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, structures. Manual examinations, however, take lot time frequently demand too much work. In addition, there are not enough professionals to assess such structural damage. As result, in this paper, efficiency computer-vision hybrid models was investigated for automatically detecting damage reinforced concrete elements. Data-driven generated by combining wavelet scattering network (WSN) with bagged trees (BT), random subspace ensembles (RSE), artificial neural networks (ANN), quadratic support vector machines (SVM), named “BT-WSN”, “RSE-WSN”, “ANN-WSN”, “SVM-WSN”. The were trained on an image database containing 4585 images. total, 15% images different sorts used test models’ robustness adaptability; these utilized training or validation phase. WSN-SVM algorithm performed best classifying It had highest accuracy models, value 99.1% testing