Recently, researchers in the road field are focusing on the development of green asphalt materials with lower emission of volatile organic compounds (VOCs). The characterization methodology of asphalt VOCs and the influencing factors on VOCs release have always been the basic issue of asphalt VOCs emission reduction research. Researchers have proposed a variety of asphalt VOCs characterization methodologies, which also have mutually irreplaceable characteristics. Asphalt VOCs volatilization is affected by many factors. In this study, asphalt VOCs characterization methodologies were summarized, including their advantages, disadvantages, characteristics and applicable requirements. Subsequently, the influencing factors of VOCs release, such as asphalt types and environment conditions, are summarized to provide theoretical support for the emission reduction research. The classification and mechanism of newly-development asphalt VOCs emission reduction materials are reviewed. The reduction efficiencies are also compared to select better materials and put forward the improvement objective of new materials and new processes. In addition, the prospects about development of VOCs release mechanism of asphalt materials during the full life cycle and feasibility research of high-efficiency composite emission reduction materials in the future were put forward.
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.