The present work demonstrates the nonlinear finite element analysis (NLFEA) of 13 concentrically and eccentrically loaded short rectangular concrete column specimens reinforced with GFRP and conventional steel bars. GFRP bars are lightweight having the high tensile strength and high corrosion resistance. An NLFEA model for the rectangular concrete specimens was developed using the commercial software ABAQUS Standard and calibrated for different materials and geometric parameters based on the previous experimental test results of the studied specimen. The behavior of reinforced concrete was modelled using the concrete damaged plasticity (CDP) model, the behavior of steel bars was simulated as a bilinear elastoplastic material, and the GFRP bars were considered as a linear elastic material. After the calibration of CDP parameters, the control sample was used for the further numerical parametric analysis to investigate the effect of critical parameters, i.e., the area of concrete (A_c), the compressive strength of concrete (), and the ratio of longitudinal reinforcement (ρ_l) and transverse reinforcement (ρ_t) on the load‐carrying capacity of columns. The results show that the selected NLFEA model can simulate the behavior of columns accurately and there was good agreement of numerical results obtained from ABAQUS Standard with the experimental results.

Asphalt pavement distresses are the major concern of underdeveloped and developed nations for the smooth running of daily life commute. Among various pavement failures, numerous research can be found on pothole detection as they are injurious to automobiles and passengers that may turn into an accident. This work is intended to explore the potential of deep learning models and deploy three superlative deep learning models on edge devices for pothole detection. In this work, we have exploited the AI kit (OAK‐D) on a single‐board computer (Raspberry Pi) as an edge platform for pothole detection. Detailed real‐time performance comparison of state‐of‐the‐art deep learning models and object detection frameworks (YOLOv1, YOLOv2, YOLOv3, YOLOv4, Tiny‐YOLOv4, YOLOv5, and SSD‐mobilenetv2) for pothole detection is presented. The experimentation is performed on an image dataset with pothole in diverse road conditions and illumination variations as well as on real‐time video captured through a moving vehicle. The Tiny‐YOLOv4, YOLOv4, and YOLOv5 evince the highest mean average precision (mAP) of 80.04%, 85.48%, and 95%, respectively, on the image set, thus proving the strength of the proposed approach for pothole detection and deployed on OAK‐D for real‐time detection. The study corroborated Tiny‐YOLOv4 as the befitted model for real‐time pothole detection with 90% detection accuracy and 31.76 FPS.

In the current work, a new strength model was proposed for capturing the ultimate axial strength of fiber reinforced polymers (FRP)-confined concrete compression members. First, a preliminary evaluation of previous strength models for estimating the wrapping effect of FRPs-confined concrete specimens was performed a large database of 520 confined concrete specimens was constructed with the detailed information of various geometric and material parameters of confined concrete specimens. Then, a strength model was suggested using regression analysis in MATLAB. The validation of the proposed strength model was made in two different approaches. In the first approach, the validation of proposed models was done by performing an experimental work of 6 carbon fiber reinforced polymer (CFRP) confined concrete cylinders and in the second approach, the nonlinear finite element analysis (NLFEA) via a general-purpose program ABAQUS was performed. After validation, a detailed parametric numerical study was done using the newly suggested strength model. A close agreement could be found between tests, NLFEA and theoretical results.

Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including GoogLeNet, MobileNet-V2, Inception-V3, ResNet18, ResNet50, ResNet101, and ShuffleNet, for crack detection and categorization. Images are classified as diagonal crack (DC), horizontal crack (HC), uncracked (UC), and vertical crack (VC). Each architecture is trained with 32,000 images equally divided among each class. A total of 100 images from each category are used to test the trained models, and the results are compared. Inception-V3 outperforms all the other models with accuracies of 96%, 94%, 92%, and 96% for DC, HC, UC, and VC classifications, respectively. ResNet101 has the longest training time at 171 min, while ResNet18 has the lowest at 32 min. This research allows the best CNN architecture for automatic detection and orientation of cracks to be selected, based on the accuracy and time taken for the training of the model.

Recently, the use of fiber-reinforced polymers (FRP)-confinement has increased due to its various favorable effects on concrete structures, such as an increase in strength and ductility. Therefore, researchers have been attracted to exploring the behavior and efficiency of FRP-confinement for concrete structural elements further. The current study investigates improved strength and strain models for FRP confined concrete cylindrical elements. Two new physical methods are proposed for use on a large preliminary evaluated database of 708 specimens for strength and 572 specimens for strain from previous experiments. The first approach is employing artificial neural networks (ANNs), and the second is using the general regression analysis technique for both axial strength and strain of FRP-confined concrete. The accuracy of the newly proposed strain models is quite satisfactory in comparison with previous experimental results. Moreover, the predictions of the proposed ANN models are better than the predictions of previously proposed models based on various statistical indices, such as the correlation coefficient (R) and mean square error (MSE), and can be used to assess the members at the ultimate limit state.