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

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.

Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches

“…The IA model provides the direct divergence of the predicted CS values. The following equation shows the parameters and their relationship in detail; Equation (16). The below equation was derived from the training database and applied to the testing and validating databases to check its credibility and reliability.…”

“…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 16 …”

“…It enhances the cohesiveness of the concrete, reduces the segregation, decreases the permeability, and decreases the cracks and fractures, creating a stronger bond within the concrete composition resulting in higher early and ultimate strength 15 . As such, the curing time is another important parameter to analyze, allowing a better understanding of the influence of time, the time in which the concrete attains its highest strength by allowing the chemical reactions to take place with water or air moist; depending on the considered method for curing the concrete specimens, on the parameters among each other and the strength of the concrete at different ages; from its early ages to its late ages 16 …”

“…It had the highest accuracy of the hybrid models, with a value of 99.1% in the testing phase. 16 This research aims to determine the impact of Nanosilica (SiO 2 ) on the mechanical properties of concrete utilizing various cement, gravel, sand, and SP combinations; FA, coarse aggregate (CA), NS, SP, and cement (C), curing age (t), CAS, FAS, and w/c, by using various models such as NLR, LR, MLR, full quadratic (FQ), Interaction (IA), and pure quadratic (PQ) analysis. Later, the best fitting model among the previously mentioned models will be nominated as the best and most reliable model to predict the CS of the concrete modified with NS.…”

“…15 As such, the curing time is another important parameter to analyze, allowing a better understanding of the influence of time, the time in which the concrete attains its highest strength by allowing the chemical reactions to take place with water or air moist; depending on the considered method for curing the concrete specimens, on the parameters among each other and the strength of the concrete at different ages; from its early ages to its late ages. 16 The cement is the most crucial component of the concrete mix design; the cement provides the required chemical reactions for the mix design to start bonding all the components together. Portland cement (OPC) is highly used in most concrete mix designs; the OPC is composed of limestone or chalk, silica sand, clay, and other materials.…”