Abstract This study develops a robust framework for estimating the compressive strength of self-compacting concrete (SCC) incorporating recycled aggregates using supervised machine learning (ML) techniques. A comprehensive experimental database comprising 582 concrete mix designs was used, encompassing diverse input variables including binder content, water, coarse and fine aggregates, recycled aggregate proportion, superplasticizer dosage, and curing time. Seven ML algorithms—XGBoost, CatBoost, AdaBoost, Extra Trees, Bagging Regressor, K-Nearest Neighbors, and Radius Neighbors—were systematically trained using a stratified 70/15/15 data split and optimized via grid search with five-fold cross-validation. Model performance was evaluated using coefficient of determination (R 2), root mean squared error, and MAE across training, validation, and testing datasets. Among all models, XGBoost demonstrated the highest accuracy, achieving an average R 2 of 0.9799, RMSE of 2.87 MPa, and mean absolute error of 1.97 MPa. The Permutation Feature Importance analysis revealed that binder content, water, and coarse aggregate were the most influential predictors of strength. This study confirms that ensemble ML models, particularly XGBoost, can reliably predict the compressive strength of SCC with recycled aggregates, while offering transparent insights into material behavior. The results provide a valuable tool for sustainable mix design optimization and practical implementation in eco-efficient concrete construction.
Reliable prediction of the compressive strength of self-compacting concrete with recycled coarse aggregate (SCRCAC) from mixture composition supports more rational mix design and fewer experimental tests. Using the benchmark dataset of the reference study (603 mixtures, eight input variables), this work re-examines machine-learning prediction of this property with an emphasis on honest evaluation rather than on a new model. A stacking ensemble of three gradient-boosting models (XGBoost, LightGBM, CatBoost) and an extremely randomized trees model, combined through a ridge meta-learner, is used as a representative model and compared with the four machine-learning models of the reference study (Random Forest, Extra Trees, XGBoost, LightGBM), the recent single-booster model of Abood et al., and the reference artificial neural network. Reported as the mean over 25 repeated 70/30 splits, the ensemble reaches R2 = 0.793 ± 0.038 and RMSE = 6.21 ± 0.48 MPa, above all four reference models (R2 = 0.7249–0.7635) and significantly, though only marginally, above a tuned single XGBoost. The central contribution is the evaluation itself. Because the dataset contains repeated identical compositions, a leakage-free protocol lowers the R2 of every model to between 0.60 and 0.71, showing that the values of about 0.81–0.87 usually reported are inflated by duplicate-composition leakage, and leave-one-source-out evaluation lowers it further to about 0.14. Mutual-information and partial-dependence analyses identify cement as the dominant predictor, with water acting mainly through a nonlinear dependence. Robust, leakage-aware evaluation, rather than model architecture, emerges as the key to credible strength prediction on this benchmark.
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
Recycled aggregate concrete (RAC) is a sustainable alternative construction material to reduce natural resource exploitation and manage construction and demolition waste. However, predicting the mechanical performance of RAC remains a challenge due to the high variability of recycled aggregate properties. The purpose of this study is to develop a machine learning model to predict the compressive strength of recycled aggregate-based concrete and compare its performance with normal concrete. The dataset used consists of 2165 samples (1600 normal concrete and 565 recycled aggregate concrete) collected from various scientific publications. Three tree-based machine learning algorithms (Random Forest, XGBoost, and LightGBM) were implemented and optimized using RandomizedSearchCV with 5-fold cross-validation. The results showed that LightGBM provided the best performance with R² = 0.92, MAE = 2.45 MPa, and RMSE = 3.52 MPa on the test set. This model is able to predict the compressive strength of normal concrete (R² = 0.92) and recycled aggregate concrete (R² = 0.91) with almost the same accuracy, indicating strong generalization. Feature importance analysis revealed that curing age, cement content, and water content are the most important factors in compressive strength prediction, while for RAC, recycled aggregate water absorption (WRCA) also makes a significant contribution. Error analysis shows that residuals are random and normally distributed without systematic bias. This model can reliably predict concrete compressive strength in the range of 20-60 MPa with an average error of ±3-4 MPa and can be integrated into mix proportioning design software to improve the efficiency of the design process and support the use of sustainable construction materials.
Suji’at, Eko Wahyu Abryandoko, Ocha Silvia Kencana et al.· Journal of Novel Engineering...· 0 citations
This study presents a comprehensive comparative analysis of several machine learning (ML) models for predicting the compressive strength (CS) of nano-silica (NS)-enhanced concrete. A large dataset comprising 724 experimental mix designs was compiled from the literature, including various variables such as cement content, water-to-binder ratio, fine and coarse aggregates, nano-silica content, superplasticizer content, and curing time. Six ML algorithms were developed and evaluated: Interaction Model, Full Quadratic (FQ), Artificial Neural Network (ANN), M5P-Tree, Gradient Boosting (GB), and Random Forest (RF). Model performance was assessed using R², RMSE, MAE, scatter index (SI), and objective value (OBJ). Among all models, the RF model achieved the highest predictive accuracy, followed by ANN and GB models. Sensitivity analysis revealed curing time as the most influential factor, while partial dependence plots exhibited the nonlinear effect of nano-silica quantity, with an optimal strength response about 15 kg/m3. In addition, SHAP (SHapley Additive exPlanations) analysis was employed to enhance model interpretability, confirming the dominant influence of curing age and water-to-cement ratio on compressive strength prediction. Compared with many previous studies relying on limited datasets or single-model approaches, this study provides a robust, interpretable, and generalizable ML framework for optimizing nano-silica concrete mix design. The findings highlight the strong potential of ML, particularly ensemble models combined with explainable AI techniques, to improve prediction reliability, reduce trial-and-error experimentation, and support more cost-efficient and sustainable concrete design.
Yousif J. Bas, Jamal I. Kakrasul, Kamaran S. Ismail et al.· Engineering Research Express· 0 citations
Accurate prediction of concrete compressive strength is essential for mixture design, quality control, and the broader use of supplementary cementitious materials in low-carbon construction. Fly ash concrete is particularly challenging to model because its strength development is affected by nonlinear interactions among binder composition, water–binder relationships, admixture dosage, and material characteristics. To address this problem, this study proposes a Dominant Learner with Adaptive Mixing (DLAM) framework for data-driven strength prediction. DLAM uses inner cross-validation to identify the most reliable learner from a pool of machine learning models and introduces a validation-controlled Ridge calibration step to exploit complementary information among candidate predictions. The calibration branch is adopted only when it improves the inner-validation root mean squared error (RMSE), thereby reducing the risk of unnecessary model combination and performance degradation. The framework is evaluated using a leakage-free repeated outer/inner validation protocol on a fly ash concrete dataset and is further examined on an independent public concrete strength dataset. DLAM is compared with individual learners, adaptive model-averaging baselines, and Stacking. The results show that DLAM achieves the lowest mean RMSE among the focused comparators on both datasets, with a clear improvement on the external dataset and a more modest gain on the fly ash dataset. These findings demonstrate that validation-controlled calibration provides a transparent and robust way to enhance machine-learning-based concrete strength prediction, especially when different learners capture complementary aspects of the mixture–strength relationship.
Machine learning is increasingly used to predict the compressive strength of self-compacting concrete with recycled coarse aggregate (SCRCAC), with coefficients of determination of 0.81–0.87 reported in the literature. This paper first shows that the widely used reference dataset of 603 mixtures contains only 504 unique compositions, with 84 groups of identical and 21 contradictory ones, so that identical mixtures leak between the training and test sets. Under an objective, leakage-free evaluation (with the model re-tuned on the deduplicated dataset), the coefficient of determination drops to about 0.73, a correction that applies to all models on this dataset. We then propose an interpretable, hyperparameter-optimized LightGBM model that (i) reaches the level of the best published results under the standard protocol (seed-averaged five-fold cross-validation (CV) R2 = 0.813); (ii) provides a calibrated uncertainty interval for each prediction via split-conformal prediction, achieving an empirical coverage of 0.906 at the 90% nominal level; and (iii) remains fully explainable (SHAP (SHapley Additive exPlanations), partial dependence), with cement as the dominant predictor, followed by water and the mineral admixture. Under a 70/30 protocol averaged over 25 splits, it achieves an R2 = 0.794 ± 0.038 and a root mean squared error (RMSE) = 6.20 ± 0.48 MPa, exceeding all four machine learning models of the reference study. Aspects in which the reference study retains an advantage are also discussed.
Bojana Milošević, Nenad Kojić, Milan Kragović· Journal of Composites Scienc...· 0 citations