Predictive modeling and design optimization of concrete properties reinforced with basalt fiber under harsh environment

Multi-Objective Optimization and Prediction of Mechanical Properties of Green Basalt Fiber-Reinforced Concrete Using Evolutionary ML Algorithms

An integrated machine learning framework combining evolutionary optimization, multi-objective optimization, and explainable artificial intelligence (XAI) for BFRC strength prediction and mix design optimization is proposed and incorporates a graphical user interface (GUI) deployment to enhance practical usability and support engineering decision-making.

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.

MICP and fiber-reinforced soil improvement: mechanisms, engineering performance, and machine learning prediction—a review

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

The proposed hybrid phase-field–machine learning framework enables rapid parametric studies and optimization of fiber-reinforced concrete systems within a computational engineering context.

Optimizing compression after impact prediction in CFRP using machine learning combined with classical composite laminate theory, SHAP analysis

The aerospace industry increasingly relies on modeling to accelerate composite structure development. While finite element methods (FEM) effectively simulate impact damage in carbon fiber-reinforced polymers (CFRPs), they demand specialized expertise and substantial computational resources. Machine Learning (ML) offers an efficient complement to traditional simulations while maintaining physical interpretability. This research investigates Gradient Boosting and Neural Networks for predicting impact damage characteristics (delamination area, indentation depth, residual indentation, and perforation) and compression-after-impact strength in CFRPs. A comprehensive dataset of 500 samples was developed, incorporating mechanical parameters derived from classical laminate theory. Results demonstrate that Gradient Boosting achieves superior accuracy for well-defined experimental measurements, while Neural Networks better handle outputs with greater experimental dispersion. SHapley Additive exPlanations (SHAP) analysis confirms the models’ physical interpretability, with feature importance rankings aligning with established composite laminate theory, thereby validating their potential for damage tolerance assessment in aerospace applications.

Machine Learning Models for Predicting Mechanical Properties of FRP-Confined Concrete Columns Across Low- to Ultra-High-Strength Concrete

Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness.