This study presents the development of predictive models for the mechanical properties of a cement-stabilized base layer incorporating waste rubber using artificial neural networks. The considered input parameters included ultrasonic pulse velocity (UPV), compressive strength (fc), rubber content (mass %), cement content (mass %) and curing duration (days). The models were employed to predict indirect tensile strength (ft) and the static modulus of elasticity (Est). A total of ten neural network models were developed and systematically evaluated. The results indicate that UPV is a highly relevant parameter, as its importance remains constant across all models (0.439–0.497 for ft and 0.167–0.225 for Est prediction), reflecting its stable contribution to predictions, while curing duration exerts a particularly significant effect on Est (0.236–0.621). The significance of the remaining input parameters varies depending on their combination within each model, highlighting the critical role of selecting an appropriate set of input variables. Statistical analysis demonstrates that all models exhibit a high level of reliability, confirming the suitability of neural networks for accurately predicting the mechanical behaviour of cement-based materials containing waste rubber. High need for standardisation of such models is highlighted.
Zvonarić et al. (Thu,) studied this question.