Prediction of Fresh and Mechanical Properties of Self-Compacting Concrete Using Gaussian Process Regression

Explainable Random Forest Framework for Predicting Compressive Strength of Sustainable Concrete Incorporating Industrial Waste Materials

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.

Experimental investigation of the mechanical and durability performance of hybrid basalt fiber-reinforced concrete using gaussian process regression-based prediction

Comparative Analysis of AI and Statistical Models for Predicting Mechanical and Durability-Related Properties of Alkali-Activated Recycled Aggregate Concrete

Alkali-activated recycled aggregate concrete (AARAC) offers a sustainable alternative to traditional concrete but suffers from complex, non-linear mechanical behavior that challenges conventional prediction methods. This study develops and compares five machine learning models, linear regression (LR), M5P, Random Forest (RF), K-Nearest Neighbors (KNN) and XGBoost, for predicting the compressive strength (Cs), flexural strength (Fs), splitting tensile strength (Ss), pull-out bond strength (PT), and water absorption (Wa%) of AARAC. A dataset of 360 experimental samples, incorporating natural aggregate, recycled concrete aggregate (RCA), cement block aggregate (CBA), water-to-cement ratio (W/C), alkaline treatment status, and slump, was used. Models were evaluated via train/test split (80/20) and 10-fold cross-validation using R2, MAE, RMSE, and MAPE. Random Forest achieved the highest test R2 (0.8736) and lowest test MAPE (1.418%) and XGBoost (R2 = 0.8605, MAPE = 1.557%). KNN and M5P performed moderately, while LR was the weakest (R2 = 0.6958, MAPE = 2.147%). All tree-based models exhibited overfitting, with training R2 up to 0.98. Scatter plot analysis revealed systematic underprediction by RF for Cs (constant offset of ~2 MPa) and increasing bias for PT, Ss, and Wa% at higher values. XGBoost gave perfect predictions for PT and Wa% but underpredicted Cs and Fs. K-fold cross-validation confirmed XGBoost as the most robust (mean R2 = 0.9844). Correlation analysis showed W/C strongly increases Wa% (r = 0.80) and decreases PT (r = −0.73); RCA negatively affects mechanical properties, while CBA and alkaline treatment improve them. The study concludes that ensemble tree models, particularly Random Forest, are superior for AARAC prediction, but systematic bias requires post hoc calibration.

Data-driven fresh property prediction and mix design optimization of self-compacting geopolymer concrete

Self-compacting geopolymer concrete (SCGC) requires coordinated control of workability and strength during mix design, yet predicting fresh-state properties from mix proportions alone has not been reliably achieved. This study trained machine learning models on 327 two-part SCGC mixes from 37 published sources to predict five fresh properties (slump flow, T500, V-funnel time, L-box ratio, J-ring step) and 28-day compressive strength (CS₂₈d). Curing temperature, pre-demoulding duration, and post-curing regime were added as inputs for CS₂₈d, giving 15 inputs total. Five tree-based algorithms (random forest, gradient boosting, extra trees, XGBoost, LightGBM) were compared, with hyperparameters tuned via RandomizedSearchCV or Optuna. Under random-split cross-validation, extra trees achieved CV R² of 0.949 (V-funnel), 0.910 (L-box), and 0.934 (J-ring); gradient boosting led for slump flow at CV R² = 0.929. Under Leave-One-Source-Out (LOSO) validation; which withholds entire laboratories from training; R² fell to 0.261 for slump flow and 0.120 for CS₂₈d; T500 reached R² = −0.279. The resulting gap, ΔR² = 0.67–0.92 across outputs, measures the inter-laboratory information leakage that random-split validation conceals and that prior SCGC ML studies have not corrected for. Adding the curing inputs raised CS₂₈d test R² by 0.119. SHAP and permutation importance analysis identified curing temperature as the dominant driver of CS₂₈d and produced physically consistent rankings for fresh property outputs. The trained models served as surrogates in a differential evolution framework that simultaneously optimises for EFNARC workability classes (SF2/SF3, PA2) and minimum compressive strength targets, tested for ambient (25°C, 24 h) and oven (70°C, 48 h) curing across six strength thresholds. Eleven of twelve scenarios returned feasible solutions, with total binder content from 362 to 574 kg/m³.

Investigation of Mechanical Behavior, Plastic Shrinkage, and Microstructure of Self-Compacting Geopolymer Concrete and The Effects of Single-Component and Two-Component Fibers

Substituting aluminosilicate pozzolans for cement offers a stable and environmentally sustainable mixture. The primary innovation lies in incorporating polypropylene and steel fibers into self-compacting mixtures to enhance mechanical properties and durability, coupled with artificial intelligence modeling (artificial neural networks and decision trees) for compressive strength prediction. This integrated experimental and computational methodology enables more precise optimization of mix designs.The methodology involved producing 16 mix designs with alkaline concentrations of 8 and 12 M, variations in pozzolan quantities, and fiber volumes (0.5 to 1% by volume). Samples underwent ambient and heat curing, with evaluations of fresh properties (slump flow, L-box, V-funnel), mechanical properties (compressive and tensile strength), water absorption, shrinkage, and microstructure (SEM and XRD). Modeling was performed using experimental data to predict compressive strength. Results indicated that increasing molarity from 8 to 12 densifies the concrete microstructure, boosting compressive strength by up to 25% (from 40 to 96.83 MPa at 28 days), although alkaline solution costs may rise. Steel fibers, by forming resilient internal networks, improved tensile and flexural strength by up to 30% and reduced drying shrinkage by 11% . Hybrid fiber combinations enhanced high-temperature durability, retaining 80% strength at 600°C. The AI models achieved accuracies exceeding 88% , positioning this concrete as a sustainable and efficient alternative to Portland cement concrete.

Predicting the compressive strength of sustainable concrete with recycled foundry sand using advanced soft computing methods.

Given the environmental challenges posed by the production and disposal of industrial waste, reusing such materials in the construction industry, especially for the development of sustainable concrete, offers an eco-friendly solution and cost reduction. This study investigates the use of waste foundry sand (WFS) as a partial replacement for fine aggregates in concrete. To accurately predict the compressive strength (fc) of WFS-containing concrete, a comparative modeling framework was employed by using one traditional statistical method, Response Surface Methodology (RSM), alongside two advanced soft computing techniques, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP). A dataset consisting of 397 laboratory samples, including various mix design parameters and curing ages as input variables, with fc as the output, was utilized to train and evaluate the models. The results indicate that the RSM model showed the best predictive performance. The fitted model achieved RMSE and MAE values of 4.289 MPa and 3.583 MPa, respectively. Under LOOCV validation, the corresponding errors were RMSECV = 5.40 MPa and MAECV = 4.19 MPa, indicating good generalization capability and stable prediction of compressive strength for concrete containing WFS. The correlation coefficient (R = 0.83) is reported as a secondary performance indicator, indicating a moderate level of agreement between predicted and experimental values. Additionally, sensitivity analysis of input variables indicated that the water-to-cement ratio and superplasticizer-to-cement ratio had the greatest impact on fc, while the WFS-to-cement ratio (WFS/C) and the WFS-to-fine aggregate ratio (WFS/FA) showed a relatively lower influence.