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 eco-friendly 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.

Culverts fulfill the vital function of safely channeling water beneath railway tracks, highways, and overpasses. They serve various purposes, including facilitating drainage in areas such as watercourses, drainage zones, and regions with restricted ground-bearing capacity. Precast reinforced concrete (RC) box culverts are a popular choice because they are strong, durable, rigid, and economical. However, culverts are prone to corrosion due to exposure to a range of environmental factors and aggressive chemicals. Therefore, enhancing the design and construction of this crucial infrastructure is imperative to effectively combat corrosion and to adhere to modern standards of reliability and affordability. In this study, carbon fiber-reinforced polymer (CFRP) was used to strengthen corroded culverts, with promising potential to improve safety and longevity in these structures. This study compared the behavior of corroded RC box culverts to CFRP-strengthened ones using the finite element method (FEM). It explored the impact of varying the damage thicknesses owing to corrosion, ranging from 0 mm to 20 mm, on the structural performance of the box culverts. The results showed that the CFRP model exhibited a substantial 25% increase in the capacity and reduced the damage compared to the reference model. Moreover, a parametric study was conducted for establishing a cost-effective design, in which numerous CFRP strip configurations were examined for a damaged-culvert model. The results indicated that a complete CFRP sheet was most effective for the maximum design capacity and repair effectiveness. The study’s outcomes provide valuable insights for professionals engaged in enhancing the strength of box culverts, aiming to increase the capacity, enhance the stability, and strengthen corroded culverts.

This study emphasizes the significance of beam–column joints (BCJs) within reinforced concrete (RC) structures and investigates their performance when subjected to seismic forces. Accurately predicting the load-carrying capacity of exterior BCJs under seismic loading poses a significant challenge. The development of a reliable and user-friendly predictive model is of paramount importance for facilitating cost-effective and safe design practices for RC structures. To address this requirement, we propose an artificial intelligence (AI)-based model that utilizes gene expression programming (GEP) to accurately predict the load-carrying capacity of exterior BCJs under monotonic loading conditions. The model is developed using GEP and utilizes a database of 128 joint load-carrying capacity results of exterior BCJs obtained from a validated finite element (FE) model using ABAQUS, which considers the effects of material and geometric factors, which have often been overlooked in prior studies. These factors encompass multiple aspects, including the beam and column dimensions, concrete material properties, longitudinal reinforcements in beams and columns, and axial loads applied to the columns. This study also compared the results of the proposed GEP model with the numerical data obtained from the validated FE model, demonstrating good accuracy and reliability. The proposed model has the potential to improve the accuracy and reliability of joint load-carrying capacity predictions, thereby aiding the design of safe and cost-effective RC structures.

Structural health monitoring (SHM) is essential for ensuring the durability and safety of steel bridges, which are considered as critical part of modern infrastructure. Ensuring the integrity of bridges is vital for maintaining their functionality and safety. Considerable research has been conducted on predicting damage location in multi-span steel bridges, while relatively limited attention has been given to the identification of damage in single-span steel bridges, and there exists a notable gap in the literature regarding the quantification of damage based on severity. This study presents a novel approach combining Modal Strain Energy-based damage indices with extensively trained data-driven Artificial Neural Networks (ANNs) to achieve highly accurate damage location and quantify this damage in terms of severity in single-span steel bridges. The research integrates a finite element (FE) model as the case study of the Alamosa Canyon Bridge in Sierra County, New Mexico. The validated FE model was used to compute damage indices based on the first two bending mode shapes. The damage index (DI) of each mode for different damage scenarios simulated on the FE model is then combined using the absolute value method to pinpoint damage locations. ANNs are employed to predict severity based on these damage indices, leveraging a comprehensive dataset generated through cubic spline interpolations and FE simulations. The DI was used as an input parameter for the ANNs to estimate damage. This methodology highlights the reduced computational effort using a streamlined method for structural health monitoring that maintains high accuracy in severity prediction and damage detection. This study enhances the infrastructure safety and maintenance strategies by offering a practical framework for steel bridges.