High-Speed Rail (HSR) as a fast, reliable and environmentally friendly mode of transportation has received a lot of attention in recent decades. The International Union of Railways reported that there are more than 18600 miles of HSR in operation and about 1.6 billion passengers per year are carried by them. Although there are plans for HSR in many states including Florida, the United States, however, is still hesitant to develop its own HSR network. One of the main barriers to developing high-speed rail is excessive vibration propagation to the media which may cause annoyance to people who live in the track neighborhood. Train induced vibration also contributes to track settlement, developing track flaws, and increasing life cycle cost of track and supporting structures. The aim of this research is to address this problem by conducting a comprehensive investigation into track dynamics. For this purpose, three-dimensional mass-spring-damper models of vehicle, track and supporting structures were developed and matrices of mass, stiffness, and damping of each subsystem were formed. The response of the whole system was, then, determined by coupling the subsystems using Hertz contact theory. The differential equations of the coupled system were solved by the Newmark integration method and the results including vertical and lateral displacements and forces were presented in the time domain. Since the purpose of this dissertation is to quantify the effect of track and vehicle condition on vibration level, rail defects were also taken into account and rail random irregularities for the vertical profile, Gauge, alignment and cross level (super elevation) were incorporated into a numerical solution. The results of the study show the effect of track and vehicle parameters on the response of the vehicle, track, and substructures. Since Florida and some other states in the United States are very prone to hurricanes, an investigation was conducted into the effect of wind speed on vehicle stability. For this purpose, a curved beam was modeled to consider the influence of track curvature, cant deficiency, wind speed and train speed simultaneously. The results from the study show the maximum allowable values of train speed and axle load for different wind speeds. The findings can be used to decide under what circumstances there is a risk of vehicle overturning and how to avoid it.

In this dissertation, we have developed computer vision methods for measurement of rail gauge, and reliable identification and localization of structural defects in railroad tracks. The rail gauge is the distance between the innermost sides of the two parallel steel rails. We have developed two methods for evaluation of rail gauge. These methods were designed for different hardware setups: the first method works with two pairs of unaligned video cameras while the second method works with depth maps generated by paired laser range scanners. We have also developed a method for detection of rail defects such as damaged or missed rail fasteners, tie clips, and bolts, based on correlation and MACH filters. Lastly, to make our algorithms perform in real-time, we have developed the GPU based library for parallel computation of the above algorithms. Rail gauge is the most important measurement for track maintenance, because deviations in gauge indicate where potential defects may exist. We have developed a vision-based method for rail gauge estimation from a pair of industrial laser range scanners. In this approach, we start with building a 3D panorama of the rail out of a stack of input scans. After the panorama is built, we apply FIR circular filtering and Gaussian smoothing to the panorama buffer to suppress the noise component. In the next step we attempt to segment the rail heads in the panorama buffer. We employ the method which detects railroad crossings or forks in the panorama buffer. If they are not present, we find the rail edge using robust line fit. If they are present we use an alternative way: we predict the rail edge positions using Kalman filter. In the next step, common to both fork/crossings conditions, we find the adjusted positions of rail edges using additional clustering in the vicinity of the edge. We approximate rail head surface by the third degree polynomial and then fit two plane surfaces to find the exact position of the rail edge. Lastly, using rail edge information, we calculate the rail gauge and smooth it with 1D Gaussian filter. We have also developed a vision-based method to estimate the rail gauge from a pair of unaligned high shutter speed calibrated cameras. In this approach, the first step is to accurately detect the rail in each of the two non-overlapping synchronous images from the two cameras installed on the data collection cart by building an edge map, and fitting lines into the edge map using the Hough transform, and detecting persistent edge lines using a history buffer. After railroad track parts are detected, we segment rails out to find rail edges and calculate the rail gauge. We have demonstrated how to apply Computer Vision methods (the correlation filters and MACH filters in particular) to find different types of railroad elements with fixed or similar appearance, like railroad clips, bolts, and rail plates, in real-time. Template-based approaches for object detection (correlation filters) directly compare gray scale image data to a predefined model or template. The drawback of the correlation filters has always been that they are neither scale nor rotation invariant, thus many different filters are needed if either scale or rotation change. The application of many filters cannot be done in real-time. We have succeeded to overcome this difficulty by using the parallel computation technology which is widely available in the GPUs of most advanced graphics cards. We have developed a library, MinGPU, which facilitates the use of GPUs for Computer Vision, and have also developed a MinGPU-based library of several Computer Vision methods, which includes, among others, an implementation of correlation filters on the GPU. We have achieved a true positive rate of 0.98 for fastener detection using implementation of MACH filters on GPU. Besides correlation filters, MinGPU include implementations of Lucas-Kanade Optical Flow, image homographies, edge detectors and discrete filters, image pyramids, morphology operations, and some graphics primitives. We have shown that MinGPU implementation of homographies speeds up execution time approximately 600 times versus C implementation and 8000 times versus Matlab implementation. MinGPU is built upon a reusable core and thus is an easily expandable library. With the help of MinGPU, we have succeeded to make our algorithms work in real-time.

Les phénomènes dynamiques observés lors de la circulation des trains provoquent des nuisances, notamment sonores et vibratoires, qui sont amplifiées par la présence de défauts sur la roue et sur le rail. Pour les analyser, il est nécessaire de prédire avec robustesse le comportement dynamique des composants impliqués dans l’interaction véhicule-voie et donc de simuler les efforts de contact générés pour des interfaces non idéalisées.L’objectif de cette thèse est donc de proposer un modèle semi-analytique global compatible avec l’intégration de multiples défauts géométriques sur les surfaces en contact. Afin de simuler l’interaction véhicule-voie dans le domaine temporel et garantir une applicabilité en phase de dimensionnement, une attention particulière est portée sur le compromis entre la précision des résultats et les temps de calcul associés.Le modèle ainsi proposé est composé d’un demi-bogie, dont le comportement vertical est représenté par un ensemble de masses-ressorts-amortisseurs, circulant sur une voie ballastée. Cette dernière est assimilée à une poutre bi-appuyée, supportée périodiquement à l’emplacement des traverses. Ces deux systèmes sont couplés en contact grâce à une procédure Distributed Point Reacting Spring (DPRS) sous forme discrétisée.Une validation du modèle est, d’une part, proposée en considérant des travaux antérieurs dans le cas de géométries parfaites. D’autre part, de multiples combinaisons de défauts, localisés comme le méplat ou répartis comme l’usure ondulatoire, sont introduites dans la simulation. La variabilité spatiale, particulière au cas de l’écaillage, est modélisée par des champs aléatoires. / The appearance of dynamic phenomena during the running of train on track leads to issues such as noise and vibration pollution, which can be further amplified by the presence of defects on the treads. In order to analyze them, it is necessary to predict with reliability the dynamic behavior of the vehicle-track interaction components, in particular the contact forces produced by non perfect treads.The aim of this PhD thesis is to provide a semi-analytical vehicle-track interaction model able to take into account multiple defects on the surfaces in contact. In order to conduct simulations in the time-domain and ensure applicability in the sizing phase, a special attention is given on the compromise between the accuracy of the results and the simulation times.The proposed model is therefore composed of half a bogie running on a ballasted track. This latter is modeled by a pinned-pinned beam with periodic supports located at the sleepers while the vertical behavior of the bogie is given by masses, springs and dampers. These two models are coupled in contact by a discretized Distributed Point Reacting Spring (DPRS) procedure.A validation of the model, based on previous work, is firstly proposed for perfect treads. Then, multiple combinations of defects, either localised as wheelflat or spread as corrugation, are introduced in the simulation. The spatial variability, specific to shelling, is modeled by random fields.

This study focuses on the reliability of railway track in the relation with high loads resulted from wheel damages, where higher (dynamic) vertical wheel loads are expected from trains with damaged wheels. The Swedish Transport Administration (Trafikverket) has monitored and recorded data of rail defects and breaks. This data is important for the risk evaluation and management process, in order to obtain a balance between the maintenance costs and availability and quality of service for the railway transportation. To detect wheel damages in advance, Wheel Impact Loads (WIL) are measured and recorded by Wheel Impact Load Detectors (WILD), which are installed along the Swedish railway network, currently at 29 locations. Additionally, to detect defects and breakages in rails, Ultrasonic Testing (UT) is a common non-destructive testing method used by Trafikverket. In this project, a case study is conducted to examine statistically the frequency of rail defects and rail breaks after trains with high WILs, which exceed 350 kN, continued to operate in a defined area during the winter of 2019/20 at Bodsjön. The case study compares the data of WILD at Bodsjön and UT of the nearby track sections, 211 and 212, over a period of five years, from 2016 to 2020. Rail reliability levels are examined based on the absolute frequencies of rail defects detected by UT and the normalised ones per kilometre of UT-checked track, as well as the severity levels of those rail defects. The severity levels are denoted from the highest to the lowest, as 1a/ 1v/ 1m, 2b and 3i, based on the derailment risks and the priority of maintenance. It is observed that the extremely high WILs in the winter 2019/2020 has coincided with the more severe rail defects found by the following UT in 2020. Furthermore, the dominant rail defect type is found as squats. By studying the frequency of squats and their locations along the defined track sections, it is shown that there is a similar trend in the frequencies of squats and the high WILs. While, the effect of the high WILs’ magnitudes onto the occurrence of squats is undefined. However, the correlation between the frequencies and magnitudes of high WILs and the rail reliability levels could not be determined from this case study due to the limitations in the data collections. The current way of data collections for WILs, wheel damage types and rail defects are discussed for future works. Lastly, the statistics shows that the level of rail reliability of the area defined in the case study is considered as acceptable, considering the recent national levels that have been reported in the annual reports on rail defects. This gives the basis for the possibility of reviewing the permissible power level and proposing a new alert limit value of WIL. Nevertheless, considering the recommendation of The International Union of Railways (UIC) for the international railway sections, the implementation of the increased alert limit of WIL shall be examined carefully, with a future pilot study in railway sections that are not used for international traffic. / Denna studie fokuserar på tillförlitligheten för järnvägsspår i samband med höga laster som orsakas av hjulskador, där högre (dynamiska) vertikala hjullaster förväntas från tåg med skadade hjul. Trafikverket har övervakat och registrerat data om järnvägsfel och avbrott. Dessa uppgifter är viktiga för riskutvärderingen och hanteringsprocessen för att få en balans mellan underhållskostnader och tillgänglighet och kvalitet på järnvägstransportens kvalitet. För att upptäcka hjulskador i förväg, mäts och registreras Wheel Impact Loads (WIL) av Wheel Impact Load Detectors (WILD), som är installerade längs det svenska järnvägsnätet, för närvarande på 29 platser. För att upptäcka defekter och brott i skenor är Ultrasonic Testing (UT) en vanlig icke-destruktiv testmetod som används av Trafikverket.I det här projektet, genomförs en fallstudie för att statistiskt undersöka frekvensen av järnvägsdefekter och rälsavbrott efter tåg med höga WILs, som överstiger 350 kN, fortsatte att köra i ett definierat område under vintern 2019/20 vid Bodsjön. Fallstudien jämför data från WILD vid Bodsjön och UT för de närliggande spåravsnitten, 211 och 212, under en period på fem år, från 2016 till 2020. Järnvägssäkerhetsnivåer undersöks baserat på de absoluta frekvenserna av järnvägsdefekter som upptäcks av UT och de normaliserade per kilometer UT-kontrollerat spår, liksom svårighetsgraden av dessa järnvägsdefekter. Svårighetsgraderna anges från det högsta till det lägsta, som 1a/ 1v/ 1m, 2b och 3i, baserat på urspårningsriskerna och underhållets prioritet.Det observeras att de extremt höga WILs under vintern 2019/2020 har sammanfallit med de allvarligare järnvägsdefekterna som hittades av följande UT år 2020. Vidare återfinns den dominerande järnvägstypstypen som knäböj. Genom att studera frekvensen av knäböj och deras platser längs ett definierat spåravsnitt, visas det att det finns en liknande trend i frekvenserna för knäböj och de höga WILs. Medan effekten av de höga WILs storheter på förekomsten av knäböj är odefinierad. Korrelationen mellan frekvenserna och storheterna för höga WILs och järnvägssäkerhetsnivåerna kunde dock inte fastställas från denna fallstudie på grund av begränsningarna i datasamlingarna. Det nuvarande sättet för datainsamling för WILs, hjulskadetyper och järnvägsdefekter diskuteras för framtida arbeten.Slutligen visar statistiken att nivån på järnvägstillförlitlighet för det område som definieras i fallstudien anses vara acceptabel, med tanke på de senaste nationella nivåerna som har rapporterats i de årliga rapporterna om järnvägsdefekter. Detta ger grunden för möjligheten att granska den tillåtna effektnivån och föreslå ett nytt varningsgränsvärde för WIL. Med tanke på rekommendationen från The International Union of Railways (UIC) för de internationella järnvägssektionerna, ska genomförandet av den ökade varningsgränsen för WIL undersökas noggrant, med en framtida pilotstudie i järnvägssektioner som inte används för internationell trafik.