Correlating aggregate kinematics and void volume variation through SmartRock for digital twin monitoring of asphalt concrete compaction

Abstract

The development of Digital Twin (DT) technology has facilitated the digital construction of road engineering. The formation of asphalt concrete strength results from a reduction in void volume fraction (VVF) and rearrangement of coarse aggregates under external loads. Investigating the relationship between these two is critical for exploring the intrinsic mechanisms of compaction and achieving DT construction for asphalt concrete compaction. By utilizing Computed Tomography (CT) technology and SmartRock, this study obtains the phased changes in the VVF of specimens and continuous kinematic response of coarse aggregates during compaction. Then, an asphalt concrete compaction DT model combining machine learning (ML) and SmartRock has been established. The results indicate that the reduction in void volume of asphalt concrete limits aggregate motion, and angle change rate (ACR) in the X and Y directions is highly correlated with the void volume change rate (VVCR) of asphalt concrete during compaction. By inputting the angle change signal of SmartRock into the established DT model, the compaction process monitoring and prediction in real time could be realized. It indicates the feasibility of constructing a DT model for asphalt concrete compaction by integrating ML with SmartRock, which can provide robust and enabling technical support for the construction of DT highways.