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 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.

Automated summary

This paper investigates the efficiency of computer-vision hybrid models for automatically detecting damage to reinforced concrete elements. The best-performing model, using the WSN-SVM algorithm, achieved an accuracy of 99.1% in classifying the damage.