Modeling the Dynamic Behavior of Recycled Concrete Aggregate-Virgin Aggregates Blend Using Artificial Neural Network

Abstract

Construction and demolition waste (CDW) aggregates have increased as a result of the rise in construction activities. Current research focuses on recycling of CDW to replace dwindling natural aggregates but pays little attention to permanent deformation behavior due to the anisotropic nature of the blended CDW aggregates. Accordingly, this study performs repeated load triaxial tests to evaluate the permanent deformation mechanism of the blended materials under various shear stress ratios and moisture conditions. An artificial neural network (ANN) deformation prediction model that accounts for the complex nature of the blended CDW and natural aggregate was developed. Moreover, a sensitivity analysis was performed to determine the relative importance of each input variable on the deformation. The results indicated that the shear stress ratio and confining pressure profoundly influence the deformation. It was demonstrated that the proposed prediction model is more robust than the conventional one. The sensitivity analysis revealed that the number of loading cycles, confining pressure, and shear stress ratios are the principal factors influencing the permanent deformation of the blended aggregates with sensitivity coefficients of 31%, 25%, and 21%, respectively, followed by the CDW and moisture contents. This model can assist practitioners and policymakers in predicting the permanent deformation of CDW materials for unbound pavement base/subbase construction.