基于探地雷达结构层厚度测量的钢桥沥青路面车辙评价

Rutting at high temperature is one of the main asphalt pavement distresses on steel-bridge decks, which can be characterized using laboratory tests of in-situ cores or field tests. However, in-situ cores only cover limited pavement areas. A straight edge and laser profiler cannot be used to characterize rutting caused by subsurface layer deformations on the steel bridge. A ground-penetrating radar (GPR) can be implemented for rutting characterization based on layer-thickness profiling. However, rutting can cause layer compression, introducing layer-thickness prediction errors due to the limited signal resolution. The affecting factors such as strong reflections from the bridge deck and antenna specifications have not been thoroughly investigated. This study explores the feasibility of using GPR in such applications. A simulation model of pavement and air-coupled antenna were built based on the accelerated pavement testing (APT) facility. Results showed that an air-coupled antenna with a central frequency of 2 GHz and a maximum height of 50 cm can reach the optimal survey speed and accuracy. GPR signals under different loading cycles were collected using the APT facility, and were compared to the simulation signals to analyze the features of GPR reflection waveforms from the steel-bridge asphalt pavement using the air-coupled antenna. A super-resolution algorithm was developed to reconstruct the thickness of each layer under the effects of partial reflection overlapping and the strong reflection from the steel deck. The empirical mode decomposition approach was applied to remove the vibration effect due to pavement surface roughness. The GPR-predicted thickness results were compared with the in-situ straight-edge and core measurements. Results showed that the rutting width error was 4. 9%, and the mean thickness prediction errors of SMA and MA layers were 2. 3% and 3. 8%, respectively. The SMA layer had larger deformation than the underlaying MA layer after loadings. Hence, asphalt-pavement rutting caused by subsurface layer deformations on the steel bridge may be identified by GPR-predicted thickness profiles using advanced signal processing methods.