Simplified modeling of rubberized concrete properties using multivariable regression analysis

The studies on rubberized concrete have increased dramatically over the last few years due to being an environmentally friendly material with enhanced vibration behavior and energy dissipation capabilities. Nevertheless, multiple resources in the literature have reported reductions in its mechanical properties directly proportional to the rubber content. Over the last few years, various mathematical models have been proposed to estimate rubberized concrete properties using artificial intelligence, machine learning, and fuzzy logic-based methods. However, these models are relatively complicated and require higher computation efforts than multivariable regression ones when it comes to the daily usage of practicing engineers. Additionally, most of the study has mainly focused on the compressive strength of rubberized concrete and rarely went into more details considering other properties and sample sizes. Therefore, this study focuses on developing simple yet accurate rubberized concrete multivariable regression models that can be generalized for various mixtures of rubberized concrete considering different sample sizes.