基于GPR信号的水泥路面脱空特征表征方法

Feature Extraction Method of Void Defect in Concrete Pavement from GPR Signal

脱空缺陷在水泥路面中不可避免,严重影响水泥路面的结构安全性,迫切需要建立脱空缺陷的早期检测和识别方法,保障行车安全。为获得表征脱空缺陷的方法,在室内构建了不同脱空尺寸的空洞模型,并用探地雷达进行了检测试验;分析了正常路面与脱空状况下的回波信号,提取了探地雷达信号的28个时频域参数,并进行了特征参数对比,用主成分分析(PCA)对两种样本进行了降维分析。结果表明,正常路面样本与脱空样本的时频特征存在明显差异,PCA降维后的二维及三维成分均能有效将正常和脱空样本区别,表明提取的28个时频域特征参数指标可作为水泥混凝土路面的脱空判别依据,并在实际路面进行了验证。为水泥路面的脱空病害的智能无损检测与识别奠定了基础。

Void defects are inevitable in concrete pavement,which seriously affect the structural safety of concrete pavement,and it’s urgently needed to detect the void defects at the early stage to ensure driving safety.Two void models with different void sizes were constructed indoors,and the ground penetrating radar(GPR)method was used for this detection experiment.The signals of the ground penetrating radar in normal and void conditions on the road were analyzed,and 28 time-frequency domain parameters of the ground penetrating radar signal were extracted and performed PCA dimensionality reduction analysis in these parameters.The results indicate that there are significant differences in the time-frequency domain parameters of the normal samples and the void samples.The two-dimensional and three-dimensional components after PCA dimensionality reduction can effectively distinguish the normal and the void samples,indicating that the extracted 28 time-frequency domain parameters can be used as the basis for distinguishing the void of concrete pavement,which lays the foundation for the intelligent non-destructive detection and identification of void defects of concrete pavement.