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

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

Recycled concrete ultra-high performance concrete (RC-UHPC) prepared from recycled concrete sand (RCS) and recycled concrete powder (RCP) represents an important pathway for achieving the high-value utilization of construction and demolition waste. However, influenced by the material properties and compositional complexity, the application of RC-UHPC requires extensive performance optimization trials, a process that is not only inefficient but also accompanied by high costs. To address this challenge, this study introduces machine learning (ML) methods to improve the design efficiency of RC-UHPC, reduce reliance on conventional experimental methods, and facilitate its engineering application. A feature system comprising 11 key input variables, including cementitious materials, admixtures, and RCS with different particle sizes, was established. The performance of various models in predicting the 28-day compressive strength (CS28) of RC-UHPC was systematically evaluated, and the main influencing factors were identified through feature analysis. The results indicate that k-nearest neighbors, random forest, and gradient boosting regression trees are the optimal ML models, with mean R2 values of 0.974 ± 0.014, 0.954 ± 0.008, and 0.971 ± 0.007, respectively, under different random seeds. Feature analysis indicates that steel fibers and fly ash have a significant positive effect on CS28. In contrast, RCS and RCP exhibit a deteriorating effect, and no significant difference is observed in the effect of RCS with different particle sizes (0–1.18 mm and 0–2.36 mm) on CS28. This study analyzes the statistical associations between RC-UHPC performance and its influencing factors from a data-driven perspective. The results can provide scientific guidance for mix design and performance regulation of RC-UHPC in engineering applications.

Machine Learning–Based Prediction of Mechanical Properties of Sustainable Concrete

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.

Synergistic Effects of

Mechanical performance optimization of concrete incorporating Crushed Brick Aggregates and manufactured sand using Response Surface Methodology

ABSTRACT The transition toward sustainable construction necessitates the strategic reuse of demolition waste as a substitute for virgin aggregates. While environmental and economic benefits are clear, the challenge lies in the inferior mechanical properties typical of recycled materials compared to natural stone. This research addresses these limitations by exploring the synergy between Crushed Brick Aggregates (CBA) and Manufactured Sand (M-Sand). By optimizing these components, the study encourages denser matrix formation and improved interfacial bonding. The objective is to engineer a moderate-strength composite that balances environmental goals with structural requirements. Fresh and hardened state characterizations, specifically workability, density, and mechanical strengths, were conducted to assess the performance of the proposed concrete. The roles of CBA and M-Sand were scrutinized via experimental protocols and enhanced through statistical modelling. Mix optimization was achieved using RSM and CCD, with model reliability confirmed through ANOVA. The interaction of variables was graphically represented in 3D response surface plots, leading to the conclusion that a 40% CBA and 60% M-Sand configuration is optimal for cost-effectiveness and sustainability. Microstructural validation through thin-section analysis evidenced a robust aggregate–matrix bond, corroborating the measured mechanical improvements.

TabPFN-Based Prediction of Concrete Compressive Strength

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.

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.