconcretemembers AT arashidahmadsafuan hybridwaveletscatteringnetworkbasedmodelforfailureidentificationofreinforcedconcretemembers AT ulrikhdmitriivladimirovich hybridwaveletscatteringnetworkbasedmodelforfailureidentificationofreinforcedconcretemembers |