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Numerical analysis and automatic identification of GPR responses to highway surface subgrade diseases

机译:GPR对公路表面和路基病害响应的数值分析和自动识别

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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:本文介绍了通过名为GprMax的软件工具对常见道路疾病的GPR响应进行建模,并提出了对这些疾病响应的自动识别。共建模了六种道路疾病,例如表面裂缝,基础裂缝,基础置换,基础破坏,空洞等。仿真结果表明,900MHz天线对此类疾病具有良好的检测能力,各种疾病都可能在GPR图像上引起相应的异常。通常,具有不同介电特性的垂直界面可能会产生弧形特征,而水平界面可能会导致相应的反射轴。较小的介电常数导致较少的GPR波传播时间,反之亦然。病区的所有天线移动空间,天线频率和水量都会对GPR图像产生影响:天线移动空间小和天线频率高可能导致较好的分辨率;病区的水分会增加反射信号。确定疾病特征后,作者开发了基于图形理论的疾病自动识别程序,该程序在现场GPR扫描中被证明是可靠的。

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