Jul 2026· Journal of reinforced plastics and composites· 0 citations· 14 references
Abstract
Accurate prediction of the shear capacity of concrete beams reinforced with fiber-reinforced polymer (FRP) bars is hindered by large scatter and parameter-dependent bias in design models. This study compiles a database of 631 shear tests on FRP-reinforced concrete beams (499 without and 132 with FRP stirrups) and assesses nine design methods. The effects of effective depth, shear span-to-depth ratio, concrete compressive strength, and FRP stirrup ratio are quantified. Results reveal deficiencies in capturing size effects and modeling the coupling between concrete and FRP contributions, particularly for beams with small shear span-to-depth ratios and high-strength concrete. To improve predictive accuracy while retaining physical transparency, a three-stage particle swarm optimization (PSO) framework is developed to recalibrate coefficients and selected exponents of code-based shear equations. The optimized formulations substantially reduce prediction bias, coefficient of variation, and average absolute error, with the CSA S806-12-based model exhibiting the best overall performance among the formulations considered. A unified shear design equation for beams with and without FRP stirrups within the parameter ranges covered by the calibration database is proposed and examined against an independent seven-beam BFRP experimental program, providing preliminary external verification within the tested ranges and showing improved prediction consistency compared with existing design models.
The present study provides an integrated approach to the shear behavior and strength prediction of FRC beams reinforced with fiber-reinforced polymer (FRP). For this purpose, a database consisting of 68 beam test results in the literature for the assessment of 10 existing shear strength models and design code provisions for FRC beams reinforced by FRP reinforcement, together with the newly proposed analytical model that has been validated using 51 test data points and checked using the remaining 17 test data points for verification. From the assessment using the database approach, the study concluded that shear performance can be increased by increasing the fiber volume fraction by 217%, while steel fibers combined at the micro- and macro-scales offer the best performance for FRC beams reinforced with FRP reinforcement. From the assessment of the various shear strength models and design code provisions for FRC beams reinforced by FRP reinforcement using the database approach, the study concluded that the newly proposed model together with three existing models and design code provisions can provide more consistency and safety compared to the existing shear strength models by providing an average safety margin of 62%, while the rest of the existing shear strength models show significant variability for the assessment of the shear performance of FRC beams reinforced by FRP reinforcement. In order to validate the trends provided by the newly proposed model for the shear performance prediction of FRC beams reinforced by FRP reinforcement, FRC beams reinforced by 0.4% and 0.8% recycled strap fibers were experimentally tested and validated using the ABAQUS code. In this study the data from 21 references from the literature has been extracted to predict and test the analytical investigation presented.
B. R. Hassan, A. Manguri, Amjad Burhan Hussein et al.· Innovative Infrastructure So...· 0 citations
This study investigates the compressive performance of fiber-reinforced polymer (FRP) rebar-reinforced Seawater and Sea Sand Concrete (SSC) columns through an integrated approach combining finite element analysis, theoretical derivation, and machine learning. Finite element models were developed to quantify the influence of key parameters on the ultimate bearing capacity and lateral deflection. The results indicate that the compressive capacity decreases significantly with increasing eccentricity and slenderness ratio. Columns reinforced with steel rebars demonstrated superior load-bearing and anti-lateral displacement capabilities compared to their FRP-reinforced counterparts. A theoretical formula for predicting the compressive capacity was derived; however, it systematically overpredicted the experimental measurements by approximately 36%. To develop data-driven predictive models for the ultimate load capacity of FRP–SSC columns, four machine learning models, backpropagation neural network (BPNN), bootstrap aggregating BPNN (Bagging-BP), genetic algorithm-optimized BPNN (GA-BP), and gradient boosting regression trees (GBRT), were employed. Using sectional dimension, concrete strength, reinforcement parameters, eccentricity, and slenderness ratio as inputs, the validation sets of the models achieved R-values of 0.942, 0.918, 0.933, and 0.990, respectively. Feature importance analysis based on SHAP identified eccentricity as the most influential parameter. Results from this work can help to understand the behavior of FRP–SSC columns under compression.
Qinghai Xie, Qu-Cheng Xu, Jia-Le He et al.· Buildings· 0 citations
This study presents a comprehensive analytical evaluation of the axial compression behavior of fiber-reinforced polymer–reinforced concrete (FRP-RC) columns. The investigation focuses on the combined effects of transverse confinement, FRP material type, column geometry, and concrete compressive strength, while explicitly considering both short and slender column configurations to evaluate the influence of geometric slenderness on structural response. The compiled experimental database covers concrete strengths ranging from approximately 10 to 90 MPa and includes columns reinforced with glass fiber–reinforced polymer (GFRP) and carbon fiber–reinforced polymer (CFRP) longitudinal bars and transverse reinforcement. The database analysis indicates that reducing spiral pitch from relatively wide spacing (100–120 mm) to dense configurations (35–40 mm) leads to a significant increase in normalized axial strength (approximately 50–100%) and enhances post-peak stability. Increasing concrete compressive strength from 30 to 50 MPa is associated with an increase in peak load of approximately 20–40%, accompanied by a reduction in ductility of about 25–35%. Columns reinforced with CFRP generally exhibit higher axial capacity than their GFRP counterparts, with observed strength gains in the range of 15% to 50%, primarily due to the higher stiffness and confinement efficiency of CFRP. Geometric effects are also pronounced. Circular columns tend to provide 10–25% higher normalized capacity compared to square sections. In addition, increasing column slenderness (higher L/D ratio) is associated with reductions in axial strength of approximately 20–30%, reflecting the influence of stability and second-order effects. A nonlinear finite element model was employed to support and extend the experimental trends observed in the database analysis. The numerical simulations captured consistent behavioral patterns, confirming the sensitivity of peak load and post-peak response to confinement intensity, concrete strength, FRP stiffness, and slenderness ratio. Comparisons with common design provisions indicate generally good agreement between predicted and experimental strengths, with experimental-to-predicted ratios typically ranging from 0.95 to 1.10, although both conservative and unconservative predictions are observed, particularly for lightly confined or slender columns. The combined experimental synthesis and numerical validation provide a unified understanding of the governing mechanisms controlling the axial behavior of FRP-RC columns and offer a strengthened basis for future refinement of design recommendations.
Mohammad Awad· Discover Civil Engineering· 0 citations
Fiber-reinforced polymer (FRP)-reinforced concrete beams have increasingly applied in construction. Accurate prediction of shear strength in FRP-reinforced concrete beams with FRP stirrups is essential for safe and efficient structural design. This study aims to improve prediction accuracy by developing an enhanced support vector regression (SVR) model with the aid of an optimization algorithm, and a reliable dataset was evaluated using a 10-fold cross-validation approach. The proposed model achieved strong predictive capability with low error values. The enhanced SVR model with 200 wolves and 100 iterations provides the best performance, with RMSE, MAE, and MAPE values of 28.14kN, 19.27kN, and 15.40%, respectively, along with a correlation coefficient of 0.955. Furthermore, the optimized models outperformed conventional SVR models with different kernel functions. These findings indicate that the enhanced SVR approach is a reliable and effective tool for predicting shear strength, thereby supporting improved structural analysis and design in engineering practice.
T. Truong, Minh-Thao Le, Ngoc-Tri Ngo et al.· Journal of Science & Technol...· 0 citations
Accurate prediction of the shear strength of reinforced concrete (RC) beams remains a challenging problem due to the complex nonlinear interactions among material properties, reinforcement characteristics, and beam geometry. This study presents a data-driven artificial neural network (ANN) framework for predicting the shear strength of RC beams using a systematically curated experimental database comprising 1,977 specimens collected from published literature. The database was preprocessed to remove incomplete and duplicate records, and the optimal ANN architecture was selected using the total goodness function. Model performance was evaluated using 10-fold cross-validation together with multiple statistical metrics, including the coefficient of determination (R²), mean absolute error, root mean squared error, bias, and prediction interval. The ANN achieved R² values of 0.9968 and 0.9686 for the representative training and testing datasets, respectively, and an overall R² of 0.993, with 1,668 predictions falling within the ± 30% error criterion. Comparative evaluation with the Canadian Standards Association (CSA), American Concrete Institute 318 (ACI 318), and Eurocode 2 (EC2) design-code models demonstrated that the proposed ANN consistently achieved superior predictive accuracy and reliability. Parametric analyses further confirmed that the predicted trends agree with established reinforced concrete shear mechanics, highlighting the dominant influence of the shear span-to-depth ratio, beam geometry, and reinforcement ratio on shear resistance. The proposed framework provides an accurate and robust decision-support tool that complements conventional design-code methods for predicting the shear strength of RC beams.
F. Khalid, Milad Razbin, S. Fareed et al.· Scientific Reports· 0 citations
The arrangement of shear studs in steel–concrete composite beams (SCCBs) significantly influences both mechanical performance and economic efficiency. However, existing optimization studies have predominantly focused on cross-sectional dimensions and material grades, with systematic optimization of the mechanical parameters and spatial layout of shear connectors remaining largely unexplored. This study proposes a framework that couples machine learning surrogate models with the NSGA-II algorithm to address this gap. For a continuous SCCB, stud spacing, shear stiffness, and pull-out stiffness are adopted as optimization variables. The surrogate-assisted NSGA-II is employed to generate the Pareto front, and the analytic hierarchy process (AHP) combined with the technique for order preference by similarity to ideal solution (TOPSIS) is subsequently applied to identify the global optimal solution. The results demonstrate that the multilayer perceptron (MLP) delivers the best overall predictive accuracy. Feature importance analysis reveals that the shear stiffness of the studs over the pier, denoted KS-P, dominates the axial force response of the deck slab, contributing 48% of the total importance. After optimization, the deflection increases by 3.9%, whereas the axial force in the concrete slab decreases by 11.7% and the stud consumption is reduced by 88%, confirming the effectiveness and engineering practicality of the proposed method. The proposed methodology provides a theoretical basis and technical support for the refined design of shear connectors in SCCBs.