Rutting characterization of steel-bridge asphalt pavement based on layer-thickness profiling using ground-penetrating radar

Abstract

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. 高温车辙是钢桥沥青路面的主要病害之一，可使用钻芯进行室内试验和现场测试表征；但钻芯取样采集的路面信息极其有限，现场车辙尺和激光剖面仪无法评价由于铺装层内部变形导致的车辙。基于探地雷达的沥青路面结构层厚度测量可进行车辙评价，但车辙导致的层间压缩会因有限的信号分辨率产生测量误差。钢桥面板的金属强反射和天线制式等影响因素尚未得到系统研究，缺乏控制试验验证。探讨了探地雷达在钢桥沥青路面车辙评价的可行性。基于室内加速加载试验条件建立沥青路面和空气耦合天线的数值模型，结果表明2 GHz空气耦合天线净空不超过50 cm可获得最佳测量速度和精度。采集室内加速加载设备不同荷载作用次数下的路面探地雷达电磁波信号，比较数值模拟探地雷达信号与室内实测信号，分析空气耦合天线探地雷达在钢桥面沥青路面反射信号中的特征。针对层间信号重叠，提出了信号重构和自动化厚度测量的非线性梯度下降算法；针对路面不平整度导致的振动效应，使用经验模式分解法进行信号降噪，钻芯取样验证算法的厚度测量精度。结果表明：在室内试验条件下，车辙宽度预测误差为4.9%，SMA层和MA层平均厚度预测误差分别为2.3%和3.8%，上面层SMA层变形大于下面层MA层。因此，使用先进的信号处理方法进行基于探地雷达结构层厚度测量的钢桥沥青路面车辙评价切实可行。