The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research shows that dynamic responses from moving trains via axle box acceleration (ABA) measurements can quantify abrupt changes in the vertical dynamic responses. Thus, this paper proposes a framework to design an enhanced vertical TQI, called EnVTQI, by integrating track longitudinal levels and dynamic responses from ABA measurements. First, measured ABA signals are processed to mitigate the influence of variation in measurement speed. Then, substructure and ballast-related features are extracted, including scale average wavelet power (SAWP) in the ranges 0.04 m^-1 to 0.33 m^-1 (substructure) and 1.25 m^-1 to 2.50 m^-1 (ballast). This enables identifying track conditions at different track layers. Finally, EnVTQI is determined by weight averaging between the conventional vertical TQI and the ABA features from moving trains. The performance of EnVTQI is evaluated based on 48 segments of a 200-m track on a Dutch railway line. The results indicate that EnVTQI helps to distinguish track segments that cause poor train-track interaction, which the conventional TQI does not indicate. EnVTQI can supplement the conventional TQI, improving the effectiveness of track maintenance decision-making.

Life cycle performance and risk management are often mentioned as critical tasks for infrastructure managers. However, without proper data collection and analytics these tasks cannot be executed. This paper discusses lessons learned from a case where a data analytics approach was deployed when an unexpected phenomenon occurred on the Dutch High Speed Line (HSL-Zuid). In November 2014, it was found that large sections of the HSL-Zuid were affected by a severe type of rolling contact fatigue (RCF). The RCF resulted in deep cracks on top of the rail. These damages were unexpected as the rails were only 5 years in operation and these rails were expected to last about 20–25 years with proper maintenance. In this case, resulting in about 20 km of rail replacements and multiple additional grinding campaigns. As the causes of defects were unknown, the authors applied data analytics to evaluate the possible causes of the RCF. Several measurements of the infrastructure, maintenance and the rolling stock resulted in a set of parameters. Then, a bottom-up approach is proposed for evaluating the affected sections to find similar parameter values among these over the whole track. The idea was to look for parameter values which could explain why certain sections were affected by the defects while others were not. The outcomes of the analysis indicated that it is highly likely that one type of rolling stock was affecting the rails in the curves of the HSL-Zuid. As the track was designed at the high-speed sections for 220–300 km/h and this type of rolling stock was driving below design speed, different loading of the rails throughout the curves occurred. Lessons learned from this case do not only apply to the technical area of wheel/rail and vehicle/infrastructure interfacing, but also to the usage of data analytics itself and life cycle management. From this case study, it is discussed how data collection and analytics can be better embedded by (rail) infrastructure managers from an early stage of development and use of infrastructure. Further scientific development for infrastructure data analytics are also discussed.

The extensive usage of railway infrastructure demands a high level of robustness, which can be achieved partly by considering (and managing) the track and rolling stock as one integral system with due attention to their interface. A growing number of infra managers consider, in this framework, the track-friendliness of vehicles that have access to their tracks as a key control parameter. The aim of this study is to provide further insight into potential contributions to track-friendliness, assessed in relation to track deterioration mechanisms and cost, understanding how potential benefits are best to be utilised. Six proposed freight bogie design measures are evaluated with respect to the improvement in curving behaviour, switch negotiation and related track degradation mechanisms. To this purpose a sensitivity analysis has been carried out by means of track–train simulations in the VAMPIRE® multi body simulation software. Additionally, the impact on track deterioration costs has been calculated for those track-friendly design modifications identified as most promising. Conclusions show that the standard Y25L freight bogie design displays rather a track-friendly behaviour. Tuning the primary yaw stiffness shows a high potential to further improve track-friendliness, significantly reducing track deterioration cost at narrow radius curves and switches (by, respectively, 30% and 60%). When calculating the overall deterioration cost for the travelled route, the calculation model should include a well-balanced representation of switches and narrow radius curves.

The long-term behaviour of railway track has attracted increasing attention in recent years. Improvements in long-term structural performance reduce demands for maintenance and increase the continuous availability of railway lines. The focus of this paper is on the prediction of the sensitivity of a track design to long-term deterioration in terms of track geometry. According to the state of the art literature, degradation is often investigated using empirical models based on field measurement data. Although a rough maintenance forecast may be made employing empirical models, the predictions are not generic, and the physical processes which govern track degradation under train operation remain unclear. The first aim of this study is to present a mathematical model to elucidate the underlying physics of long-term degradation of railway tracks. The model consists of an infinitely long beam which is periodically supported by equidistantly discrete sleepers and a moving unsprung mass which represents a travelling train. The mechanical energy dissipated in the substructure is proposed to serve as a measure of the track degradation rate. Secondly, parametric studies on energy dissipation are conducted to identify effects of various track design parameters on the susceptibility of the track to degradation, as well as the effect of the train speed. It has been shown that the track/subgrade stiffness is the most influential parameter on degradation whereas other system parameters do influence the degradation rate but at lower magnitudes. The conclusions can be used to optimise the track design in the early stage for better long-term structural performance of railway tracks.