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The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

The deterioration of the sleeper support on the ballasted track begins with the accumulation of sleeper voids. The increased dynamic loading in the voided zone and the ballast contact conditions cause the accelerated growth of the settlements in the voided zones, which results in the appearance of local instabilities like ballast breakdown, white spots, subgrade defects, etc. The recent detection and quantification of the sleeper voids with track-side and onboard monitoring can help to avoid or delay the development of local instabilities. The present paper is devoted to the study of the dynamic behavior of railway track with sleeper voids in the ballast breakdown zone. The result of the experimental track-side measurements of rail acceleration and deflection is presented. The analysis shows the existence of the dynamic impact during wheel entry in the voided zone. However, the measured dynamic impact is subjected to the bias of the track-side measurement method. Both the mechanism of the impact and the measurement aspects are explained by using the one-beam model on viscoelastic foundation. The void features in the dynamic behavior are analyzed for the purpose of track-side and onboard monitoring. A practical method of the void parameter quantification is proposed.

In this paper, an application of computer vision and machine learning algorithms for common crossing frog diagnostics is presented. The rolling surface fatigue of frogs along the crossing lifecycle is analysed. The research is based on information from high-resolution optical images of the frog rolling surface and images from magnetic particle inspection. Image processing methods are used to pre-process the images and to detect the feature set that corresponds to objects similar to surface cracks. Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle. Statistically significant crack features and their combinations that depict the surface fatigue state are found. The research result consists of the early prediction of rail contact fatigue.

The given article considers the method of calculating the track geometry deformation with respect to uneven accumulation of residual deformations along the track. The technique proposes two significant changes in existing approaches to calculating the efficiency of the ballast layer. The transition from the approach of allowable stresses design in the ballast layer to the deformative approach of accumulations of track geometry deformations allows us to draw conclusions regarding the intervals of track tamping and the duration of ballast layer life cycle. The transition from the determinative to probabilistic approaches makes it possible to draw conclusions not only from the average unevenness, but also with regard to all possible facts of unevenness. The method is based on the mechanism of sudden and gradual deformations occurrence, which depends on a number of key factors: dynamic stresses on the ballast, non-uniformity of track elasticity, performance of current maintenance work. Based on the experimental studies results, the dependencies of sudden deformations and the intensity of gradual deformations on the level of stress on the ballast layer were established. The experimental results of the influence of the sub-ballast base elasticity on the intensity of accumulation of residual deformations are shown. On the basis of the developed method, the prediction of track geometry deterioration for a given structure of the track, the rolling stock and the permissible level of geometric deviations for track maintenance is presented.