Rubberized concrete is becoming increasingly popular as a sustainable construction material due to its capacity to repurpose waste tyre rubber (WTR) and reduce the use of natural aggregates. However, experimental testing of its mechanical properties is expensive and time-consuming, emphasizing the importance of reliable predictive tools. To solve this issue, this research uses gene expression programming (GEP) and random forest (RF) models to estimate the flexural strength (FS) and split tensile strength (STS) of rubberized concrete. The development of the models were carried out using a comprehensive dataset that included 112 experimental findings and seven main input parameters. Multiple statistical metrics were used to evaluate model performance, such as R, MAE, RMSE, RAE, and RRSE. Although both models correctly predicted the strength trends of rubberized concrete, RF outperformed GEP with testing R² values of 0.9799 for FS and 0.9817 for STS, compared to 0.9183 and 0.8965, respectively. The error analysis revealed that both models’ prediction errors were within acceptable bounds, with RF models having lower error as compared to GEP. Furthermore, the proposed models were compared to previously published prediction models and outperformed them in terms of accuracy and generality. Furthermore, the Shapley Additive Explanations (SHAP) analysis applied to the dataset revealed the relative importance of input parameters on mechanical behavior. The findings show that machine learning-based techniques are successful at predicting the mechanical properties of rubberized concrete and have the potential to enhance environmentally friendly construction through the efficient reuse of WTR.
Sheraz et al. (Mon,) studied this question.