Internal Distress Recognition of Asphalt Materials Based on Static Coplanar Array Capacitance Imaging Technology

Coplanar array capacitance imaging technology (CACT) is a non-destructive testing (NDT) method with rapid visual recognition. However, there is little research on its application for identifying internal distress in asphalt materials. This study proposes a CACT using a novel coplanar array capacitance sensor (CACS) to identify internal distress in asphalt materials. Firstly, the sensitivity field distributions of electrode pairs and CACS are established, and the optimal sensitivity layer is determined. In addition, the normalized capacitance of electrode pairs and the contribution rate required for distress imaging are analyzed. Finally, the distress images of asphalt materials are reconstructed. The relative depth, shape feature, and type of internal distress are determined. It is found that Layer 14 is the optimal sensitivity layer. Circular distress exhibits the highest normalized capacitance values, followed by square distress and triangle distress. The opposing electrode pairs demonstrate the greatest contribution to distress imaging. The redder the image, the deeper the distress, demonstrating that CACT can determine the relative depth of internal distress. The square distress image has the highest accuracy, followed by circular distress and triangle distress. The reconstructed images show that moisture distress in asphalt mixtures is reconstructed with the highest accuracy, followed by moisture distress in asphalt mastics, void distress in asphalt mastics, and void distress in asphalt mixtures.