Polygonisation is a common nonuniform wear phenomenon occurring in railway vehicle wheels and has a severe impact on the vehicle–track system, ride comfort, and lineside residents. This paper first summarizes periodic defects of the wheels, including wheel polygonisation and wheel corrugation, occurring in railways worldwide. Thereafter, the effects of wheel polygonisation on the wheel–rail interaction, noise and vibration, and fatigue failure of the vehicle and track components are reviewed. Based on the different causes, the formation mechanisms of periodic wheel defects are classified into three categories: (1) initial defects of wheels, (2) natural vibration of the vehicle–track system, and (3) thermoelastic instability. In addition, the simulation methods of wheel polygonisation evolution and countermeasures to mitigate wheel polygonisation are presented. Emphasis is given to the characteristics, effects, causes, and solutions of wheel polygonisation in metro vehicles, locomotives, and high-speed trains in China. Finally, the guidance is provided on further understanding the formation mechanisms, monitoring technology, and maintenance criterion of wheel polygonisation.

Anti-slip control systems are essential for railway vehicle systems with traction. In order to propose an effective anti-slip control system, adhesion information between wheel and rail can be useful. However, direct measurement or observation of adhesion condition for a railway vehicle in operation is quite demanding. Therefore, a proportional–integral controller, which operates simultaneously with a recently proposed swarm intelligence-based adhesion estimation algorithm, is proposed in this study. This approach provides determination of the adhesion optimum on the adhesion-slip curve so that a reference slip value for the controller can be determined according to the adhesion conditions between wheel and rail. To validate the methodology, a tram wheel test stand with an independently rotating wheel, which is a model of some low floor trams produced in Czechia, is considered. Results reveal that this new approach is more effective than a conventional controller without adhesion condition estimation.

This study investigated the fretting wear and fatigue of full-scale railway axles. Fatigue tests were conducted on full-scale railway axles, and the fretting wear and fretting fatigue in the fretted zone of the railway axles were analysed. Three-dimensional finite element models were established based on the experimental results. Then, multi-axial fatigue parameters and a linear elastic fracture mechanics-based approach were used to investigate the fretting fatigue crack initiation and propagation, respectively, in which the role of the fretting wear was taken into account. The experimental and simulated results showed that the fretted zone could be divided into zones I–III according to the surface damage morphologies. Fretting wear alleviated the stress concentration near the wheel seat edge and resulted in a new stress concentration near the worn/unworn boundary in zone II, which greatly promoted the fretting crack initiation at the inner side of the fretted zone. Meanwhile, the stress concentration also increased the equivalent stress intensity factor range ΔK _eq below the mating surface, and thus promoted the propagation of fretting fatigue crack. Based on these findings, the effect of the stress redistribution resulting from fretting wear is suggested to be taken into account when evaluating the fretting fatigue in railway axles.

To simulate ballast performance accurately and efficiently, the input in discrete element models should be carefully selected, including the contact model and applied particle shape. To study the effects of the contact model and applied particle shape on the ballast performance (shear strength and deformation), the direct shear test (DST) model and the large-scale process simulation test (LPST) model were developed on the basis of two types of contact models, namely the rolling resistance linear (RRL) model and the linear contact (LC) model. Particle shapes are differentiated by clumps. A clump is a sphere assembly for one ballast particle. The results show that compared with the typical LC model, the RRL method is more efficient and realistic to predict shear strength results of ballast assemblies in DSTs. In addition, the RRL contact model can also provide accurate vertical and lateral ballast deformation under the cyclic loading in LPSTs.

Due to the fact that ballastless tracks in high-speed railways are not only subjected to repeated train–track dynamic interaction loads, but also suffer from complex environmental loads, the fundamental understanding of mechanical performance of ballastless tracks under sophisticated service conditions is an increasingly demanding and challenging issue in high-speed railway networks. This work aims to reveal the effect of train–track interaction and environment loads on the mechanical characteristic variation of ballastless tracks in high-speed railways, particularly focusing on the typical interface damage evolution between track layers. To this end, a finite element model of a double-block ballastless track involving the cohesive zone model for the track interface is first established to analyze the mechanical properties of the track interface under the loading–unloading processes of the negative temperature gradient load (TGL) followed by the same cycle of the positive TGL. Subsequently, the effect of wheel–rail longitudinal interactions on the nonlinear dynamic characteristics of the track interface is investigated by using a vehicle-slab track vertical-longitudinal coupled dynamics model. Finally, the influence of dynamic water pressure induced by vehicle dynamic load on the mechanical characteristics and damage evolution of the track interface is elucidated using a fluid–solid coupling method. Results show that the loading history of the positive and negative TGLs has a great impact on the nonlinear development and distribution of the track interface stress and damage; the interface damage could be induced by the wheel–rail longitudinal vibrations at a high vehicle running speed owing to the dynamic amplification effect caused by short wave irregularities; the vehicle dynamic load could produce considerable water pressure that presents nonlinear spatial–temporal characteristics at the track interface, which would lead to the interface failure under a certain condition due to the coupled dynamic effect of vehicle load and water pressure.

Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers. In this study, the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables. First, the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions, namely the compression of the train dwell time at stations and the compression of the train running time in sections. Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time, namely the delay time, the scheduled supplement time, the running interval, the occurrence time, and the place where the delay occurred, under the two train operation adjustment actions. Finally, the gradient-boosted regression tree (GBRT) algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions. A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.