Predictive modelling of volumetric and Marshall properties of asphalt mixtures modified with waste tire-derived char: A statistical neural network approach

Abstract

The goals of this study are to assess the viability of waste tire-derived char (WTDC) as a sustainable, low-cost fine aggregate surrogate material for asphalt mixtures and to develop the statistically coupled neural network (SCNN) model for predicting volumetric and Marshall properties of asphalt mixtures modified with WTDC. The study is based on experimental data acquired from laboratory volumetric and Marshall properties testing on WTDC-modified asphalt mixtures (WTDC-MAM). The input variables comprised waste tire char content and asphalt binder content. The output variables comprised mixture unit weight, total voids, voids filled with asphalt, Marshall stability, and flow. Statistical coupled neural networks were utilized to predict the volumetric and Marshall properties of asphalt mixtures. For predictive modeling, the SCNN model is employed, incorporating a three-layer neural network and preprocessing techniques to enhance accuracy and reliability. The optimal network architecture, using the collected dataset, was a 2:6:5 structure, and the neural network was trained with 60% of the data, whereas the other 20% was used for cross-validation and testing respectively. The network employed a hyperbolic tangent (tanh) activation function and a feed-forward backpropagation. According to the results, the network model could accurately predict the volumetric and Marshall properties. The predicted accuracy of SCNN was found to be as high value ​>98% and low prediction errors for both volumetric and Marshall properties. This study demonstrates WTDC's potential as a low-cost, sustainable aggregate replacement. The SCNN-based predictive model proves its efficiency and versatility and promotes sustainable practices.