The building and construction industry’s demand for steel reinforcement bars has increased with the rapid growth and development in the world. However, steel production contributes to harmful waste and emissions that cause environmental pollution and climate change-related problems. In light of sustainable construction practices, bamboo, a readily accessible and ecofriendly building material, is suggested as a viable replacement for steel rebars. Its cost-effectiveness, environmental sustainability, and considerable tensile strength make it a promising option. In this research, hybrid beams underwent analysis through the use of thoroughly validated finite element models (FEMs), wherein the replacement of steel rebars with bamboo was explored as an alternative reinforcement material. The standard-size beams were subjected to three-point loading using FEMs to study parameters such as the load–deflection response, energy absorption, maximum capacity, and failure patterns. Then, gene expression programming was integrated to aid in developing a more straightforward equation for predicting the flexural strength of bamboo-reinforced concrete beams. The results of this study support the conclusion that the replacement of a portion of flexural steel with bamboo in reinforced concrete beams does not have a detrimental impact on the overall load-bearing capacity and energy absorption of the structure. Furthermore, it may offer a cost-effective and feasible alternative. 

In the pursuit of creating more sustainable and resilient structures, the exploration of construction materials and strengthening methodologies is imperative. Traditional methods of relying on steel for strengthening proved to be uneconomical and unsustainable, prompting the investigation of innovative composites. Fiber‐reinforced polymers (FRPs), known for their lightweight and high‐strength properties, gained prominence among structural engineers in the 1980s. This period saw the development of novel approaches, such as near‐surface mounted and externally bonded reinforcement, for strengthening of concrete structures using FRPs. In recent decades, additional methods, including surface curvilinearization and external prestressing, have been discovered, demonstrating significant additional benefits. While these techniques have shown the enhanced performance, their full potential remains untapped. This article presents a comprehensive review of current approaches employed in the fortification of reinforced cement concrete structures using FRPs. It concludes by identifying key areas that warrant in‐depth research to establish a sustainable methodology for structural strengthening, positioning FRPs as an effective replacement for conventional retrofitting materials. This review aims to contribute to the ongoing discourse on modern structural strengthening strategies, highlight the properties of FRPs, and propose avenues for future research in this dynamic field.

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.

In order to determine the ideal degree of inclination that should be employed for constructing effective thermal energy storage systems, it is important to examine the impact of inclination angle on the melting behavior of phase change materials (PCMs) such as paraffin wax within a square cell. In consequence, this would guarantee the greatest capacity for energy release and storage. Additionally, analyzing this influence aids engineers in creating systems that enhance heat flow from external sources to the PCM and vice versa. To find out how the cell’s inclination angle affects the melting of PCM of paraffin wax (RT42) inside a square cell, a numerical analysis is carried out using the ANSYS/FLUENT 16 software. Specifically, the temperature and velocity distributions, together with the evolution of the melting process, will be shown for various inclination angles, and a thorough comparison will be made to assess the influence of inclination angle on the PCM melting process and its completion. The findings demonstrated that when the cell’s inclination angle increased from 0° to 15° and from 0° to 30° and 45°, respectively, the amount of time required to finish the melting process increased by 15%, 42%, and 71%, respectively. Additionally, after 210 min of operation, the PCM’s maximum temperature is 351.5 K with a 0° angle of inclination (horizontal) against 332.5 K with an angle of inclination of 45°.

Fiber addition enhances the composite action between the steel tube and concrete core, increasing the strength of the concrete core. To better understand how fiber-reinforced infilled steel–concrete composite thin-walled columns (SCTWCs) behave, multiple investigations have been conducted using both experimental and analytical methods. This article provides a comprehensive review of SCTWCs’ confinement approaches using fiber-reinforced polymer (FRP) and carbon fiber-reinforced polymer (CFRP). In this research, the behavior and formation of FRP and CFRP wrappings of the SCTWCs are reviewed and discussed. The ability of the FRP to serve as a confining material and reinforcement for the columns has increased its use in columns applications. The FRP can be applied to reinforce the structures from the exterior. By applying the CFRP strips, the columns’ load-carrying capacity is improved up to 30% when compared with their corresponding un-strengthened columns. External bonding of the CFRP strips efficiently creates external confinement pressure, prevents local buckling of the steel tubes, and enhances the load-carrying capacity of the SCTWCs. The primary goal is to facilitate a clear understanding of the SCTWCs. This article helps structural researchers and engineers better understand the behavior of the SCTWCs that include the FRP and CFRP composites as external reinforcement. Future research directions are also suggested, which utilize previous research works.

The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface area of the specimen, concrete cover, type of reinforcement bars, yield strength of reinforcement bars, concrete compressive strength, diameter of reinforcement bars, bond length, water/cement ratio, and corrosion level of reinforcement bars. These parameters were used to build the ANN and SVM models. The reliability of the developed ANN and SVM models have been compared with twenty analytical models. Moreover, the analyzed results revealed that the precision and efficiency of the ANN and SVM models are higher compared with the analytical models. The radar plot and Taylor diagrams have also been utilized to show the graphical representation of the best-fitted model. The proposed ANN model has the best precision and reliability compared with the SVM model, with a correlation coefficient of 0.99, mean absolute error of 1.091 MPa, and root mean square error of 1.495 MPa. Researchers and designers can apply the developed ANN model to precisely estimate the steel-to-concrete bond strength.