Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members

Mohammad Sadegh Barkhordari, Mohammad Mahdi Barkhordari, Danial Jahed Armaghani (), Ahmad Safuan A. Rashid and Dmitrii Vladimirovich Ulrikh
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Mohammad Sadegh Barkhordari: Department of Civil and Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 1591634311, Iran
Mohammad Mahdi Barkhordari: School of Medicin, Kerman University of Medical Sciences, Kerman 7616914115, Iran
Danial Jahed Armaghani: Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia
Ahmad Safuan A. Rashid: School of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Dmitrii Vladimirovich Ulrikh: Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia

Sustainability, 2022, vol. 14, issue 19, 1-15

Abstract: 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.

Keywords: structural damage recognition; wavelet scattering network; support vector machine; random subspace ensemble; hybrid models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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