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Numerical Analysis and Automatic Identification of GPR Responses to Highway Surface Subgrade Diseases

机译:高速公路表面和路基疾病对GPR反应的数值分析及自动识别

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This paper deals with modeling the GPR response from usual road diseases by the software tool called GprMax and presents the automatic identification of these disease responses. There are totally six kinds of road diseases being modeled, such as surface crack, base crack, base replacement, base breaking, void, ea al. Simulation results show that a 900MHz antenna has good detecting capability to such diseases, all kinds of diseases could cause corresponding anomaly on the GPR pictures. Generally, the vertical interface with different dielectric properties could cause arc-shaped features, while the level interface could lead to corresponding reflection axis. The little dielectric constant leads to less GPR wave travel time, and vice versa. All the antenna moving space, antenna frequency and water in disease area have influence on the GPR image: little antenna moving space and high antenna frequency could lead to nice resolution; moisture in disease area could increase the reflection signal. After determining the disease feature, the authors exploited a program for disease automatic identification basing on the graphics theory, which proved to be reliable in a field GPR scan.
机译:本文涉及通过称为GPRMAX的软件工具对常规道路疾病进行建模,并提出了这些疾病反应的自动识别。含有六种道路疾病的建模,如表面裂缝,基地裂缝,基础更换,底座断裂,空隙,EA A1。仿真结果表明,900MHz天线对这种疾病的检测能力良好,各种疾病可能导致GPR图像上的相应异常。通常,具有不同电介质特性的垂直界面可能导致弧形特征,而电平接口可能导致相应的反射轴。小介电常数导致较少的GPR波行程时间,反之亦然。所有天线移动空间,天线频率和疾病区域的水都对GPR图像有影响:小天线移动空间和高天线频率可能导致漂亮的分辨率;疾病区域的水分可以增加反射信号。在确定疾病特征后,作者利用了一个关于图形理论的疾病自动识别程序,这被证明在野外GPR扫描中可靠。

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