Glass fiber-reinforced polymer (GFRP) reinforcements are superior to traditional steel bars in concrete structures, particularly in vertical elements like columns, and offer significant advantages over conventional steel bars when subjected to axial and eccentric loadings. However, there is limited experimental and numerical research on the behavior of GFRP-reinforced concrete (RC) columns under eccentric loading having different spacing of stirrups. In this study, six specimens were cast under three different values of eccentricities (25 mm, 50 mm, and 75 mm) with two groups of stirrups spacing (50 mm and 100 mm). The experimental results showed that by increasing the eccentricity value, there was a reduction in the load-carrying capacity of the specimens. The finite element ABAQUS software was used for the numerical investigation of this study. The results from the finite element analysis (FEA) were close to the experimental results and within the acceptable range. The maximum difference between the experimental and FEA results was 3.61% for the axial load and 12.06% for the deformation.

This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with information on eight parameters, including geometrical parameters like the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete (f_co^'), thickness (nt), the elastic modulus of CFRP (Ef), unconfined concrete strain (?_co), confined concrete strain (?_cc) and the ultimate compressive strength of confined concrete (f_cc^'). Three metaheuristic models are implemented, including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error, and their predicted results are validated against experimental studies and finite element analysis. The study shows that the hybrid PSO model predicted the strength of CFRP-confined concrete cylinders with a maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale, time-consuming experimental tests that make the process quick and economical.

Reinforced concrete corbels were examined in this study for the cracking behavior and strength evaluation, focusing on defects typically found in these structures. A total of 11 corbel specimens were tested, including healthy specimens (HS), specimens with lower concrete strength (LC), specimens with less reinforcement ratio (LR), and specimens with more concrete cover than specifications (MC). The HS specimens were designed using the ACI conventional method. The specimens were tested under static loading conditions, and the actual strengths along with the crack patterns were determined. In the experimental tests, the shear capacity of the HS specimens was 28.18% and 57.95% higher than the LR and LC specimens, respectively. Similarly, the moment capacity of the HS specimens was 25% and 57.52% greater than the LR and LC specimens, respectively. However, in the case of the built-up sections, the shear capacity of the HS specimens was 9.91% and 37.51% higher than the LR and LC specimens, respectively. Likewise, the moment capacity of the HS specimens was 39.91% and 14.30% higher than the LR and LC specimens, respectively. Moreover, a detailed nonlinear finite element model (FEM) was developed using ABAQUS, and a more user-friendly strut and tie model (STM) was investigated toward its suitability to assess the strengths of the corbels with construction defects. The results from FEM and STM were compared. It was found that the FEM results were in close agreement with their experimental counterparts.

This study examines the vital role of artificial neural networks (ANNs) in predicting and optimizing carbon fiber-reinforced polymer (CFRP) reinforced concrete structures. ANNs excel in modeling complex non-linear relationships, making them ideal for analyzing composite materials' intricate behavior. The review covers ANNs' fundamental principles, CFRP materials, reinforcement techniques, and the challenges of coupling multiple design parameters. It investigates ANNs' potential to model these complexities through multivariate input parameters and ensemble techniques, highlighting current ANN-based practices like response surface models. The study suggests overcoming limitations with advanced feature engineering, hybrid modeling, and embracing emerging technologies. It emphasizes the transformative potential of integrating ANNs and CFRP in designing more efficient, durable, and sustainable concrete structures.

This paper presents the results of a deep neural network (DNN) model predicting the axial load-carrying capacity of fire-damaged concrete square and rectangular reinforced concrete columns subsequently repaired using carbon fiber-reinforced polymer (CFRP) composites. An experimental database comprising 143 column specimen samples was collected from the literature. Thirteen parameters were noted, including Length of Columns (mm), Width of Columns (mm), Corner Radius (mm), Height of Column (mm), Initial Compressive Strength of Concrete (MPa), Initial Tensile Strength of Steel (MPa), Longitudinal Reinforcement Ratio (As/Ag, %), Temperature of Fire (°C), Fire Exposure Time (Minutes), Number of Layers of CFRP(Count), Thickness of CFRP (mm), and Tensile Elastic Modulus of CFRP (GPa). These parameters served as inputs for the model, with the final axial compressive strength of the CFRP-repaired column considered as the target/output. DNN models were calibrated for numerous numbers of neurons in the hidden layers to achieve optimization. The proposed DNN model demonstrated strong agreement with the experimental database, exhibiting an overall correlation factor (R) of 0.99946. The predicted outcomes of the proposed model closely align with the experimental findings, as evidenced by a thorough comparison with prior experimental investigations. Consequently, this model holds potential value for engineers seeking speedy assistance in repairing fire-damaged structures using CFRP composites.

This investigation introduces advanced predictive models for estimating axial strains in Carbon Fiber-Reinforced Polymer (CFRP) confined concrete cylinders, addressing critical aspects of structural integrity in seismic environments. By synthesizing insights from a substantial dataset comprising 708 experimental observations, we harness the power of Artificial Neural Networks (ANNs) and General Regression Analysis (GRA) to refine predictive accuracy and reliability. The enhanced models developed through this research demonstrate superior performance, evidenced by an impressive R-squared value of 0.85 and a Root Mean Square Error (RMSE) of 1.42, and significantly advance our understanding of the behavior of CFRP-confined structures under load. Detailed comparisons with existing predictive models reveal our approaches' superior capacity to mimic and forecast axial strain behaviors accurately, offering essential benefits for designing and reinforcing concrete structures in earthquake-prone areas. This investigation sets a new benchmark in the field through meticulous analysis and innovative modeling, providing a robust framework for future engineering applications and research.