Adequate railway track condition is a prerequisite for safe and reliable railway operation. Many track quality indices (TQIs) have been developed with the aim of assessing the track condition holistically. These indices combine measurement signals of some or all relevant geometry parameters with different mathematical models. In this paper, a selection of important TQIs is evaluated. Using measurement data of a five kilometer track section, the indices are calculated and their properties are discussed. This study reveals that all indices exhibit drawbacks to varying degrees. As a consequence, a new index has been developed—the track quality index of Graz University of Technology (TUG_TQI). Its favorable characteristics are presented by means of the above-mentioned test section. The TUG_TQI combines all relevant track geometry parameters, which are normalized beforehand to eliminate over or underrepresentation of different parameters. Thus, the index reliably describes the overall geometrical track quality.

/pdf/analyzing-major-track-quality-indices-and-introducing-a-282lmrg6nn.pdf

Analyzing Major Track Quality Indices and Introducing a Universally Applicable TQI

Evaluating the applicability of multi-sensor equipped tamping machines for ballast condition monitoring

This study assesses life-cycle greenhouse gas (GHG) emissions associated with the entire railway infrastructure network of Austria, a first detailed study for a country, modelled through a top-down approach. Railway track is analysed for the first time in detail for a variety of specific boundary conditions using a bottom-up approach focusing on track renewal and maintenance. The methodology of standard elements allows for quantifying expected maintenance demands over the life cycle as well as determination of service life (SL). For this, the network is clustered into the main condition-affecting parameters and documented maintenance and renewal measures are analysed and interpreted accordingly to estimate future behaviour. This Austrian approach used for assessing life-cycle costs serves as input for evaluating environmental impacts, a novel model. Data were gathered via Environmental Product Declarations, governmental publications, and company-specific environmental reports to correspond to the standard supply chains of the Austrian Federal Railways' life-cycle (manufacturing, construction, maintenance, and reuse/recycling) infrastructure practices, and reflect actual transport distances, transport modes, the Austrian electricity mix, and emissions. The railway infrastructure causes 235,000 tonnes of CO2eq emissions per year (0.3% of Austria's total) based on the current infrastructure network, asset distribution, and renewal rates. Within railway infrastructure, the track (incl. rails, fasteners, sleepers and ballast) is the main contributor to GHG emissions with 55% of the total. The GHG emissions associated with the concrete tunnels are 16 times more GHG-intensive per kilometre per year than the railway track but supply only 22% of the total emissions. The railway infrastructure contributes an additional 141% of GHG emissions over emissions from passenger traffic, which is much higher than previously anticipated. In-depth analysis of railway track shows that concrete sleepers with under-sleeper pads come with lower environmental impacts than conventional concrete sleepers. Higher traffic loads as well as narrow curves cause a significant increase in environmental impacts. For rails in a straight section with a SL of 50 years and two grinding measures, the costs regarding GHG emissions amount to €6,500 (including the production, construction, and use phases) when calculating with a cost of €20 per tonne CO2eq on the market. Currently, this equals to around 5% of the economic costs, but this is expected to significantly increase as costs for environmental impacts are set to increase until 2050. Mitigation potential can be found in special rail steel production, reuse of materials, use of alternative fuels, and efficient maintenance strategies.

Embodied greenhouse gas assessment of railway infrastructure: the case of Austria

During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data

This paper develops a novel method for the prediction of the level of degradation of the ballast layer in railway turnouts by establishing a statistical mapping between the train-induced vertical t...

The predictive power of track dynamic response for monitoring ballast degradation in turnouts

Impact of substructure improvement on track quality

The condition and deterioration of a railway track over time has a major influence on its maintenance demands, service life and consequently its life cycle costs. Railway track condition is currently assessed on the basis of manual inspections, wayside equipment and measurements performed by a measurement car. This paper deals with combining the advantages of above assessment technologies, allowing continuous and permanent measurements for the entire network. On the one hand, we use Distributed Acoustic Sensing, a methodology relying on the effect of Rayleigh backscattering. This technology uses fibre optic cables, which are already installed in cable troughs alongside railway tracks and used for telecommunication or signalling. On the other hand, fractal analysis of vertical track geometry allows for a componentspecific condition evaluation, i.e. distinguishing the root cause of an irregularity in track geometry. Correlating both methodologies should show whether or not/to what and to what extent this innovative methodology of using fibre optic cables is applicable for assessing the component specific condition of a railway track. The proposed combination of both methodologies paves the way for real time condition assessment of railway track.

https://www.icevirtuallibrary.com/doi/pdf/10.1680/icsic.64669.107

Fibre Optic Sensing as Innovative Tool for Evaluating Railway Track Condition

Trassenpreise auf Basis der Richtlinie 2012/34/EU – ein erster Benchmark

Fibre optic sensing in railway infrastructure monitoring

Research on track quality behavior has been extensively published, and many different approaches have been presented to describe the process of track quality. The research goal of this pape...

Preparing Track Geometry Data for Automated Maintenance Planning

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