Railway infrastructure is a crucial asset for the mobility of people and goods. The increased traffic frequency imposes higher loads and speeds, leading to accelerated infrastructure degradation. Asset managers require timely information regarding the current (diagnosis) and future (prognosis) condition of their assets to make informed decisions on maintenance and renewal actions. In recent years, in-service vehicles equipped with on-board monitoring (OBM) measuring devices, such as accelerometers, have been introduced on railroad networks, traversing the network almost daily. This article explores the application of state-of-the-art OBM-based track quality indicators for railway infrastructure condition assessment and prediction, primarily under the prism of track geometry quality. The results highlight the similarities and advantages of applying track quality indicators generated from OBM measurements (high frequency and relatively lower accuracy data) compared to those generated from higher precision, yet temporally sparser, data collected by traditional track recording vehicles (TRVs) for infrastructure management purposes. The findings demonstrate the performance of the two approaches, further revealing the value of OBM information for monitoring the track status degradation process. This work makes a case for the advantageous use of OBM data for railway infrastructure management, and attempts to aid understanding in the application of OBM techniques for engineers and operators.

This paper presents an improved virtual coupling train set (VCTS) operation control framework to deal with the lack of optimization of speed curves in the traditional techniques. The framework takes into account the temporary speed limit on the railway line and the communication delay between trains, and it uses a VCTS consisting of three trains as an experimental object. It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader–follower model. The follower train uses the improved speed curve of the leader train as its speed reference curve through knowledge migration, and this completes the multi-objective optimization of the driving strategy for the VCTS. The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy. They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network (ICM-PER-D3QN) algorithm outperforms the deep Q-network (DQN) algorithm in optimizing the driving strategy of the leader train. The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57% when compared to the DQN algorithm. Furthermore, the particle swarm optimization (PSO)-based model predictive control (MPC) algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation, with an average increase of 37.7% in tracking accuracy compared to the traditional MPC algorithm.

Swing nose crossings (SNXs) have been widely used in heavy haul railways to create a smoother load transfer and hence reduced impact load. However, the current design of SNXs hasn’t been fully examined under heavy haul operating conditions. Additionally, maintenance guidelines for SNX wear-related issues in Australian heavy haul railways are relatively lacking. As such, this study aims to investigate the dynamic response of the wheel–rail contact and analyse the wear performance of an SNX currently used in Australian heavy haul railways. Dynamic implicit–explicit finite element analysis was conducted to simulate the wheel–rail contact along the SNX. The distribution of the wear intensity over the SNX was identified by using a local contact-based wear model. The influence of various scenarios on wear was also explored. The results verify the improved dynamic performance of the SNX, as the increased contact force after load transfer remains below 1.2 times the static load. The findings also indicate that the decrease in relative height and increase in nose rail inclination result in greater wear on the nose rail. Notably, the SNX considered in the current study exhibits better wear performance when used with moderately worn wheels.

With the rapid development of heavy haul railway transportation technology, tunnel foundation defects and their effects on structural performance have attracted wide attention. This paper systematically investigates the evolution mechanism of tunnel foundation defects in heavy haul railway tunnels and their impact on structural stiffness degradation through experiments and numerical simulations. A heavy haul train–ballasted track–tunnel basement–surround rock dynamic interaction model (TTTR model) is constructed. Firstly, the study reveals the four-stage evolution process of initial defects in the tunnel basement under complex environmental conditions. Experiments were conducted to measure the load-bearing capacity and stiffness degradation of the tunnel basement structure under different defect states. It is found that foundation defects, especially under the coupling of loose fill in the basement with the water-rich environment of the surrounding rock, significantly reduce the stiffness of the tunnel bottom structure and increase the risk of structural damage. Then, based on refined simulation of wheel–rail interaction and multi-scale coupled modeling technology, the TTTR dynamic interaction model was successfully constructed, and its validity was proven through numerical validation. A time-varying coupling technique of constrained boundary substructures (CBS technique) was adopted, significantly improving computational efficiency while ensuring calculation accuracy. The study also analyzes the effects of different degrees of void defects on the dynamic behavior of the train and the dynamic characteristics of the tunnel structure. It finds that foundation defects have a significant impact on the train’s operational state, track vibration displacement, and vibration stress of the tunnel lining structure, especially under the coupling effect of basement voids and the water-rich environment, which has the greatest impact. The research results of this paper provide a theoretical basis and technical support for the maintenance and reinforcement of tunnel foundation structures.

Foamed concrete has been used to address the issue of differential settlement in high-speed railway subgrades in China. However, to enhance crack resistance, reinforcement is still necessary, and further research is required to better understand the performance of foamed concrete in subgrade applications. To this end, a series of tests—including uniaxial compressive and dynamic triaxial tests—were conducted to comprehensively examine the effects of basalt fiber reinforcement on the mechanical properties of foamed concrete with densities of 700 and 1000 kg/m³. Additionally, a full-scale model of the foamed concrete subgrade was established, and simulated loading was applied. The diffusion patterns of dynamic stress and dynamic acceleration within the subgrade were explored, leading to the development of experimental formulas to calculate the attenuation coefficients of these two parameters along the depth and width of the subgrade. Furthermore, the dynamic displacement and cumulative settlement were analyzed to evaluate the stability of the subgrade. These findings provide valuable insights for the design and construction of foamed concrete subgrades in high-speed rail systems. The outcomes are currently under consideration for inclusion in the code of practice for high-speed rail restoration.

To mitigate and alleviate low wheel–rail adhesion, a train-borne system is utilised to deposit sand particles into the wheel–rail interface via a jet of compressed air in a process called rail-sanding. Britain Rail Safety and Standards Board introduced guidelines on the sand particles’ shape, size, and uniformity which needs to be adhered to for rail-sanding. To further investigate these guidelines and help improve them, this research presents a parametric study on the particle characteristics that affect the rail-sanding process including density, size and size distribution, coefficient of uniformity, and shape, utilising a coupled computational fluid dynamics–discrete element method (CFD–DEM) model. The efficiency of rail-sanding is estimated for each case study and compared to the benchmark to optimise the sand characteristics for rail-sanding. It is concluded that particle size distribution (within the accepted range) has an insignificant effect on the efficiency while increasing particle size or the coefficient of uniformity decreases the efficiency. Particle shape is shown to highly affect the efficiency for flat, compact and elongated particles compared to the spherical shape. The current numerical model is capable of accurately predicting the trends in the efficiency compared to the actual values obtained from full-scale experiments.

For a large-scale dynamic system, the efficiency of computation becomes a vital work sometimes in engineering practices. As a layered structural system, ballastless track and substructure occupy most part of the degrees of freedom of the whole system. It is, therefore, rather important to optimize the structural models in dynamic equation formulations. In this work, a three-dimensional and coupled model for multi-rigid-body of train and finite elements of track and substructures is presented by multi-scale assemble and matrix reassemble method. The matrix reassembling tactic is based on the multi-scale assemble method, through which the finite element matrix bandwidth is greatly narrowed, and the Cholesky factorization, iterative and multi-time-step solution have been introduced to efficiently obtain the train, track and substructure responses. The subgrade and its subsoil works as a typical substructural system, and comparisons with the previous model without matrix reassembling, SIMPACK and ABAQUS have been conducted to fully validate the efficiency and accuracy of this train–track–subgrade dynamic interaction model.

The integration of a large number of power electronic converters, such as railway power conditioner (RPC), introduces a series of problems, including harmonic interaction, stability issues, and wideband resonance, into the railway power supply system. To address these challenges, this paper proposes a novel harmonic resonance prevention measure for RPC–network–train interaction system. Firstly, a harmonic model, a parallel resonance impedance model, a series resonance admittance model, and a control stability model are each established for the RPC–network–train interaction system. Secondly, a comprehensive resonance impact factor (CRIF) is proposed to efficiently and accurately identify the key components affecting resonance, and to provide the selection results of optimization parameters for resonance prevention. Next, the initially selected parameters are constrained by the requirements of ripple current, reactive power and stability. Subsequently, the impedance parameters (control parameters and filter parameters) of the RPC are optimized with the objective of reshaping the parallel resonance impedance and series resonance admittance of the RPC–network–train interaction system, ensuring the output current harmonics of RPC meet standards to achieve resonance prevention, while ensuring the stable operation of the RPC. Finally, the proposed resonance prevention measure is verified under both light load and heavy load conditions using a simulation platform and a hardware-in-the-loop experimental platform.

Critical for metering and protection in electric railway traction power supply systems (TPSSs), the measurement performance of voltage transformers (VTs) must be timely and reliably monitored. This paper outlines a three-step, RMS data only method for evaluating VTs in TPSSs. First, a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time, mitigating the influence of stochastic fluctuations in traction loads. Second, a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT. Third, a trend analysis method is developed to assess the evolution of the measurement performance of VTs. Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1% relative to their original accuracy levels. Additionally, the method accurately tracks deviation trends, enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations.