An Artificial Neural Network-Based Approach to Predict Compressive Strength in Rubberized Concrete Using Experimental Datasets

Abstract

The incorporation of recycled rubber as a partial replacement for fine aggregate in concrete has emerged as a promising approach for enhancing sustainability in construction. By diverting rubber waste from landfills and reusing it in construction materials, the preservation of ecosystems is promoted, and the demand for extracting and processing natural resources is reduced. However, accurately predicting the compressive strength of such rubberized concrete presents a significant challenge due to the complex relationship between different factors involved. Lately, artificial neural networks (ANNs), a type of artificial intelligence technique, have gained significant popularity for their effectiveness in predicting complex problems. ANNs have been commonly employed in recent years owing to their exceptional ability to recognizing patterns, adaptability, and learning capability. This paper proposes a novel approach to address this issue by utilizing the power of ANNs for compressive strength of rubberized concrete prediction. A thorough dataset from the laboratory is employed to train, test, and validate ANNs. By engaging in a process of training, the ANNs effectively capture the complex interrelationships within the data. This enables ANNs to make accurate predictions of the compressive strength of rubberized concrete. The proposed approach offers flexibility to accommodate different rubber substitution levels and provides a reliable tool for optimizing the mix design parameters to achieve desired strength requirements. The results demonstrate the efficiency of ANNs in precisely predicting the compressive strength of concrete with waste rubber, facilitating sustainable and efficient construction practices. In summary, this research contributes to the utilization of recycled rubber in concrete production and provides valuable insights for predicting the compressive strength of rubberized concrete.