Railway transition zones represent a significant challenge due to abrupt variations in substructure properties, leading to differences in vertical strain. These differences cause the wheel-rail dynamic interaction to get excited, which can cause passenger discomfort, track deterioration, and derailment. Consequently, proactive design strategies are crucial to mitigate these stresses. In previous research, an optimal geometry of railway sleepers was designed, however it was assumed that every sleeper would have the same geometry. This renders the design to be susceptible to inherent uncertainties. To address this limitation, a new design should be made incorporating uncertainties. However, railways involve numerous parameters
influencing performance. Employing all parameters within an optimization framework is computationally not feasible. Therefore, this thesis aims to identify the most influential design variables within a railway transition zone. This leads in the following research question:

How can the most influential design variables on the performance of a railway transition zone be determined using a static model while taking uncertainties into account?

In this research, the design variables have been limited to only railway sleepers. To answer this question, a comprehensive review on how uncertainty is implemented into designs was done. Leveraging this knowledge, Optimized Latin Hypercube Sampling (OLHS) was employed to generate samples defining the length and width of each sleeper within the transition zone. These samples were then used as input variables within a static Ansys model, modified to mimic dynamic factors. This model subsequently calculates the stress and settlement in the ballast and soil layer for several locations across the transition zone. This process is repeated for ten different samples created with OLHS. The location exhibiting the highest settlement and stress for each sample was identified, resulting in a dataset for comparison between the
samples with the most favorable and least favorable performance.

One pattern that can be seen in the results is that higher stresses occur when there is a higher
disparity between adjacent railway sleepers. This may be the cause of differences in bearing surface area, where longer sleepers spread the load from the rails over a larger area than shorter sleepers. This causes uneven settlement, and consequently higher stress concentrations. If this hypothesis holds true, incorporating gradual transitions in sleeper length throughout the transition zone becomes a critical design consideration for future optimization efforts. However, further research is warranted to definitively validate this observation.

This thesis explores the use of Axle Box Accelerations (ABA) in passenger trains as a means to monitor and assess the structural health of railway turnouts, particularly the crossings. The increasing demand for higher capacity, speed, and axle loads, coupled with reduced maintenance time, has put a strain on the reliability of these sensitive components. The study focuses on the fixed crossing, a vulnerable component due to its discontinuity that can cause large impact forces leading to potential failure and disruptions.

The research aims to develop an assessment method to determine crossing health using ABA measurements from two retrofitted intercity trains. The crossing, formed by the wing rails and a crossing nose, presents a discontinuity that disturbs wheel motion and generates impact forces. The study acknowledges the challenges related to data quality, quantity, and processing due to the random nature of measurements and conditions. Despite these challenges, the research leverages the Law of Large Numbers and the Central Limit Theorem to overcome frequency-limited measurements.

The proposed assessment method is validated against geometry metrics derived from train-borne laser measurements. However, the current approach does not establish a reliable relationship between the impact angle and wear levels, making it unsuitable for monitoring wear levels of crossings. Nevertheless, a strong correlation was found between the kinematic wheel dip angle, mean and standard deviation of vertical impact, indicating that a larger mean or standard deviation suggests a more degraded vertical geometry. This finding is in agreement with prior research based on wayside measurements and underscores the potential of in-service passenger train ABA for assessing the structural health of turnout crossings.

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.

For railway travel the Insulated Rail Joint (IRJ) is a critical part in most railway safety systems but at the same time also considered a weak link. The IRJ creates a discontinuity in stiffness and geometry that leads to wheel-rail impact forces. The angled IRJ is a proposal to reduce these impact forces. Despite the real-world experiences with angled IRJs the research on the topic is marginal. In this thesis a study is presented into the dynamic behaviour of the wheel-rail impact occurring at IRJs. Numerical models are established to simulate a wheel rolling over IRJs with angles of 0, 15, 30 and 45 degrees, using the implicit-explicit sequential finite element method with the software ANSYS/LS-DYNA. The impact forces are the main output and analysed. Three variations of the IRJ models were used for simulations. The basic version IRJ model is built up with a fully constrained rail foot to simulate an infinite support stiffness condition. The second version is supported by spring and damper elements to simulate supports such as ballast and rail pads. Height differences between the rail ends are generated when a wheel passes the joint, which corresponds to a real-world scenario. Different degrees of support degradation are simulated by varying the support stiffness, using the spring and damper element parameters. The third version also incorporates the spring and damper elements as support but couples some nodes between the fish plate and rail web in vertical displacement to reduce the rail height difference caused by wheel pass-by, aiming to simulate a ‘factory-new’ condition joint. The established models were validated in terms of wheel-rail contact solution and contact force amplitude. In comparison with existing FE wheel-rail impact models the proposed models in this thesis are less time consuming and more flexible for joint angle adjustments. The second version simulating degraded joint conditions provided the most interesting findings and was used in a sensitivity analysis by varying the velocity and wheel load. The simulation results show that angled IRJs are advantageous when degradation is present, whereas in ‘new joint’ conditions the gap width plays a significant role and angled IRJs with larger gaps produce higher impact forces. Based on the results the recommendation is given to consider angled IRJs on tracks with a low maintenance scheme and further research is suggested.

This thesis project discusses the development of a fast assessment tool for the performance of railway turnout crossings. The assessment tool is developed in Matlab and allows the user to swiftly set up a model and assess the performance. The rail geometry input can be either design drawings or in situ measured geometry. This allows the user to compare designed, delivered and deployed crossings. Comparisons are made between several types of 1:9 crossings, by assessing the designs from various countries. The vehicle-track interaction is modelled in the commercial multi-body system (MBS) analysis package VI-Rail. The simulations indicate that the automated workflow provides insights similar to manual modelling, while being much faster. The report ends with recommendations based on the geometry comparisons and simulation results.