Automatic Detection of Diseases in Tunnel Linings Based on a Convolution Neural Network and a Support Vector Machine

Abstract

The complexity of diseases in tunnel linings and the interference of clutter and the strong reflection of rebar in ground-penetrating radar (GPR) data are the important factors that lead to the low accuracy and poor automation of disease detection. As consequence, this paper carries out an automatic detection method for hidden lining diseases. Firstly, in order to suppress the interference of strong clutter, the state equation and measurement equation of GPR data are established, and the recursive formula of clutter suppression is deduced. Secondly, combined with a convolution neural network, the network which can suppress the strong reflection of rebar is built. Finally, the multi-dimensional characteristics of disease in the time domain, frequency domain, and time-frequency domain are extracted, and then the support vector machine (SVM) data set is established and the automatic detection method for diseases is formed. The proposed method can avoid the low efficiency of manual interpretation and the over-dependence of detection accuracy of relying upon the experience level of technicians.