Predicting the Compressive Strength of Rubberized Concrete Using Artificial Intelligence Methods

Amedeo Gregori, Chiara Castoro and Giri Venkiteela
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Amedeo Gregori: Department of Civil, Building and Environmental Engineering, University of L’Aquila, Via G. Gronchi 18, 67100 L’Aquila, Italy
Chiara Castoro: Department of Civil, Building and Environmental Engineering, University of L’Aquila, Via G. Gronchi 18, 67100 L’Aquila, Italy
Giri Venkiteela: New Jersey Department of Transportation, 1035 Parkway Avenue, P.O. Box 600, Trenton, NJ 08625-0600, USA

Sustainability, 2021, vol. 13, issue 14, 1-16

Abstract: In this study, support vector machine (SVM) and Gaussian process regression (GPR) models were employed to analyse different rubbercrete compressive strength data collected from the literature. The compressive strength data at 28 days ranged from 4 to 65 MPa in reference to rubbercrete mixtures, where the fine aggregates (sand fraction) were substituted with rubber aggregates in a range from 0% to 100% of the volume. It was observed that the GPR model yielded good results compared to the SVM model in rubbercrete strength prediction. Two strength reduction factor (SRF) equations were developed based on the GPR model results. These SRF equations can be used to estimate the compressive strength reduction in rubbercrete mixtures; the equations are provided. A sensitivity analysis was also performed to evaluate the influence of the w/c ratio on the compressive strength of the rubbercrete mixtures.

Keywords: rubbercrete; strength reduction factor (SRF); artificial intelligence methods; Gaussian process regression (GPR); support vector machine (SVM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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