Experimental investigation and machine learning models for predicting flexural and tensile strengths of recycled concrete: Bridging the gap between sustainable materials and structural design

Abstract

The sustainability of structural components requires recycled aggregate concrete (RC) to achieve adequate flexural and split tensile strengths for practical use. These strengths depend on mix design and the properties of recycled coarse aggregates (R-CA). The variability in R-CA sources and the inherent heterogeneity of RC complicate strength predictions. No existing model accounts for this variability, leaving a gap between sustainable materials and structural design. This study develops machine learning-based models to predict the flexural and split tensile strengths of RC, regardless of R-CA source and properties. Key factors such as water absorption, effective water-to-cement ratio, coarse aggregate-to-cement ratio, and R-CA replacement ratio are used for predictions. The impact of different R-CA types on RC and natural aggregate concrete (NC) is also experimentally analyzed. A dataset of 353 test results from this study and 33 prior studies is used, and various machine learning algorithms (MLA) are evaluated. Results show a 41 % and 23 % reduction in flexural and split tensile strengths of RC compared to NC, but acid and mechanically treated R-CA can recover up to 94 % and 93 % of NC's strengths, respectively. Among all MLA models, the gradient boost model depicted the highest accuracy in predictions for the flexural and tensile strengths of both RC and NC. This research introduces new equations and a C/C++ tool for predicting RC and NC strengths, contributing to sustainable concrete design and bridging the gap between research and practical application.