This thesis transforms the way we understand and monitor rail infrastructure with a digital twin that merges measurement data and physics-based models to deliver instant insights. This thesis will be of particular interest to professionals in the rail industry seeking to answer the following questions: What are the key features in the measurement data? How can an accurate physics-based vehicle-track interaction model be developed for a specific problem? And, how can mearsurement data and models be combined to deliver actionable insights in real-time?@en

While various train-borne techniques have been developed for measuring railway track stiffness, differentiating stiffness at different track layers remains a challenge. This study proposes a digital twin framework for the vehicle–track interaction system, which enables track stiffness evaluations based on axle box accelerations (ABA). The digital twin consists of a physics-based model, a model library and data-driven models. Compared to existing techniques, the proposed method simultaneously evaluates the stiffness of the railpad, sleeper and ballast layers at a sleeper spacing resolution, while being robust to varying track conditions, such as track irregularities and vehicle speeds. This is accomplished by employing a localized frequency-domain ABA feature capable of distinguishing between the characteristics of different track layers. Furthermore, track stiffness is evaluated in near real-time. This is achieved using a model library derived from physics-based simulations of a range of track conditions. Two data-driven models that can quickly select or interpolate model instances contained in the library are developed. During operation, the data-driven models use the measured ABA features as input and then infer the stiffness for the different track layers. The proposed method is applied to evaluate the track stiffness of a downscale test rig in a case study. The track stiffness evaluated by the proposed method is compared with that obtained through hammer tests and with the observations of the track component conditions. These comparisons show that the proposed method can capture the stiffness variations due to periodically fastened clamps and substructure misalignments at different speeds. In addition, the proposed method is demonstrated to be superior to the commonly used hammer test method for evaluating track stiffness under loaded conditions.@en

This paper presents a big data-based analysis of the rail wear of the whole Belgian railway network measured in 2012 and 2019. Wear rates are reported, discussed, and quantitatively formulated as functions of critical factors in terms of curve radius, annual tonnage (rail age), high rail in curves, an average from both rails in straight tracks at rail top (vertical wear) and gauge corner (45° wear) and for steel grade R200 and R260. The influence of preventive grinding is also analysed. The wear rates are derived in an aggregated manner for the whole network. The wear rates do not show significant change with changes in rolling stock over the years, implying that the wear rates could also hold for other networks. It is found that R200 shows, on average, a 34% higher wear rate than R260. Also, the wear rate per tonnage is lower for high-loaded tracks. Thus, time is a relevant factor in explaining the wear evolution of low-loaded tracks; for instance, the effect of corrosion may have an important role. The paper provides statistically significant information that can be used for wear modelling, understanding and treating rolling contact fatigue based on the wear rate and developing tailored rail maintenance strategies.

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Dit rapport behandelt een wissel die geen bewegende delen bezit en hierdoor geen controle circuit nodig heeft. Het rapport bezit een analyse van verschillende bestaande wissels en wisselconcepten, beide in hoofdspoor en tramspoor, om een evaluatie te geven van elk concept. Op deze manier is bepaald of een zodanig "statische wissel" wenselijk is in het Nederlandse spoor. Er is vastgesteld dat dit niet het geval is, daarentegen andere wissels wel. Er wordt daarom aangeraden om meer onderzoek te doen naar deze andere aanpassingen aan wissels.

This report considers a switch that does not have moving parts and therefore has no control circuit. The report contains an analysis on current existing switches and switch designs, in heavyrail but also tramrail, to give an evaluation of the different concepts. This way it was determined if a so called "static switch" could be wished for in the Dutch Railroads. It was concluded that this kind of switch was not profitable, but other switches were. Recommended was, that certain (existing) changes in switch designs should be looked into more.

Track quality measures differ from standard to standard. Widely used indices for track quality are the standard deviations of the rail longitudinal level data and the standard deviations of alignment data. Researchers have opted for various learning methods as to relate track quality to different parameters. Some research has gone to the point of using track quality interchangeably with the notion of defect density. What is common in all researches is that a track quality index is used as label for predictions. The differences are inherent to the variables/features that were chosen to conduct such predictions. With the plethora of data at the hands of asset managers nowadays, it has become easier to opt for these learning methods. Machine Learning classification is a method wherein a number of features are used to classify a datapoint into a label. With thorough learning, by inputting the features, the label would then be predicted. The features of interest in this project are structural elements describing the track sections. This can go from the sleeper type and subsoil type all the way to the distance from the nearest switch or bridge. This will allow for the relationships between the structural elements and the track’s quality to be bolstered all the while creating a predictor based on data that need not be measured. The research question is thus formulated as follows: How to relate track quality in a section to its surrounding structural elements and form a predictor out of it? 1.What structural elements are to be studied? 2.How to automate the generation of the features? 3.Which classifier algorithm yields the most accurate predictions? 4.Which structural features are the most important in predicting track quality? The research question is divided into 4 sub-research questions each pertaining to a part of the research. 1.Decide on which structural elements are the most relevant for the study. Some elements may be redundant with respect to other elements. For instance, subsoil type and ballast settlement could form a redundancy. This will be decided on from the literature. This part will contribute to a section in the thesis in which a strong relation will be drawn with respect to the courses taken and studied at the TU Delft. 2.Data Gathering is a huge part of this research. Input data will be from Fugro’s relative track geometry data and KRDZ data. In addition, ProRail’s informatieportaal, which is a website containing all the railway assets and their locations on the map will also be of huge aid to gather the structural elements data. The answer to this sub-research question lies in automating the structural feature generation, making them ready for learning. 3.Running the Machine Learning classification code on sections of a real-life track and comparing the accuracies of each classification algorithm as to come up with recommendations for future work. 4.Some machine learning algorithms, namely the random forest classifier have in their output an importance of features measure. This will give us insight on which structural elements affect track quality the most, which would allow for better resource allocation in the long run. In the goal of answering all the research questions and to realize the research objective, an outline of the research is included in this report along with a workflow diagram. The project has shown that predicting track quality using structural elements and relative track geometry can be done using classification methods, on the condition that the predicted track and the trained track have similar feature importance values, and that structural features that are displayed in the track to predict are also available in the trained model.

In this master thesis the behaviour of a high speed railway track constructed on top of a pile foundation with settlements under a dynamic load has been analysed. Therefore, a model was developed in the software program DARTS, dynamic analysis of rail track structures. In DARTS the railway track was modelled by stiff and elastic layers, which were loaded by a moving train load. The train was modelled as a mass spring system. The results of DARTS were analysed and validated with field measurements of the HSL. After the validation of the model multiple simulations were performed to analyse the structure for different uncertainties and possible solutions. These simulations showed that the model in DARTS is sensitive for changes of the pile stiffness. The results also show that it is difficult to model permanent displacements in DARTS.

Railway sleepers form an important part of a classic track structure. Sleepers exists in their current form for a long time. The transition from wood to concrete sleeper has been made, but wood is still used and present in many track sections. As creosoted wooden sleepers are not allowed to be installed on the tracks anymore the search for durable and sustainable alternatives has started. One promising type is a hybrid sleeper constructed from recycled plastic (polyethylene) with steel reinforcement. The main part of this thesis is to create a finite element model that is capable of describing hybrid plastic sleepers. The ultimate goal is to be able to use this model to assist in sleeper acceptation. Current rules, regulations and high availability requirements make it difficult to test sleepers in the track under live loading conditions. Not many infrastructure managers are eager to install not already proven technology in their tracks. The general parameters of the model were investigated and combined with some specific finite element modelling methods. Those formed the starting point of the design. Important design methods were the parametrisation of the model as much as possible to allow assessment of slightly different models. Secondly a mapped mesh was preferred above a free mesh to improve on accuracy. Also the amount of detail is kept high to be able to fully investigate the sleepers, e.g. to investigate the reinforcement bond. The ANSYS APDL-language is used to program the FEM parametrically. This resulted in a comprehensive finite element model of one a whole sleeper. Fully modelled with base plate (without detailed fastening), rail pad and rail section. Every part is constructed out of solids and meshed with a mapped mesh including the reinforcement. Two model methods, a general circular and a more element wise optimal octagonal model, were used to generate the reinforcement. All single sleeper models can be connected to form a piece of track with several possibilities in altering the foundation parameters per sleeper or allowing for different types of sleepers inside the track. Concluding all generated finite element analysis (FEA) results could not impress enough to recommend the usage of FEA for sleeper acceptation. Especially with new, very non-linear behaving materials and a very dynamic loads, the effort involved in creating a validated finite element model (FEM) would be too great and the results not usable enough. For some parts a FEM can be beneficial, during design for example. Other simpler models than the one constructed here could be helpful if directly based on test results. But at least at this point in time, with relatively unknown materials, the direct testing of sleepers under loading conditions laboratory and in the track itself with close monitoring are regarded much more informative.

The high speed rail line in the Netherlands, constructed since 2002, is built to create a fast travel connection from Amsterdam to Rotterdam and the Belgium border. The track is designed for trains with a maximum operating train speed of 300 km/h. A great part of the structure is founded on typical Dutch soft soils consisting of Holocene clay and peat layers. To ensure high speeds, mostly settlement free plates with a ballastless track structure are constructed to produce optimum track qualities. At the village Rijpwetering a part of the track, parallel to the highway, has shown lateral deformations since construction. This has resulted in the research question “What are the main factors driving the horizontal deformation mechanism in the high-speed rail settlement free plate structure and what measures can be taken to solve these horizontal deformations?” The research consists of an analysis of the measurement results since 2005. From the measurement results a clear division can be made between the supported and the unsupported sections of the structure. The supported sections have almost stopped displacing in time with an average deformation rate of 0 to 0.5 mm per year. Unsupported sections show an ongoing deformation with a rate of 2.7 to 3.3 mm per year. This deformation will continue and lead to problems concerning structural safety due to bending moments in the top of the foundation pile. To identify the mechanism, a finite element modelling of the track structure embankment is performed from which the deformation behaviour is assessed. Multiple geometrical variants are considered which lead to asymmetrical horizontal deformations. From the simulations an asymmetrical soil layer with a varying stiffness in the subsoil on either side has led to the highest deformation behaviour. In other words, an embankment partly founded on soft soils (peat) and partly on stiff soils (sand). This can be identified as the main factor driving the horizontal deformation mechanism. The combination of different variants leads to results corresponding to the field measurements. To stop horizontal deformations the model is updated with 2 variants to observe the effectivity of the measures. One option is to apply a prestressed sheetpile wall, the other option is to reduce the structures weight with a low weight material (EPS). By applying a prestressed sheetpile in the slope of the embankment the resulting deformations can be stopped when applying a prestressing force of 100 to 150 kN. For the weight reduction, the EPS has to be placed in the slope on the soft soil. Reducing the weight with 144 kN/m or when applying EPS an area of 8.5mm2 stops further deformations. Applying weight reduction on the side of the stiff soil will only increase the problem and deformations will grow. Since weight reduction is usually a cheaper and a lower risk measure in comparison to prestressed sheetpile structures, it’s a very interesting alternative to investigate more accurately. Depending on the surrounding structures and construction possibilities the best option can be chosen to reduce deformations in this structure and prevent deformations in future structures.

In this paper, we present preliminary results on the development of smart technology solutions for lower density railway lines. The goal is to reach a cost effective inspection and asset management to minimize maintenance interventions time/cost without dedicated inspection vehicles. The proposed methods include axle box acceleration measurements and ultra-low cost smartphones. The collected data will be further used to increase knowledge of the condition of the railway track and to estimate the comfort of passengers. In order to make use of the data, the data is interpreted and converted from monitoring information into management information. Feasibility and preliminary studies were conducted in the railway lines of Romania. The results presented in this paper were obtained in the framework of the H2020 project NeTIRail-INFRA, Work Package 4: Monitoring and Smart Technology.@en

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using Hilbert-Huang Transform, the locations where the major risks are detected and ssessed with a degradation model. Finally, evolutionary multiobjective optimization is employed to solve the maintenance decision problem, and to facilitate the visualization of the trade-offs between number of interventions and performance. Real-life measurements from the track from Braşov to Zărneşti in Romania are included to show the methodology.@en