Aug 2026· Materials· Vol 19, pp. 3436· 0 citations· 63 references
Medicine
TL;DR
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.
Abstract
Basalt fiber-reinforced concrete (BFRC), reinforced with chopped basalt fibers having lengths ranging from 12 to 30 mm and diameters ranging from 0.013 to 0.020 mm, is a sustainable construction material with enhanced strength and durability; however, its complex nonlinear behavior makes accurate prediction and optimal mix design challenging. This study proposes an integrated machine learning framework combining evolutionary optimization, multi-objective optimization, and explainable artificial intelligence (XAI) for BFRC strength prediction and mix design optimization. The proposed framework further incorporates a graphical user interface (GUI) deployment to enhance practical usability and support engineering decision-making. Random Forest, Gradient Boosting Regressor, and XGBoost models were optimized using Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution, while NSGA-II was employed to identify optimal trade-offs between compressive strength and splitting tensile strength. SHAP analysis was applied to interpret the influence of key mix parameters on strength prediction. The optimized models achieved high prediction accuracy, with R2 values of 0.88 for compressive strength and 0.95 for splitting tensile strength, demonstrating the effectiveness of the proposed framework. The developed GUI provides a practical decision-support tool for sustainable and performance-oriented BFRC mix design.
The compressive strength of ultra-high-performance concrete (UHPC) is jointly influenced by multiple factors, including material composition, mixture proportion parameters, and curing regime. Conventional empirical methods are therefore insufficient to accurately characterize the highly nonlinear relationships involved. To improve the prediction accuracy of UHPC compressive strength and to achieve mixture proportion optimization that simultaneously considers mechanical performance, economic efficiency, and environmental impact, this study developed random forest (RF), artificial neural network (ANN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) models based on 810 publicly available UHPC experimental datasets. Model performance was evaluated using R2, RMSE, MAE, and MAPE. To enhance the robustness of model validation, repeated K-fold cross-validation, sensitivity analysis with different random seed splits, and benchmark model comparisons were further introduced. The results indicate that the XGBoost model achieved superior predictive performance on both the test set and robustness validation, with test-set R2, RMSE, MAE, and MAPE values of 0.9604, 7.77, 5.58, and 4.80, respectively. The model was further interpreted using SHAP, PDP, and ICE methods, and the results revealed that curing age, fiber content, silica fume content, and water-to-binder ratio were important variables affecting the compressive strength of UHPC. Furthermore, XGBoost was used as a surrogate model and coupled with NSGA-II and TOPSIS methods for multi-objective optimization. Under the constraints of compressive strength, water-to-binder ratio, superplasticizer-to-binder ratio, and absolute volume, a computationally recommended UHPC mixture proportion balancing strength, cost, and carbon emissions was obtained. This study provides a reproducible machine-learning-assisted approach for UHPC compressive strength prediction and low-carbon, cost-effective mixture proportion design.
Rong Li, Teng Zhou, Siyu Lu et al.· Applied Sciences· 0 citations
Basalt fiber reinforced concrete (BFRC) has recently attracted increased attention for improving durability, mechanical strength, and chemical resistance concerning harsh environmental conditions. The most noticeable gap is left in being able to predict long-term performance accurately and optimize that performance because of the complexities that arise from the multiscale interactions between fibers, matrix, and environmental stressors. This study, therefore, offers a highly unified and multiscale machine learning framework by pulling together five disaggregated analytical models into a single predictive-optimization pipeline prearranged for basalt fiber reinforced concrete. The physics-augmented graph attention transformer network (P-GATNet) is expected to embed interfacial physics within graph-based message passing to capture load-driven mechanical responses, resulting in highly accurate strength and fracture evolution predictions (e.g., flexural R² ≈ 0.97). The spectral decomposition assisted degradation model uses Hilbert-Huang-based spectral analysis, which then decouples degradation mechanisms for accurately forecasting alkali resistance and damage kinetics with an error of less than 4.5%. The multi-agent physics reinforcement optimizer (MAPRO) jointly optimizes the strength and chemical performance by modeling competing failure mechanisms via cooperative agents. For improved representation of features, the deep morphological encoder with multi-modal fusion (DME-MMF) marries image-derived morphological embeddings with experimental tabular data, thus enhancing the interpretability and accuracy of predictions. Lastly, the transformer-based inverse composite generator enables reverse material design by producing feasible basalt fiber reinforced concrete formulations that satisfy predetermined strength and durability targets at an approximate success rate of 93%. This approach improves predictive fidelity, interpretability, and design in basalt fiber reinforced concrete.
V. Vairagade· Journal of Materials Science...· 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
This paper explores fibre-reinforced composite material optimization with many objectives in a range of engineering uses. Because of their adjustable mechanical qualities and high strength-to-weight ratios, composite materials have gained widespread acceptance in the automobile, marine, aerospace, and defining sectors. The optimization of laminated composite structures presents unique challenges due to their anisotropic behaviour and numerous design variables, including fibre orientation, volume fraction, and layer sequence. While traditional approaches have focused on single-objective optimization, this research emphasizes the importance of multi-objective techniques that address the inherent trade-offs between competing factors such as weight reduction, cost efficiency, stiffness enhancement, and vibration control. Various optimization methods, including genetic algorithms, ant colony optimization, and machine learning approaches such as random forest regression, support vector regression, and ada boost regression, are explored for their effectiveness in navigating complex design spaces. The review highlights how proper optimization can significantly improve structural performance while maintaining damage tolerance and cost-effectiveness. The research contributes to the advancement of composite material design by demonstrating how systematic optimization across multiple parameters can yield significant performance improvements, addressing both mechanical requirements and stability goals in critical applications ranging from aircraft frames to wind turbine blades.
Vidhya Prasanth· Journal on Materials and its...· 0 citations
The novel compression-cast concrete (CCC) delivers superior mechanical and durability performance over conventional vibration-cast concrete (VCC), alongside economic and environmental advantages. However, its widespread adoption requires an optimized and systematic design method. This study presents a data-driven framework that integrates machine learning (ML) and multiobjective optimization for both forward prediction and inverse design of CCC. Using an experimental data set, various ML models were trained, with Optuna-optimized backpropagation neural networks (OP_BPNN) showing the best accuracy. Model interpretability was enhanced using individual conditional expectation and Shapley additive explanations. The validated OP_BPNN served as a surrogate in inverse optimization via nondominated sorting genetic algorithm III (NSGA-III), targeting compressive strength while minimizing cost and
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emissions and maximizing density. Optimal solutions were ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). Compared to VCC, the optimized CCC showed up to 15% potential reduction in cost and 38% lower
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emissions, as predicted by the model within the studied parameter range. A user-friendly graphical interface was developed to facilitate practical implementation. The framework offers a scalable tool for CCC design aligned with project-specific performance and sustainability goals.
M. Tahir, Yingwu Zhou, Biao Hu et al.· Journal of materials in civi...· 0 citations
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, and results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS.
J. Xing, Xiao Tan, Mu Guo et al.· Materials· 0 citations
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