Condition assessment and maintenance planning of railway infrastructure is a prerequisite for safe and reliable train operation. As the loads are constantly increasing, condition assessment of the track must also be further developed. Existing methods can describe the condition of the track well in many cases, but they will reach their limits with faster deterioration processes and shorter time windows for inspection and maintenance, both associated with higher loads. This development can only be countered with an increased understanding of the system and the associated better planning of component specific measures. Among others, short-wave effects of the track need to be considered. The aim of this paper is to demonstrate the possibility of describing short-wave effects with an already existing data source. Insulated rail joints, welding joints, switch components, but also rail corrugation of different wavelengths and squat can be detected, evaluated and monitored by a measuring system based on optical distance meters. These assets and wear phenomena form essential parts of track asset management, but still are not described sufficiently by established methods. Although the so-called rail surface measurement system has been installed on the main Austrian measuring car for years, its full potential could not be exploited due to insufficient positioning accuracy. The method presented in this paper intends to change that. This allows for a holistic assessment of track condition when planning maintenance activities.

For future requirements, asset management of turnouts needs to rely on data-based assessment tools. These tools must enable the quantification of quality behaviour of turnouts and identify causes of poor behaviour. In this paper, we provide a toolbox addressing these requirements. We use track geometry as the main criterion for quality behaviour in combination with additional indicators, each associated with a different component, to understand turnout performance. The toolbox is applied to five similar turnouts to compare their performance. It is revealed that one of the turnouts performs significantly worse than the others. A deeper analysis can identify worn ballast in several areas of the turnout as the cause of poor performance. Problems in the ballast bed can be attributed to worn insulated rail joints as well as to stiffness changes in the transition areas of the turnout.

Managing head checks is a crucial task for an infrastructure manager as in case of deep cracks, rails can break and, thus, accidents might be the consequence. Many infrastructure managers use vehicle-mounted eddy-current testing for detecting cracks. This is sufficient for guaranteeing safe railway operation in applying a reactive maintenance regime removing cracks of a pre-defined depth. Moving this towards a predictive maintenance regime is only possible through assessing the crack growth. Establishing a stable deterioration function needs a sound data basis including a proper re-positioning of the test results of consecutive testing campaigns. This paper presents the results achieved from analysing the eddy-current testing campaigns of 10 years on a main line of the Austrian railway network and calculating a crack growth function as regression to field data. While it is possible to derive stable functions, the testing frequency needs to be shortened in order to move further to predictive maintenance.

When a wheel passes over a rail surface irregularity, the resulting vehicle excitations lead to the application of additional system forces to both the track and the vehicle. These forces contribute to an accelerated track geometry deterioration, which in turn results in increased costs. In a recent paper, a clear correlation between the presence of rail irregularities and poor track geometry quality was demonstrated. Rail surface irregularities thereby were quantified by raw data of a chord-based optical measurement system mounted on the regular track recording vehicle in Austria. This paper deals with deconvolution of the recorded data in order to guarantee irregularity quantification without any distortion. Two different deconvolution approaches are developed and validated by additional measurements. Using the deconvoluted data, previously published evaluations were repeated, and the impact of using deconvoluted data instead of chord values was analysed. The correlation between short-wave effects and track geometry quality can not only be confirmed; it is even stronger than predicted by the chord data. The results of the analysis demonstrate that irregularities with amplitudes exceeding 0.08 mm contribute to an accelerated deterioration in track geometry. Amplitudes of a greater severity result in track geometry levels that are up to 120% inferior to the average.

Short wavelength rail defects or short-wave effects of rail have a significant influence on the system behaviour of railways. The influence of additional dynamic loads due to short-wave effects on component wear and the general quality behaviour of track is shown by several research papers. However, the described effect cannot yet be quantified comprehensively. This paper attempts to fill this gap. With the help of extensive data analysis, we can show that short-wave effects lead to a track geometry that is up to 50% worse than on average. Furthermore, we show examples illustrating the influence of untreated welded joints on the development of isolated defects of the track geometry. A detailed in-depth analysis of sections with different boundary conditions finally shows that short-wave effects have a significant influence on track geometry if the amplitude of the effect exceeds 0.15 mm. This value can be recommended as a threshold value in Austria and as a reference value in other countries.