The surface crack of ballastless track slab can seriously reduce the serviceability and durability of high-speed railway (HSR). Aiming at accurately and efficiently detecting the slab cracks, this research proposes an infrared thermography (IRT)-based method for the surface crack, which is the most serious and common crack type in track slab. A three dimensional finite element (FE) model of IRT detection of concrete slab with surface cracks is established. The relation between the width of detectable cracks and the ambient temperature can be thereby obtained by inputting the measured thermodynamic parameters in the model. Parametric study shows that with ambient temperature higher than 15 °C, cracks with a width of no less than 0.2 mm can be well detected. Scale model test and field test are conducted, IRT method can effectively locate the slab surface cracks with width as small as 0.14 mm when ambient temperature is no less than 20 °C.

The effect of track geometry on vehicle vibration is a major concern in high-speed rail (HSR) operation from the perspectives of ride comfort and safety. However, how to quantitatively characterize the relation between them remains a problem to be solved in track quality assessment. By using fractal analysis, this paper studies the detailed correlation between track surface and alignment irregularities and car body vertical and lateral acceleration in various wavelength ranges. The time-frequency features of the track irregularity and car-body acceleration are first analyzed based on empirical mode decomposition (EMD). Then, the fractal features of the inspection data are determined by calculating the Hurst exponent of their intrinsic mode functions (IMFs). Finally, the fractal dimensions of the track irregularity and car-body acceleration are obtained, and the correlation between their fractal dimensions with respect to different IMFs is revealed using regression analysis. The results show that the fractal dimension is only related to the roughness of the IMF waveforms of the track irregularity and car-body vibration and is irrelevant to the amplitude of the time series of the data; the correlation coefficient of the fractal dimension of the track irregularity and car-body acceleration is greater than 0.7 for wavelengths greater than 30 m, indicating that the relationship between track irregularity and car-body vibration acceleration is more obvious for long wavelengths. The findings of this research could be used for optimizing HSR track maintenance work from the viewpoint of the ride quality of high-speed trains.

In order to investigate the mapping relationship between the meteorological parameters and the internal temperature field of the track plate, this paper takes the CRTS II type ballastless track of a passenger dedicated line in East China as the research object, and builds an on-line monitoring system for the temperature field of the intelligent track plate, and carries out real-time data on the ambient temperature, solar radiation, wind speed and the internal temperature of the track plate. Based on the BP neural network method, the variation rule of the temperature field inside the track plate is studied, and the mapping relationship between the environmental meteorological parameters and the temperature of the track plate is established. The prediction results show that the prediction accuracy of the nonlinear mapping model is 92.4% and the prediction accuracy of the temperature gradient is 81.5%, and the prediction accuracy is 96% and 93% respectively for the 13:00-16:00 time section of the overall temperature and the maximum positive temperature gradient of the track plate.

Amplitude characteristics of the rocket sled track alignment and profile measured irregularity were analyzed by statistical methods in time domains. The levels of the interval and overall track irregularity quality were evaluated by the straightness evaluation index. Stationary of the track irregularity samples was tested, and maximum wavelength of the track irregularity was analyzed in order to ensure availability and accuracy of spectrum estimations. The rocket sled track irregularity spectrum was estimated by Welch methods, and characteristics and cause of the rocket sled track irregularity were analyzed and investigated in frequency domains. This study could be helpful for rocket sled track maintenance and repair and provide reference data for rocket sled-track dynamics research.

One of the most important problems in Empirical Mode Decomposition (EMD) applications is mitigation of the end effects. The mirror extrema extending method is an effective method to deal with it. When mirror extrema extending is used to solve the end effects, it must put the mirror at the local extrema point. Aiming at this problem, a method of combining the mirror extension with a grey neural network is proposed to extend the data. The grey neural network is adopted to forecast an extrema point forward and backward, and then the mirror extrema extending is used to mitigate the end effects. To evaluate the end effects, five common end-effect mitigation methods of EMD have been compared, including the mirror extrema extending method, the BP neural network prediction method, the support vector regression machines prediction method, the self-adaptive matching extending method, and the grey neural network mirror extrema extending method. The five end-effect mitigation methods are evaluated quantitatively and compared through energy, the correlation coefficient of the decomposed signal and the original signal, and operation time. Results show that the grey neural network mirror extrema extending method is the best option among the five methods. Finally, the method is applied to analyze track irregularity and achieve satisfactory results.