Mortar layer void detection of ballastless track using the impact echo method based on support vector machine

Abstract

High-speed railway is developing rapidly nowadays. However, under the repeated load of the natural environment and the train, the mortar layer in the ballastless track as the elastic adjustment layer of the structure could be void and cause void defects, thereby seriously affecting the performance of the track board and the safety of the high-speed train. With its simple operation and the use of portable equipment, the impact echo method is a useful nondestructive detection method for detecting defects inside the structure. However, in traditional impact echo detection, manpower is required for judging the final test results, which is highly subjective and time consuming. A field test was designed in this paper, for which a track slab measuring 6.55 × 2.55 × 0.24 m was constructed. Complete cavitation, foam board, and Pykrete were used to simulate the void defects. A 62 × 22 grid with a 0.1 m interval was drawn on the board, each grid point was tapped, and two sensors were placed at the standard position to collect data. Fast Fourier transform was used to obtain the amplitude–frequency domain spectrogram, while wavelet transform was used to obtain the time––frequency spectra. The spectra were processed to extract effective features and form 62 × 22 × 2 = 2,728 sample data. The sample data were divided into a training set and a test set. The training set was used to train the support vector machine (SVM) to classify and identify whether a void defect exists, and the test set was used to evaluate the performance of the SVM. Genetic algorithm (GA) was applied to optimize the parameters in the SVM. Results showed that GA-SVM had a good classification effect with an accuracy of 84.26% and can effectively identify the mortar layer void of the ballastless track slab.