Enhanced support vector regression for improving accuracy in predicting shear strength in Fiber-reinforced polymer-reinforced concrete beams with FRP stirrups
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.
ABSTRACT This study establishes four popular data-driven techniques – Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB) for predicting the flexural strength (FS) of high-performance steel fiber-reinforced cementitious composites (FRCCs). A database containing 156 experimental records was used to train and test predictive models with nine feasible input variables. The results demonstrated that the GB model was the best predictor for estimating the FS of FRCCs. The GB model maintained satisfactory predictive accuracy after 10-fold cross-validation, achieving an average R2 of 0.834 on the validation folds generated from the training dataset. The predictive capability of GB model remained stable at 600 Monte Carlo simulations. The Shapley Additive Explanations (SHAP) method and partial dependence plots (PDP) indicated that fiber volume content was the most influential factor affecting FS predictions. To validate the accuracy of the GB model, a single-point case study was conducted on three specimens subjected to a three-point bending load. The discrepancy between the experimental values and GB predictions corresponded to an absolute prediction error of 1.21%, highlighting the accuracy of the developed model. Finally, a cloud-based web application was developed to provide a convenient tool for practical FS prediction of FRCCs.
Duy-Liem Nguyen, Tan-Duy Phan· Journal of Structural Integr...· 0 citations
Accurate prediction of the mechanical strength of Basalt Fiber Reinforced Concrete (BFRC) is critical for structural design, safety assessment, and the advancement of sustainable infrastructure in civil engineering. Traditional prediction methods often fail to capture the nonlinear relationships between BFRC mix proportions and resulting strength characteristics, leading to unreliable estimations. To address this limitation, this study proposes the Optimized Moment Balanced Machine (OMBM), an advanced machine learning model developed to improve the predictive accuracy of BFRC strength parameters. The model was trained and evaluated using key input features, including cement content, silica fume, fly ash, superplasticizer, water, aggregate composition, and fiber property parameters. The performance of the OMBM was benchmarked against four established machine learning models, such as Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Linear Regression (LR). Results from ten-fold cross-validation show that OMBM consistently outperforms the comparison models across five evaluation metrics. It achieved the lowest RMSE (2.411), MAE (1.788), and MAPE (4.08%), along with the highest values for correlation coefficient (R = 0.978), and coefficient of determination (R2 = 0.956). Furthermore, the OMBM achieved a Reference Index (RI) score of 1.000, which confirms its position as the leading predictive model within this comparative framework. These results confirm the robustness and reliability of the proposed OMBM model, making it a highly effective tool for accurate strength prediction of BFRC. This approach offers significant potential for the advancement of sustainable infrastructure by enabling more accurate and efficient use of concrete materials.
R. R. Khasani, Ferry Hermawan, Yuliana Usman· IOP Conference Series: Earth...· 0 citations
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.
Weijia Ye, Yihao Liang, Xiangzhou Liang et al.· Journal of reinforced plasti...· 0 citations
Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.
Yuchen Lin· International Conference on...· 0 citations
An interpretable and uncertainty-aware machine-learning framework for estimating the shear capacity of FRCM-strengthened beams enables accurate, transparent, and uncertainty-aware assessment of shear capacity in FRCM-strengthened concrete beams.
Xiangsheng Liu, G. Figueredo, G. Gordon et al.· Journal of composites for co...· 0 citations
This work addresses the simultaneous prediction of Marshall Stability (MS) and Indirect Tensile Strength (ITS) by integrating machine learning models with multi-objective optimization for the preliminary design of asphalt concrete. Based on 389 experimental samples, 15 variables were selected to describe asphalt properties, aggregate gradation, volumetric parameters and fiber characteristics, and four dual-output prediction models were developed. The models were evaluated using 50 Monte Carlo splits. TabICLv2 performed slightly better for MS prediction, with an RMSE of 1.49 ± 0.22 kN and an R2 of 0.85 ± 0.04, whereas TabPFN showed a slight advantage for ITS prediction, achieving an RMSE of 0.23 ± 0.08 MPa and an R2 of 0.91 ± 0.06. Furthermore, Pareto filtering identified nine non-dominated mixtures, and TOPSIS ranking selected the highest-ranked equal-weight compromise mixture, with MS = 15.23 kN and ITS = 3.90 MPa. The results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS, carbon fibers are more favorable for improving MS, and plastic fibers are more effective in improving ITS. Finally, a Streamlit-based graphical user interface was developed to enable real-time prediction and MS–ITS trade-off visualization, providing a reference for preliminary mix design of asphalt concrete.
J. Xing, Xiao Tan, Mu Guo et al.· Materials· 0 citations