Removal of surface moisture effect for predicting asphalt pavement density from capacitance data

Abstract

Asphalt concrete (AC) attains the ideal density during compaction is critical to ensure construction quality and service performance of asphalt pavement. Therefore, it is imperative to develop a novel instrument and methodology capable of continuously evaluating AC density during the compaction stage. The interdigital coplanar capacitive sensor (ICCS) has exhibited potential for AC density measurement. However, its application during compaction is hindered by the negative impact of water sprayed by roller, which distorts the capacitance data. This study proposes an innovative approach to remove the surface moisture effect from capacitance data. Initially, the method for removing surface moisture effect from capacitance data was proposed, which consists of the dielectric mixing model, generalized partial capacitance model (GPC) model, and conformal mapping technique (CMT). Subsequently, the feasibility of proposed removal method was verified by numerical simulations and experimental tests for various scenarios. Finally, the predicted accuracy of AC density by ICCS was discussed before and after removing the fluence of surface moisture. The findings demonstrated that the GPC model, in conjunction with the CMT and dielectric mixing model, effectively calculated the capacitance value for any multi-layer structure, thereby considering the influence of surface moisture. Furthermore, the proposed theoretical approach effectively removed the surface moisture impact from both simulated and experimental capacitance data, enabling the accurate determination of the dielectric constant and the dry AC density. The measured error of the ICCS with water reduced from 10.68 % to 6.87 % after removing the its effect, which are close to that without water.