| description abstract | Manufactured sand (MS) has been increasingly used as fine aggregate for concrete. This paper proposes a prediction of the compressive strength of concrete with manufactured sand (MS-concrete) based on an ensemble classification and regression tree (En_CART) method. A data set containing 1,350 original measured strengths of 328 concrete mixtures from actual engineering projects were used for training and testing. The cross-validation and experimental data from the literature were also used for validation, both indicating that the En_CART model provides an accurate and robust prediction. The comparison of En_CART with various machine learning methods, including artificial neural network, linear regression, Gaussian process regression, random forest, and support vector machine regressions, indicates that the En_CART model indicates superiority in predicting the compressive strength of MS-concrete. Based on the proposed model, the evolution of compressive strength is analyzed. The importance analysis indicates that age is the most significant factor influencing the compressive strength of MS-concrete, and stone powder content presents approximately 25% of the age contribution. The compressive strength of MS-concrete was found to first increase and then decrease with increasing content of MS. The optimal content of MS slightly increases with an increase in the strength level of MS-concrete. Stone powder, at certain MS content, is also found to indicate remarkable improvement in the compressive strength of MS-concrete. The optimum content of stone powder in MS is higher for MS-concrete with lower strength and lower for MS-concrete with higher strength. | |
