Development of a material-machine cyber model for pavement paving compaction

Over the past decades, extensive researches have taken attempts to improve the quality control of pavement compaction. Different methods such as conventional laboratory tests, practical in-situ experiments, image-based technologies, and numerical simulations, etc. have been taken into consideration to solve this issue. On the one hand, the research method must be accurate, reliable, repeatable. On the other, expected efficiency also requires time- and labour-saving. A proper way to improve the pavement compaction is based on the design conception, material selection, and the construction quality control. Furthermore, the practical way to guarantee the constructing quality is to optimize the working operations of the paver and compaction machine. In this case, the objective of this thesis is to propose a material-machine cyber model, to have a comprehensive understanding of compaction behavior, and to improve the compaction operations during paving compaction. The DEM simulation is proposed in this thesis, which can be applied to simulate the interaction between the granular materials and the machine. In repeated simulation experiments, the model parameters used in DEM must be validated prior to the numerical simulation. Laboratory tests provide the access to capture the physical parameters of the DEM model, and the real-time sensing technology makes it possible to acquire the movement of the aggregates during the whole compaction process. With these two practical ways, the DEM simulation can perform a practical and precise evaluation of the in-situ compaction, and provide a practical scenario to optimize the working operations of the pavement construction machine. This dissertation outlines the method to systematically investigate how to assess the material-machine interaction, and how to improve the pavement compaction quality control