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.
Concrete compressive strength (CCS) is a critical parameter directly affecting the load-bearing capacity, durability, and overall safety of engineering structures. Traditional experimental approaches for determining CCS are time-consuming and costly, making predictive models an attractive alternative. In this study, thirteen different machine learning algorithms were applied to a well-established dataset (1030 samples, 8 input parameters) to estimate concrete compressive strength. Unlike many previous studies using the Yeh dataset that primarily emphasize prediction accuracy of individual models, this work presents a systematic multi-model comparison within a unified hyperparameter optimization framework. In addition to conventional performance metrics, permutation importance and SHAP-based explainability analyses are jointly employed, and detailed error evaluations are conducted across curing age and water-to-binder ratio subgroups to enhance engineering interpretability. Among the models tested, the CatBoost algorithm demonstrated the highest predictive performance (R² = 0.9469, RMSE = 3.70), followed closely by XGBoost, Gradient Boosting, and a stacking ensemble model. The results highlight that boosting-based machine learning models not only achieve high accuracy but also provide interpretable and robust predictions when evaluated through comprehensive error and explainability analyses.
Remzi Gürfidan, K. Erten· Bitlis Eren Üniversitesi Fen...· 0 citations
ABSTRACT Basalt Fiber Reinforced Concrete (BFRC) is being recognized as an eco-friendly advanced material with reduced environmental footprint, higher mechanical performance and long-term durability. However, its compressive strength prediction still appears to be a difficult problem due to the nonlinearity caused by the interaction of the mix components. This paper demonstrates a production-quality hybrid ML model to predict 28-day compressive strength of BFRC, providing an economical alternative to laborious and expensive laboratory-based testing. A well-defined database of 450 samples is generated involving the essential parameters, such as cement (440 kg/m3), SCMs (110 kg/m3), basalt fiber (0–4.25 kg/m3), fine aggregate (740 kg/m3), and coarse aggregate (975 kg/m3), water (121 kg/m3), superplasticizer (3.52 kg/m3), and curing period (3–365 days). The pre-processed and normalized data were partitioned into the training set (80%) and the test set (20%). Five ML models Gradient Boosting (GB), Cat Boost (CB), Light GBM (LGB), and their hybrid ensembles: GB+CB, GB+LGB were trained and compared using different metrics such as R2, RMSE, MAE, MedAE, etc. Among all ML models, the GB+LGBM model showed the best performance with R2 = 0.9445, RMSE = 5.99 MPa, MAE = 3.62 MPa, and MedAE = 2.26 MPa on the test set. SHAP analysis revealed that coarse aggregates (SHAP ≈+8) and cement (SHAP ≈+7) were the most influential factors, while the remaining water content and the excessive dosage of fiber were disadvantageous. Estimated compressive strength varied from 20 to 140 MPa. This study shows a novel approach by demonstrating the ability of ensemble ML models to capture complex concrete behavior, providing a data-driven approach for sustainable manufacturing of concrete. However, full reliance on the literature-based dataset still has significant limitations, which will increase noise, and these limitations can be further overcome by experimental validation in future studies.
Ibrahim Y. Hakeem, Abdullah Alzlfawi, M. H. R. Sobuz et al.· Matéria· 0 citations
Accurate evaluation of the mechanical performance of modified concrete is important for mixture design and engineering application. This study developed machine-learning models to predict the compressive strength (CS) and flexural strength (FS) of rubber-polymer modified concrete using 50 experimental samples. Nine variables-cement, water, fine aggregate, coarse aggregate, superplasticizer, rubber content, polymer content, polymer solid content, and rubber particle size-were used as input features. Four regression models, namely linear regression, ridge regression, RBF kernel ridge regression, and bagged linear regression, were trained for two independent prediction tasks. Model performance was assessed by fivefold cross-validation using RMSE, MAE, and R2. Within a unified framework integrating data characterization, model comparison, and post-hoc interpretation, RBF kernel ridge regression achieved the best overall performance for both targets. For CS prediction, the RMSE, MAE, and R2 were 6.656, 4.921, and 0.824, respectively; for FS prediction, the corresponding values were 1.063, 0.644, and 0.802. Correlation analysis and ridge-coefficient interpretation showed that CS was more sensitive to coarse aggregate, water, superplasticizer dosage, and polymer solid content, whereas FS was more strongly influenced by fine aggregate, water, polymer solid content, and cement. These 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
There has been a rise in the need of concrete leading to high consumption of cement and emission of carbon. Sustainable concrete incorporating supplementary cementitious materials (SCMs) is an alternative that is eco-friendly, but the mechanical behavior is complicated and hard to predict using the traditional tests. The work presents a framework involving machine learning because of predicting the mechanical properties of sustainable concrete, namely, compressive, split tensile, and flexural strength. Four models such as Linear Regression, Support Vector Regression, Random Forest, and Artificial Neural Network were constructed based on a data of nearly 1000 sustainable concrete mixes. To estimate the model performance, R 2, RMSE and MAE were used. The findings indicated that ANN and RF had the greatest prediction accuracy. The feature analysis established the most influential factors to be water content, cement dosage, replacement ratio of SCM, and curing age. The mix design approach proposed here is a fast, economical, and sustainable approach to the design of concrete mix.
Arti Chouksey, Santosh Reddy P, Priya S et al.· 2026 International Conferenc...· 0 citations
The use of supplementary cementitious materials such as fly ash can reduce environmental impacts and improve the sustainability of concrete construction. However, the nonlinear interactions among mixture design parameters make accurate prediction of concrete compressive strength challenging. In this study, TabPFN, a pre-trained foundation model for tabular data, was applied to predict the compressive strength of fly ash concrete and compared with tuned Random Forest, support vector regression, an artificial neural network, LightGBM, CatBoost, Ridge regression, and Abrams empirical regression. A dataset containing 1062 samples and eight mixture-level variables was used for model development and evaluation. Predictive performance was assessed using the coefficient of determination, mean absolute error, and root mean square error over 100 repeated random splits. The results showed that TabPFN achieved the best overall performance, with an average coefficient of determination of 0.9329, a mean absolute error of 3.2758 MPa, and a root mean square error of 4.6678 MPa. Compared with the strongest tuned gradient-boosting baseline, CatBoost, TabPFN reduced the mean absolute error and root mean square error by 0.8768 MPa and 0.8560 MPa, respectively. Furthermore, repeated-split conformal prediction demonstrated reliable uncertainty quantification, with an average prediction interval coverage probability of 0.9615 and a mean prediction interval width of 23.4554 MPa. SHAP analysis identified the water-to-cement ratio, mortar strength, and water-to-binder ratio as important variables, while additional multicollinearity and feature ablation analyses indicated that correlated ratio variables should be interpreted cautiously. The results indicate that TabPFN provides an accurate, robust, and uncertainty-aware framework for preliminary prediction of 28-day fly ash concrete compressive strength.
Zhihao Zhao, Jinjin Wang, Guohui Ma et al.· Buildings· 0 citations
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