An investigation of flexural resistance of high-performance steel fiber-reinforced cementitious composites: data-driven prediction and experimental verification

Prediction of compressive and flexural strength of modified concrete using machine learning

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.

Integrating strain-orthogonal phase-field modeling and machine learning for fracture prediction in steel fiber reinforced concrete

This paper aims to investigate the effects of steel fiber position, inclination angle and length on the fracture behavior of steel fiber reinforced concrete (SFRC), while reducing the computational burden associated with high-fidelity phase-field fracture simulations. A computational framework integrating phase-field modeling (PFM) and machine learning (ML) is developed. A strain-orthogonal phase-field formulation is employed to simulate interfacial damage and crack propagation in SFRC across different concrete grades and fiber configurations. Based on these simulations, a dataset of 357 samples is generated, comprising peak load (Pmax), critical displacement (U) and mechanical work (W). Gradient-boosting algorithms, including CatBoost, LightGBM and XGBoost, are trained and optimized using Bayesian optimization with five-fold cross-validation to construct efficient surrogate models. The results show that CatBoost consistently provides the highest prediction accuracy, achieving test R2 values of 0.999 for peak load, 0.986 for critical displacement, and 0.942 for mechanical work. The developed ML surrogates enable near-instantaneous prediction of fracture responses, offering a substantial reduction in computational cost compared with standalone phase-field simulations. Model interpretation based on SHAP reveals that matrix stiffness and initial crack length dominate peak load, while fiber inclination plays a more significant role in post-peak mechanical work and energy dissipation. This study proposes a hybrid phase-field–machine learning framework for efficient fracture analysis of SFRC. By exploiting high-fidelity numerical simulations as a data source for surrogate modeling, the proposed approach enables rapid parametric studies and optimization of fiber-reinforced concrete systems within a computational engineering context.

Machine Learning-Based Prediction of Compressive Strength in Basalt Fiber Reinforced Concrete

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.

Dynamics of Compressive Performance in Textile-Reinforced Concrete: A Machine-Learning Approach for Failure Mode and Strength Prediction

This study presents a computational failure framework that incorporates decision tree classification and machine-learning modeling to identify failure modes in textile-reinforced concrete columns and predict their strength. To achieve this, a decision tree classifier was developed using an experimentally derived dataset that considered geometry information, matrix parameters, fiber type (basalt, carbon, steel, glass, PBO), and concrete core properties to detect tensile-jacket rupture and interfacial debonding failure modes. A machine-learning model was subsequently proposed to estimate the ultimate confined compressive strength, using the unconfined concrete strength, confinement ratio, fiber tensile capacity, and matrix characteristics. The decision tree achieved an acceptable classification accuracy, revealing that the fiber ratio and concrete strength were the most important parameters for predicting the failure mode. Moreover, the developed explicit predictive expression for the design applications of the ultimate strength of columns demonstrated a suitable correlation with experimental strength measurements. The proposed methodology not only elucidates the key factors affecting failure behavior but also provides practical tools for forensic investigation and reliability-based design of fiber-reinforced confinement systems.

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.

Development of machine learning models to estimate strength characteristics of combined cellulose xanthate and fibre reinforced soft clayey soil used as subgrade