Facilitating the application of recycled concrete UHPC: compressive strength prediction and statistical association analysis based on explainable machine learning

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.

Effect of the recycled materials replacement rate from highway solid waste on concrete performance

This study investigates the influence of replacement rate for recycled coarse aggregate (RCA) and recycled fine aggregate (RFA) on the concrete performance in highway reconstruction and expansion projects, and provides theoretical and technical support for its application in non-load-bearing structures (NLBS) and pavements. Through the application research of RCA, the influences of different replacement rates were analyzed, including slump, compressive strength, splitting tensile strength, flexural tensile strength, and wear resistance. Combined with relevant test data, the nonlinear relationship between the replacement rate of RCA and the performance variation was summarized. The application of RFA in NLBS, the influence of RFA replacement rate on slump, compressive strength, and splitting tensile strength (STS) were discussed, and the sensitivity difference of RFA and RCA to concrete performance was compared. Research shows that when RCA is used for NLBS and pavements, the concrete slump reduces significantly when the rate exceeds 20%. In terms of mechanical properties, compressive strength decreases rapidly when the rate exceeds 50%, and STS also continues to decrease. In terms of durability, the flexural strength decreases significantly when the substitution rate exceeds 50%. For RFA in NLBS, both slump and compressive strength decrease sharply once the replacement rate exceeds 30%, while STS decreases continuously with the RFA content. Research recommends that the RCA rate does not exceed 50%, and the RFA rate does not exceed 30%.

Integrated Prediction Model for Normal and Recycled Aggregate Concrete Strength Using Ensemble Learning Techniques

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.

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.

Synergistic Effects of Brick Waste and Fly Ash on Concrete Performance: An Experimental and Computational Study

This study investigates the performance of sustainable concrete using Over-Burnt Brick Waste (OBBW) as a partial replacement for natural Coarse Aggregates (CA), with Class C fly ash (F) as a supplementary cementitious material. Concrete mixtures were prepared with OBBW replacement levels ranging from 5% to 55% at a constant 10% F content. Mechanical properties were evaluated experimentally, and statistical analysis and machine learning models were used to assess the relationships among the mix design parameters. The experimental results indicate that OBBW replacement levels of up to 50% improved mechanical performance. Specifically, the optimized concrete mix achieved a Compressive Strength (CS) of 34.65 MPa, representing a 22.7% increase over the control mix (28.24 MPa). Furthermore, flexural strength increased by 4.7%, from 3.4 MPa to 3.56 MPa. The findings demonstrate that the use of OBBW and fly ash enables the production of high-performance, eco-friendly concrete.

Synergistic Effects of