基于支持向量机的隧道衬砌空洞机器识别方法

探地雷达是检测隧道衬砌空洞最为有效的方法之一,但检测数据的解析始终是限制其广泛应用的关键。基于支持向量机的基本理论,文章建立了一套隧道衬砌空洞探地雷达图像的机器识别方法,该方法包括图像预处理、特征提取和支持向量机识别三个步骤。首先,探地雷达图像需经过零时修正、滤波、偏移、增益等预处理以提高信噪比;其次,对图像的时域信号进行分段,在分段信号上提取方差、标准绝对偏差和四阶矩三个统计量作为图像特征;最后,利用已知数据对支持向量机模型进行训练,并用数值模拟和模型试验数据对训练好的支持向量机模型进行测试。结果表明,该方法不仅能够准确识别隧道衬砌和围岩内的空洞,还可以对空洞埋深及横向分布范围做出较准确的判断。

Machine Recognition Method of Tunnel Lining Voids Based on SVM Algorithm

Ground penetrating radar(GPR)is one of the most effective detection methods for tunnel lining voids.However,the difficulties in data explanation are always the key to restrict its wide application.Based on support vec tor machine(SVM)algorithm,a set of machine recognition method of GPR image for tunnel lining voids is estab lished.This method includes pre-processing of GPR data,feature extraction and SVM recognition.Firstly,the GPR image needs to be preprocessed by time-zero correction,filtering,migration and gain and so on to improve the sig nal-noise ratio(SNR).Secondly,each time-domain trace of GPR image is segmented and three statistics,namely variance,mean absolute deviation and fourth-order moment are extracted from the segmented signal as image fea tures.Finally,the SVM model is trained by using the known data,and the data from a numerical simulation and a model experiment are used to test the trained SVM model.The results show that the proposed method can not only accurately recognize all voids in the tunnel lining and surrounding rock,but also accurately estimate the cover depths and lateral ranges of the voids.