Abstract
Preplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile strengths of PAC, this research developed 12 supervised Machine Learning (ML) algorithms in Python software to provide estimations for civil engineers. To prepare the training and testing datasets, a comprehensive investigation was performed to prepare experimental studies on the compressive and tensile strengths of PAC. Then, according to the features of the dataset, four scenarios were defined based on the input features. The capability of ML algorithms was investigated in each scenario. Results showed that the ETR, RDF, and BR algorithms achieved the prediction accuracy of 98.3%, 95.3% and 94.6%, respectively, for estimating the compressive strength of PAC with input features of Case B. Therefore, due to the performance of the ML models, their generality was investigated by preparing the experimental test of two specimens of PAC and by validating the results. Notably, that the proposed ML models (e.g. BR method) can accurately predict the compressive and tensile strengths of specimens (e.g. with accuracy of 98.4 ∼ 99.7%, respectively) and can be used to facilitate and reduce the experimental tests as well as the experimental efforts.