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.
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, P. S et al.· 2026 International Conferenc...· 0 citations
Compressive strength is the single most important design parameter governing the safety, serviceability, and economy of concrete structures, yet its determination through standard 7-, 14-, or 28-day destructive cylinder/cube testing is slow, costly, and unable to assess concrete already cast in place. This study develops and evaluates a Random Forest (RF) regression model to predict the compressive strength of concrete directly from eight standard mix-design parameters — cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and curing age — using Yeh's (1998) benchmark dataset of 1,030 experimentally tested concrete mixtures. Following data cleaning, exploratory correlation analysis, an 80:20 train-test split, and five-fold GridSearchCV hyperparameter tuning, the optimized Random Forest model is benchmarked against Linear Regression, Ridge Regression, and Support Vector Regression using the coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The Random Forest model achieves the strongest predictive performance of the models tested, substantially outperforming the linear baselines and confirming that concrete strength development is governed by non-linear interactions among mix constituents. Feature importance analysis further shows that curing age and cement content are the dominant predictors, while water content exerts a clear negative influence consistent with Abrams' Law, and coarse/fine aggregates contribute comparatively little, consistent with their role as largely inert fillers. These findings demonstrate that Random Forest regression offers a fast, accurate, and interpretable, non-destructive alternative to conventional strength testing, with practical value for mix-design optimization, quality control, and early-stage structural decision-making.
M. Selvakumar, S. Geetha, P. Krishna Kumar et al.· International journal of com...· 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
A comparative framework evaluating nine regression algorithms using the UCI Concrete Compressive Strength dataset, jointly integrating correlation-corrected statistical validation, multi-model Bayesian optimization, and domain-informed feature engineering with SHAP interpretation, rarely combined in prior concrete-strength studies.
Musthafa 'Abduh Fakhruddin, Sri Winarno, Acun Kardianawati· IDEALIS : InDonEsiA journaL...· 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 evaluates variations in supplementary cementitious materials, water-cement ratios, curing times, aggregate sizes, and cement content, the predictive efficacy of five machine learning models – linear regression, random forest, support vector machine (SVM), k-nearest neighbours (KNN), and decision tree – on the compressive strength of concrete. With R2 values between 0.2767 and 0.7440, root mean squared error values ranging from 8.4935 to 17.0477, and mean absolute error values spanning 6.7134 to 14.1603, linear regression shown better accuracy. KNN did well with R2 values of 0.427 and 0.5209 in Cases 2 and 3, respectively, it performed badly in Case 4. under Case 2, the SVM obtained an excellent R2 of 0.4467. With a R2 of 0.8607, random forest performed best in Case 3, it failed in Case 4 nevertheless. Always underperforming, the decision tree showed negative R2 values and notable errors under all conditions. Hence improving model performance, the random forest and SVM obtained R2 values of 0.8607 and 0.8756, respectively. According to Shapley Additive explanations studies, ‘curing time days’ and ‘water-cement ratio’ greatly influence ‘compressive strength’. Emphasising the better predictive power of linear regression, the paper offers a thorough assessment of model efficiency and feature significance for improving concrete mixes.
Md. Zia Ul Haq, Sandeep Singh, Meena Y.R. et al.· Proceedings of the Instituti...· 0 citations