Rail Track Maintenance Research Articles

3D reconstruction and depth estimation method for local anomalies of rail surface based on multi-view stereo matching

Abstract Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. Experimental results demonstrate that the proposed method achieves a minimum error of approximately 5.5% within a maximum depth range of 0.35mm, when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.

Analysis of Variables Influencing Towing Limits in Self-Propelled Rail Track Maintenance Equipment

Analysis of Variables Influencing Towing Limits in Self-Propelled Rail Track Maintenance Equipment

Weld Fatigue Damage Assessment of Rail Track Maintenance Equipment: Regulatory Compliance and Practical Insights

<div>The use of appropriate loads and regulations is of great importance in weld fatigue assessment of rail on-track maintenance equipment and similar vehicles for optimized design. The regulations and available loads, however, are often generalized for several categories, which proves to be overly conservative for some specific categories of machines. EN (European Norm) and AAR (Association of American Railroads) regulations play a pivotal role in determining the applicable loads and acceptance criteria within this study. The availability of track-induced fatigue load data for the cumulative damage approach in track maintenance machines is often limited. Consequently, the FEA-based validation of rail track maintenance equipment often resorts to the infinite life approach rather than cumulative damage approach for track-induced travel loads, resulting in overly conservative designs. The work presented in this article evaluates and compares the weld fatigue damage of track maintenance equipment for EN loads using infinite life approach and AAR loads using cumulative damage approach and highlights the need of having distinct category for track maintenance machines in regulations based on its usage and application to have close representative loads for the endurance limit approach or suitable loads for the cumulative damage approach. Additionally, the study utilizes the BS7608 regulation to determine weld class and predict weld fatigue damage. Both nominal and hot-spot stress approaches are employed to thoroughly investigate and calculate fatigue damage, providing a comprehensive analysis of weld fatigue in rail maintenance equipment.</div>

Microstructural investigation on a rail fracture failure associated with squat defects

Squats, observed in numerous railway networks worldwide, are a significant concern for rail track maintenance. This work presents a specific case study of a rail fracture failure associated with squat defects. A detailed microstructural analysis was conducted to investigate the causes of squat formation, subsequent crack propagation, transverse defects, and fracture. According to the findings, the initiation of squats can be attributed to the presence of White Etching Layers on the rail surface and rolling contact fatigue. After the initiation of Squats, the cracks extended downwards into the bulk rail at a shallow angle until reaching a sizeable martensitic island located in the subsurface of the rail along the path of crack propagation. The brittleness of the undesired martensitic structure triggered rapid crack development, facilitated transverse cracking, and ultimately resulted in rail fracture failure. Transmission electron microscopy examination confirmed that the abnormal defects are plate martensite with a twin substructure and high carbon content, which could be generated as a result of improper heat treatment during the manufacturing of rails.

CFD Analysis of the Flow around Simplified Next-Generation Train Subjected to Crosswinds at Low Yaw Angles

The development of Next-Generation Trains (NGT) made of lightweight materials is a challenging task for the transport industry. It reduces axle loads, which saves money by lowering rail track maintenance costs and the amount of energy needed to drive vehicles. However, the increasing speed and decreasing mass of high-speed trains, on the other hand, raises concerns about the effects of strong crosswinds on their aerodynamics and train stability. As a result, the purpose of this research is to investigate the unsteady flow structure around an NGT in crosswind using a Computational Fluid Dynamics (CFD) technique known as Unsteady Reynolds Averaged Navier-Stokes (URANS). Based on the height of the train model and the velocity, the Reynolds number for the simulation used was . The simulation run in four different crosswind angles: 5°, 10°, 15°, and 20°. The simulation results were compared with experimental results. The findings revealed that a larger yaw angle, which is primarily determined by the incoming wind velocity, can lead to higher flow separation and a more complex three-dimensional flow around the train. Additionally, when the wind angle is small, separation of flow and wakes is limited to the train's end; however, as the wind angle increases, separation of flow occurs from the train's upper and lower corners, indicating that the vortices formed as a result of the flow passing over the roof top and underbody. Finally, the study's findings will contribute to a better understanding of the flow characteristics around an NGT in crosswinds, which is simply impossible to achieve through full-scale testing due to the cost, resources, and accuracy.

A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network

Abstract Rail track breakages represent broken structures consisting of rail track on the railroad. The traditional methods for detecting this problem have proven unproductive. The safe operation of rail transportation needs to be frequently monitored because of the level of trust people have in it and to ensure adequate maintenance strategy and protection of human lives and properties. This paper presents an automatic deep learning method using an improved fully Convolutional Neural Network (FCN) model based on U-Net architecture to detect and segment cracks on rail track images. An approach to evaluating the extent of damage on rail tracks is also proposed to aid efficient rail track maintenance. The model performance is evaluated using precision, recall, F1-Score, and Mean Intersection over Union (MIoU). The results obtained from the extensive analysis show U-Net capability to extract meaningful features for accurate crack detection and segmentation.

Determination procedure of actual drive wheel–rail contact area for railway vehicles

The main function of the wheel–rail system is to ensure the transmission of torque from the wheel to the rail, and to ensure the appropriate traction of the locomotive. Numerous studies have shown that the contact of real rough surfaces is discrete, i.e. random, due to the interaction of individual protrusions of micro-surfaces. Modern science of friction states that when roughness is randomly located on the surfaces of contacting bodies, the contact convergence of these bodies under load determines the actual contact area dependent on the geometry of the contacting surfaces. The friction force in the contact zone is governed by the sum of the friction forces at individual points of interaction. Therefore, the total friction force depends on the actual contact area of the contact surfaces. Therefore, determining its actual value, which is a parameter that ensures the performance of railway transport, is a priority and urgent task. This paper presents a method for calculating the actual contact area, which allows determining the expected value of the traction force in the contact zone of the locomotive wheel with the rail. The application of the results of these studies in maintenance of rail tracks during operation makes it possible to conclude that an accurate determination of the actual contact area can reduce the contact stresses when the wheel and rail surfaces interact by almost 1.9 times.

Machine learning aided rail corrugation monitoring for railway track maintenance

Urban rail transit is a critical infrastructure system that supports urban economic and social development. It has a significant mass transportation capacity while enables environmental benefits. Public transport is a way to resolve large-scale urban road traffic problems and contributes towards sustainable development. However, with the operations of railway vehicles on curves, unbalanced and undulated wears often appear on rails, especially on the low rail. This rail surface defect, so-called 'rail corrugation', directly affects the service life of rolling stocks and track components. The high-frequency vibrations caused by train-track interaction over rail corrugations also impair passenger ride comfort and generate excessive noises. In severe cases, the defects may even endanger the safe passage of a railway vehicle. In practice, rail corrugation has brought huge challenges to the reliable operations and maintenance of railway networks. With the continuous expansion of railway lines and the increasing traffic demands, any existing rail corrugation test method is not enough to meet the actual needs of track maintainers to promptly identify and mitigate rail surface defects. Therefore, this investigation aims to establish a new technique to prognose and classify rail corrugations efficiently and effectively. This study adopts D-track dynamic simulation package to obtain over thousands of vibration data in the form of axle box accelerations from train-track interactions under different conditions. Neural network models have been developed to recognize the rail corrugations and then classify their severity to aid the planning and prioritization of rail track maintenance activities. The models have been trained and tested using the vibration data, achieving the accuracy of over 90%. The optimal model has then been highlighted. The investigation has demonstrated the potential of the neural network to detect and classify rail corrugations, which can be used practically for curved track condition monitoring and maintenance planning.

Fatigue Life Assessment Method for Prestressed Concrete Sleepers

Concrete sleepers are one of the most important applications of a railway track system. Researchers have previously studied the impact load characteristics and ultimate load carrying capacity of a prestressed sleeper but research on the fatigue life of prestressed concrete sleepers is limited. Prestressed concrete sleeper fatigue damage is mainly due to the accumulation of defects, caused by the repeated load of wheel-rail interaction. Fatigue load, fatigue characteristics and the existing design methods of prestressed concrete sleeper are summarized in this paper. The commonly used fatigue assessment methods of concrete structures are also evaluated. Based on the results of former research result, this article presents a convenient fatigue life assessment method for a prestressed concrete sleeper, and contrasts with the test results. The insight information gained can be used to evaluate the service performance and predict the fatigue life of the concrete sleeper, as well providing design flexibility and broadening the design principle. The outcome of this study may also improve the rail track maintenance and inspection criteria, in order to establish an appropriate track condition monitoring network in practice.

Rolling Contact Fatigue on premium rail grades: Damage function development from field data

The concept of the rail damage function provides vital understanding of the operational performance of rail grades in terms of surface degradation. The present study extends this concept from conventional to premium pearlitic rail. This is done on the basis of both simulations of dynamic train-track interaction and field observations. Rolling Contact Fatigue (RCF) damage index values for the conventional R260Mn and the premium R370CrHT grade rail are established, describing the behaviour of the associated damage functions. Defining the individual rail grade damage response to operational loading levels, the potential of the RCF-damage function to support the process of rail grade selection and track maintenance is further discussed.

1st Proctor Lecture of ISSMGE:

Abstract Ballasted rail tracks are widely used throughout the world because they are economical, readily drained, and have sufficient load bearing capacity. Despite these advantages, geotechnical concerns such as ballast degradation, fouling (e.g. coal and subgrade soil), poor drainage of soft subgrade, pumping of clayey subgrade, differential track settlement and track misalignment due to excessive lateral movements exacerbate the cost of track maintenance. Globally, billions of dollars are spent annually on the construction and maintenance of rail tracks. Existing industry design standards are often unable to address these problems because they ignore true cyclic loading patterns, track vibrations, and the onset of plasticity and degradation of track materials. The mechanisms of ballast breakage and deformation, understanding the interface behaviour using geosynthetics, the need for effective track confinement using geocells, time-dependent drainage and filtration properties of track materials require further research to improve existing design guidelines. In view of this, large scale laboratory tests have been carried out using state-of-the-art facilities designed and built at the University of Wollongong and in other proactive rail institutes worldwide in Europe, America, Japan and China. Based on these tests, various factors governing the stress–strain behaviour of ballast, the strength and degradation of ballast, the ability of various geosynthetics and synthetic energy absorbing mats to minimise ballast breakage and track settlement, the effectiveness of subballast as a granular filter and its stabilisation with geocell have been analysed. In Australia, field studies on instrumented tracks at Bulli (near Wollongong), Singleton and Sandgate (near Newcastle), have been carried out to assess the performance of railroad embankments stabilised with geosynthetic grids, rubber mats, and prefabricated vertical drains. This inaugural Ralph Proctor Lecture focuses on the current state of research encompassing deformation and degradation assessment of railroads and the benefits of geo-inclusions, highlighting examples of innovations from theory to practice, predominantly based on the own experience of the Author.

Planning and scheduling efficient heavy rail track maintenance through a Decision Rules Model

The most advanced preventive maintenance for railway track relies strongly on inspection and deterioration assessment to provide a given prediction for a maintenance need. Maintenance of track sections is then decided based on long-term planning for an extended track life and on short-term scheduling, combining maintenance tasks, to reduce the number of track possessions. In the particular case of track geometry correction (TGC), which requires moving costly heavy equipment and specialized crews working over-night, a large outlay is involved as well as relevant track possession costs. However, to the best of our knowledge, no research has yet addressed a combined tactical and operational analysis over the determinant logistics issue involved in the problem. In this sense, we propose a method to plan and schedule the heavy maintenance interventions for TGC, where both travel costs and number of workdays spent on track are minimized, ensuring at the same time, an adequate quality for the track.The method follows a Decision Rules Model (DRM) which is able to provide planning/scheduling solutions by following a set of rules used in maintenance practice. A resolution algorithm that reproduces the optimal solving is developed, whose formulation was established previously. The DRM is able to reach solutions for annual periods with significant reductions in the unit maintenance cost. The method is best applied in the consideration of regular work intensities and, observing a real case of a 300 km double track line, it was possible to conclude that the problem can be reduced to the determination of the frequency of the number of traverses of the complete line (required for maintenance) that ensures the minimum maintenance cost for a given temporal horizon.

Precise Vehicle Location as a Fundamental Parameter for Intelligent Self-aware Rail-track Maintenance Systems

The rail industry in the UK is undergoing substantial changes in response to a modernisation vision for 2040. Development and implementation of these will lead to a highly automated and safe railway. Real-time regulation of traffic will optimise the performance of the network, with trains running in succession within an adjacent movable safety zone. Critically, maintenance will use intelligent trainborne and track-based systems. These will provide accurate and timely information for condition based intervention at precise track locations, reducing possession downtime and minimising the presence of workers in operating railways. Clearly, precise knowledge of trains’ real-time location is of paramount importance.The positional accuracy demand of the future railway is less than 2m. A critical consideration of this requirement is the capability to resolve train occupancy in adjacent tracks, with the highest degree of confidence. A finer resolution is required for locating faults such as damage or missing parts, precisely.Location of trains currently relies on track signalling technology. However, these systems mostly provide an indication of the presence of trains within discrete track sections. The standard Global Navigation Satellite Systems (GNSS), cannot precisely and reliably resolve location as required either.Within the context of the needs of the future railway, state of the art location technologies and systems were reviewed and critiqued. It was found that no current technology is able to resolve location as required. Uncertainty is a significant factor. A new integrated approach employing complimentary technologies and more efficient data fusion process, can potentially offer a more accurate and robust solution. Data fusion architectures enabling intelligent self-aware rail-track maintenance systems are proposed.

Study on track dynamic forces due to rail short-wavelength dip defects using rail vehicle-track dynamics simulations

A rail vehicle-track interaction dynamics model has been applied to determine the track vertical dynamic forces due to rail shortwavelength dip defects such as squat, dip joints and welds, etc., which are required in both rail vehicle acceptance procedure and track maintenance. The model is validated using the field measurement data of rail squat defects and accelerations on a vehicle axlebox. The simulated track dynamic forces — the P2 forces due to rail dipped joints have been compared with those calculated by using a well-known formula. The results are compared and the formula’s limitations have been discussed. The dependence of the track vertical dynamic forces on the rail dip defect size and vehicle speed has also been investigated.

A Heuristic Approach to the Railroad Track Maintenance Scheduling Problem

Abstract: Every year, billions of dollars are spent on rail track maintenance to keep the serviceability of the railroad network. These maintenance projects (of different types) must be performed by suitable maintenance teams within a planning horizon. This article presents a time-space network model to solve the track maintenance scheduling problem (TMSP). The objective is to minimize the total travel costs of the maintenance teams as well as the impact of maintenance projects on railroad operation, which are formulated by three types of side constraints: mutually exclusive, time window, and precedence constraints. An iterative heuristic solution approach is proposed to solve the large-scale TMSP model with a large number of side constraints. The proposed model and solution approach are applied to a large-scale real-world problem. Compared to the current industry practice the model outcome eliminated all hard side-constraint violations and reduced the total objective value (travel costs and soft side-constraint violation penalties) by 66.8%.

THE INFLUENCE OF DISTURBED TONNAGE AND PLAN LINE ON SPENDING DURING THE LINE KEEPING TRACK

The research results regarding the change to expenses of materials for the current rail track maintenance are presented.

New Decision Support System for Optimization of Rail Track Maintenance Planning Based on Adaptive Neurofuzzy Inference System

It is well known that maintenance planning affects, in general, the life of the structures, material wear, and quality of service. In particular, the maintenance of rail tracks affects the traffic volume as well, and therefore it is an important issue for the management of a railway system. Accurate maintenance planning is necessary to optimize resources. The condition of railways is checked by special diagnostic trains. Because of the vast amount of data that these trains record, it is necessary to analyze these data through an appropriate decision support system (DSS). However, the most up-to-date DSSs, such as EcoTrack, are based on a binary logic with rigid thresholds and complicated algorithms with a large number of rules that restrict their flexibility in use. In addition, they adopt considerable simplifications in the rail track deterioration model. In this paper, a neurofuzzy inference engine has been implemented for a DSS to overcome these drawbacks. Based on fuzzy logic, it was able to handle thresholds expressed as a range, an approximate number, or even a verbal value. Moreover, through artificial neural networks, it was possible to obtain more precise rail track deterioration models. The results obtained with the proposed model have been clustered through a fuzzy procedure to optimize the maintenance schedule, thus grouping the interventions in space and in time.

Stabilisation of granular media and formation soil using geosynthetics with special reference to railway engineering

Railway ballast breaks down and deteriorates progressively under train cyclic loading, and soft formation soil fails due to repetitive stress, leading to costly rail track maintenance. Using geosynthetics, track conditions can be improved and maintenance costs can be reduced. This paper addresses the potential use of geosynthetics for improving the deformation characteristics of rail ballast and formation soil. The prospective use of different types of geosynthetics was investigated using a large-scale prismoidal triaxial rig, and a plane strain finite element analysis (PLAXIS) of the rig was carried out to obtain the optimum location of geosynthetics in rail track substructure. A large-scale consolidometer was also employed to determine the effect of prefabricated vertical drains (PVDs) in optimising the accelerated primary consolidation of track soft formation. This paper also includes a section where recommendations are made on how to prepare the stability of rail tracks on surface formation soils considerably disturbed/remoulded by the Asian tsunami in Sri Lanka. The research findings reveal that geosynthetics have a good potential for resilient track construction and for reducing the cost of track maintenance. Le ballast d'infrastructure ferroviaire se fracture et se détériore progressivement sous le chargement cyclique des trains et le sol de formation molle se fracture en raison de la charge répétée. Ces dégradations sont à l'origine d'une maintenance coûteuse des voies ferrées. Les conditions de ces infrastructures peuvent s'améliorer et le coût de leur maintenance diminuer grâce à l'utilisation de la géosynthétique. Cet article illustre l'application potentielle de la géosynthétique pour l'amélioration des caractéristiques de déformation du ballast des voies ferroviaires et du sol de formation. L'utilisation prospective de différents types de géosynthétique est étudiée au moyen d'un montage triaxial prismoïde et une analyse en éléments finis en déformation plane (PLAXIS) du montage est réalisée pour obtenir l'emplacement optimal de la géosynthétique au niveau de la sous-structure de la voie ferrée. Un consolidomètre de grande échelle a également permis de déterminer le role des drains verticaux préfabriqués (DVP) dans l'optimisation de la consolidation primaire accélérée de la formation molle de la voie. L'article présente également une section comportant des recommandations sur les moyens de préparer la stabilité des voies ferrées sur le sols de formation en surface considérablement perturbés / remodelés par le tsunami asiatique qui a touché le Sri Lanka. Les résultats de ces travaux indiquent que la géosynthétique se révèle prometteuse en termes de construction de voies ferrées é lastiques et de réduction des coûts de maintenance des voies.

Discrete Element Method Analysis of Railtrack Ballast Degradation during Cyclic Loading

Ballast materials forming part of railway structures are subjected to cyclic loads. As a result of these loads, ballast densification, aggregate degradation, and lateral spread of the ballast material underneath the ties takes place inducing permanent deformations on the railways. Maintenance and rehabilitation costs of railtracks due to problems related with ballast performance are substantial, and millions of dollars are annually spent around the world in these activities. Understanding the crushable behavior of railtrack ballast could lead to the design of better railways that will reduce these costs. This paper presents the results of two discrete element method simulations intended to study the effect of crushing on the behavior of a simulated track ballast material forming part of a simulated track section. Even though the two simulations consider the same idealized material, crushing was allowed only in one simulation. The simulated track sections were subjected to a cyclic load, and the values of permanent deformation as a function of number of cycles were recorded. The obtained results showed that the induced permanent deformation strongly increased when considering particle crushing even though only a few particles were broken. Moreover, it was found that crushing concentrated underneath the simulated sleepers. Snap shots of the track sections are presented allowing a visualization of the evolution of crushing.

Failure modes in aluminothermic rail welds under high axle load conditions

Aluminothermic welding of rails is widely used within the railway industry for in-track welding during re-rail and defect replacement. The process provides flexibility and low capital cost, but suffers from variable quality in finished welds, due to the inherent limitations of the processes used, and their operator dependency. The service performance of such welds can be considered in terms of the integrity of the joint, i.e. its ability to support the service loads without fatigue damage or fracture, and the batter behaviour of the running surface, which in turn influences the extent of impact loading. Under high axle load conditions, these performance requirements are unforgiving in terms of weld quality and material characteristics. In response to recent failures in aluminothermic welds, and in recognition that such welds represent one of the main risks for a catastrophic derailment, and a major limitation to further increases in axle loads, a program was undertaken to both remedy the current problem with premature failures, and to develop improved rail welding and track maintenance practices which would meet the performance demands of higher axle loads.