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

Authors

Amir Khosrow Ghamari, Ali Seyedkazemi, Saba Jahangir, Saman Soleimani Kutanaei

Published in

Scientific reports. Jul 11, 2026. Epub Jul 11, 2026.

Abstract

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.

PMID:
42436176
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.

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