sustainability Article Predicting the Compressive Strength of Rubberized Concrete Using Artiﬁcial Intelligence Methods Amedeo Gregori 1 , Chiara Castoro 1, * and Giri Venkiteela 2   Citation: Gregori, A.; Castoro, C.; Venkiteela, G. Predicting the Compressive Strength of Rubberized Concrete Using Artiﬁcial Intelligence Methods. Sustainability 2021, 13, 7729. https://doi.org/10.3390/su13147729 Academic Editors: Raf Dewil, Rawaz Kurda and Hawreen Hasan Ahmed Received: 3 June 2021 Accepted: 7 July 2021 Published: 11 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Civil, Building and Environmental Engineering, University of L’Aquila, Via G. Gronchi 18, 67100 L’Aquila, Italy; amedeo.gregori@univaq.it 2 New Jersey Department of Transportation, 1035 Parkway Avenue, P.O. Box 600, Trenton, NJ 08625-0600, USA; giri.venkiteela@dot.nj.gov * Correspondence: chiara.castoro@graduate.univaq.it 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) 1. Introduction Waste tyre disposal represents a growing environmental problem, not to be overlooked. Globally, more than 500 million units of waste tyres are discarded every year without any treatment [1] and their increasing number has raised concerns worldwide due to the threat they pose directly and indirectly to human health and the environment. For this reason, recycling of waste tyres has been implemented in many countries. The possibility of recycling scrap tyres as aggregates in concrete gained acceptance worldwide in the engineering sector, and positive results have already been achieved, preserving natural resources and helping to maintain ecological balance. Scrap tyres undergo several processes to separate the steel wires from the rubber and to reduce the rubber to smaller crumbs. This crumb rubber can then be added into concrete mixture as partial replacement of the natural aggregates [2], modifying the concrete properties [3–11]. In some cases, cleaned, shredded rubber can be used. For example, the textile com- ponents are removed, steel ﬁbres are pulled out, and the rubber surface is sometimes subjected to pre-treatments to consolidate the adhesion with the cement paste, improving the ﬁnal properties of the modiﬁed concrete. The size, shape, and level of cleanliness of the fragments of rubber are essential factors in deﬁning the ﬁnal characteristics of the material. The resulting material is called rubbercrete, a lightweight concrete with speciﬁc me- chanical, thermal, acoustic, and rheological characteristics. Rubbercrete exhibits numerous beneﬁts compared to conventional concrete, such as lower density [12], increased ductil- ity [13], enhanced plastic capacity [14], higher toughness [15], higher impact resistance [16], better resistance to chloride penetration [17], lower thermal conductivity [2], higher noise reduction [18], and better electrical resistivity [19]. Sustainability 2021, 13, 7729. https://doi.org/10.3390/su13147729 https://www.mdpi.com/journal/sustainability