Machine learning-based prediction of preplaced aggregate concrete characteristics

• Published in: Engineering applications of artificial intelligence Vol. 123; p. 106387
• Main Authors: Moaf, Farzam Omidi, Kazemi, Farzin, Abdelgader, Hakim S., Kurpińska, Marzena
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Data-driven techniques; Machine Learning algorithm; Prediction model; Preplaced-Aggregate Concrete; Supervised learning; Preplaced-Aggregate Concrete; Machine Learning algorithm; Data-driven techniques; Supervised learning; Prediction model
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.106387

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. [Display omitted] •Twelve Machine Learning (ML) algorithms were developed to predict the compressive and tensile strengths of Preplaced-Aggregate Concrete (PAC).•Surrogate prediction models implemented in Python software have the highest prediction accuracy with capability of prediction having different input features.•Graphical User Interface (GUI) was developed to predict he compressive and tensile strengths of PAC based on data points of experimental specimens.•Using GUI mitigates the need for expensive and time-consuming experimental tests, while provides the compressive and tensile strengths of PAC with higher acceptable accuracy.