TY  - EJOU
AU  - Mohammed, Nayeemuddin 
AU  - Ayadat, Tahar 
AU  - Asiz, Andi 
AU  - Pasha, Nadeem 

TI  - Advanced Machine Learning for Sustainable Concrete Strength Prediction and Resource Optimization
T2  - Structural Durability \& Health Monitoring

PY  - 
VL  - 
IS  - 
SN  - 1930-2991

AB  - Significant efforts have been made to increase the strength of concrete by using industrial waste such as fly ash and steel slag as partial substitutes for concrete in concrete. However, predicting the concrete’s compressive strength is a challenge as it is influenced by several factors such as the shape and size of the aggregate, the water-ratio balance. This study examines the predictive capability of three deep learning models: Bagging Extreme Gradient Boosted Model (BXGBM), Deep Random Vector Functional Link (DRVFL), and Kernel Extreme Learning Machine (KELM) on the prediction for compressive strength of concrete. The dataset was split into a training and testing set, and the performance measures were analyzed. The statistical metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R<sup>2</sup>). According to model BXGBM, the MSE was found to be 3.757, and the R<sup>2</sup> of 0.9864 was as in testing, with MSE of 16.63, R<sup>2</sup> = 0.941 performed well with good accuracy. The model DRFVL has a training MSE of 29.858, R<sup>2</sup> = 0.8922, and has low overall generalizability of 46.030 and 0.8391 on the testing set. KELM also did well with a training MSE of 13.851, R<sup>2</sup> of 0.950; testing performance declined with an MSE of 31.05 and R<sup>2</sup> of 0.891. The results show that BXGBM is most trustworthy as a model that predicts the compressive strength of concrete, which allows emphasizing its high potential in applying to the practical sphere of concrete technology.
KW  - Concrete; strength; sustainable; prediction; regression; performance; optimization

DO  - 10.32604/sdhm.2026.080495