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.

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This paper describes the use of big data analytics for understanding the Rolling Contact Fatigue (RCF) phenomena at the High Speed Line (HSL Zuid) in The Netherlands. The authors developed a data model to investigate the impacting parameters in train-track interaction. This has been done to gain more insight about the circumstances under which RCF occurs and to conclude why some track sections are severely affected and others not. To evaluate the worst affected areas by RCF, the methodology proposes a bottom-up approach. By focusing on the worst affected sections with RCF, a set of characteristic parameter values are defined to describe different types of hotspots. Then, a comparison between the hotspots is performed. The methodology has been applied using real-life data of the Dutch High-speed line, where certain sections had been heavily affected by RCF. Findings concluded that slow running traffic through curves on a highspeed line is likely to contribute to the appearance of RCF.@en

During a typical measurement campaign, lots of temporal and spatial data can be gathered regarding the condition of the rail. This paper proposes two approaches that make use of data analytics techniques to find causes of rolling contact fatigue (RCF) damages. The first approach, named ‘bottom-up approach’, determines the influencing factors regarding RCF based on the worst affected areas (hotspots). The second approach, called ‘top-down approach’, determines the influencing factors based on the condition of the whole track. The approaches use correlation analysis, clustering and similarity of parameters. To show the advantage of the approaches, they have been used for the study of the Dutch High Speed Line (HSL). The results indicates that severe RCF defects occurred only under two very specific conditions. First, in specific curves where one type of train was driving under high tractive efforts and large cant excess through curves. Second, at the entry zones of the HSL where voltage locks are present, the same type of trains’ low driving speeds result in driving without cant excess/deficiency (theoretical cant). The conditions suggest that structurally driving below design speed on a high-speed track can be a cause of rail damages.@en