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

作　　者：Nura Shehu Aliyu Yaro Muslich Hartadi Sutanto Noor Zainab Habib Aliyu Usman Abiola Adebanjo Surajo Abubakar Wada Ahmad Hussaini Jagaba 

机构地区：[1]Department of Civil and Environmental Engineering,Universiti Teknologi PETRONAS,Seri Iskandar 32610,Malaysia [2]Department of Civil Engineering,Ahmadu Bello University,Zaria 810107,Nigeria [3]Institute of Infrastructure and Environment,Heriot-Watt University,Dubai 294345,United Arab Emirates [4]School of Civil Engineering,Central South University,Changsha 410075,China [5]Interdisciplinary Research Centre for Membranes and Water Security,King Fahd University of Petroleum and Minerals,Dhahran 31261,Saudi Arabia

出　　处：《Journal of Road Engineering》2024年第3期318-333,共16页道路工程学报（英文）

基　　金：the University of Teknologi PETRONAS(UTP),Malaysia,and Ahmadu Bello University,Nigeria,for their vital help and availability of laboratory facilities that allowed this work to be conducted successfully.

摘　　要：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 WTDCmodified 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.

关 键 词：Waste tire Neural network Sustainable practices Asphalt mixtures Predictive model 

分 类 号：U414[交通运输工程—道路与铁道工程]