Track Degradation Research Articles

Railway track degradation: The contribution of a spatially variant support stiffness - Local variation

This study addresses the contribution of spatial variance in the railway track support stiffness to the expected long-term track degradation. Hereto, a novel frequency-domain model is developed with a double periodicity ‘layer’, capable of dealing with both sleeper periodicity and arbitrary non-uniformity in track properties. The model application focuses on a locally reduced support stiffness (hanging sleeper) along the track. The resulting susceptibility to degradation is assessed by quantifying the mechanical energy dissipated over the influence length under a moving train axle. Different descriptions of this energy amount are benchmarked with respect to their predictive value. In the presence of a degraded sleeper support, hanging sleepers are found to develop faster with increasing train speed; the speed effect may be estimated as roughly linear. Moreover, degradation increases progressively with an increasing local relative stiffness reduction. Coincidence of the train speed corresponding to the sleeper passing frequency with the first resonance peak of the system leads to severely increased degradation; increased damping however attenuates dissipation peaks at resonant speeds and shifts their position upwards. The effect of a degraded support is most significant on soft subgrades. The effect of multiple degraded sleeper supports increases up to three sleepers, for any train speed. With respect to the system parameters, particularly the railpad stiffness has significant effect; especially for high-speed tracks a high pad stiffness is very unfavorable. Other effective control parameters in the case of degraded sleeper supports are the sleeper spacing and the rail cross-sectional properties; for example replacing a 54E1 with a 60E1 rail profile may reduce energy dissipation with roughly 30% on high-speed track. An increasing unsprung vehicle mass is unfavorable for track degradation, again with the effect increasing with the train speed. The developed methodology is shown to have significant potential with respect to railway track design in terms of multi-parametric optimization for concrete cases with a given input in terms of soil properties and operational regime.

Modeling of the safe region based on support vector data description for health assessment of wheelset bearings

• A new modeling method for safe region is proposed to handle cases where only normal data are available. • A novel optimization method determines the kernel parameter for the safe region model. • A new indicator derived from the safe region model tracks degradation evolution throughout the whole life cycle. • The proposed methods are applied to anomaly detection and degradation evaluation of wheelset bearings. This paper presents a modeling method, an optimization method, and two applications for the safe region concept that has been developed for the health assessment of wheelset bearings of high-speed trains. The proposed safe region model uses support vector data description to handle cases in high-speed trains where only normal data are available. The optimization process is designed with a strong physical interpretation of the relationship between the kernel parameter and the model performance. A new indicator derived from the safe region model is proposed for degradation evaluation throughout the whole life cycle. Experimental data on wheelset bearings with naturally generated faults are used to demonstrate that the proposed approach has competitive advantages in several performance aspects for anomaly detection and provides monotonic labels in assessment of increasing degradation levels.

Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management

Predicting current and future states of rail infrastructure based on existing data and measurements is essential for optimal maintenance and operation of railway systems. Mathematical models are helpful tools for detecting failures and extrapolating current states into the future. This, however, inherently gives rise to uncertainties in the model response that must be analyzed carefully to avoid misleading results and conclusions. Commonly, Monte Carlo simulations are used for such analyses which often require a large number of sample points to be evaluated for convergence. Moreover, even if quite close to the exact distributions, the Monte Carlo approach necessarily provides approximate results only. In contrast to that, the present contribution reviews an alternative way of computing important statistical quantities of the model response. The so-called point estimate method, which can be shown to be exact under certain constraints, usually (i.e. depending on the number of input variables) works with only a few specific sample points. Thus, the point estimate method helps to reduce the computational load for model evaluation considerably in the case of complex models or large-scale applications. To demonstrate the point estimate method, five academic but typical examples of railway asset management are analyzed in more detail: (a) track degradation, (b) reliability analysis of composite systems, (c) terminal reliability in rail networks, (d) failure detection/identification using decision trees, and (e) track condition modeling incorporating maintenance. Advantages as well as limitations of the point estimate method in comparison with common Monte Carlo simulations are discussed.

Prediction of tram track gauge deviation using artificial neural network and support vector regression

ABSTRACTAlong with increase in tram traffic, gradual increase in gauge deviation is inevitable. Excessive track gauge is considered as an effective parameter in aggravating the quality of ride as well as the traffic safety of tram services. To ensure a smooth ride, reduce the risk of the train derailment and lower cost of maintenance, preventive maintenance operations are necessary which demand accurate track degradation prediction modelling. In this study, the Melbourne tram network is considered as a case study. Two types of rail tracks including straight segments and curved segments are studied. In order to develop models for gauge degradation prediction, two machine learning models including artificial neural network (ANN) and support vector machine (SVM) are applied. Two indexes including the coefficient of determination (R2) and mean squared error (MSE) are used to evaluate the performance of the proposed models. According to the results, both ANN and SVM models provide acceptable and somewhat similar outcomes. However, the performance of ANN models in predicting gauge deviation of straight segments is slightly better than SVM models. On the contrary, the performance of SVM models in predicting gauge deviation of curved segments is slightly better than ANN models.

Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation

Gradual deviation in track gauge of tram systems resulted from tram traffic is unavoidable. Tram gauge deviation is considered as an important parameter in poor ride quality and the risk of train derailment. In order to decrease the potential problems associated with excessive gauge deviation, implementation of preventive maintenance activities is inevitable. Preventive maintenance operation is a key factor in development of sustainable rail transport infrastructure. Track degradation prediction modelling is the basic prerequisite for developing efficient preventive maintenance strategies of a tram system. In this study, the data sets of Melbourne tram network is used and straight rail tracks sections are examined. Two model types including plain Support Vector Machine (SVM) and SVM optimised by Genetic Algorithm (GA- SVM) have been applied to the case study data. Two assessment indexes including Mean Squared Error (MSE) and the coefficient of determination (R2) are employed to evaluate the performance of the proposed models. Based on the results, GA-SVM model produces more accurate outcomes than plain SVM model.

The influence of rubber crumbs on the critical state behavior of waste mixtures

The practical application of waste materials such as steel furnace slag (SFS) and coal wash (CW) is becoming more prevalent in many geotechnical projects. It was found that the inclusion of rubber crumbs (RCs) from recycled tyres into mixtures of SFS and CW not only solves the problem of large stockpiles of waste tyres, it also can provide an energy-absorbing medium that will reduce track degradation. In order to investigate the influence of RC on the geotechnical properties of the granular waste matrix (SFS+CW+RC), a series of monotonic consolidated drained triaxial tests were conducted on waste mixtures. The test results reveal that the inclusion of RC significantly affects the geotechnical properties of the waste mixtures, especially their critical state behaviour. Specifically, the waste matrix can achieve a critical state with a low RC content (<20%), whereas those mixtures with higher RC contents (20-40%) cannot attain a critical state within the ultimate strain capacity that can be applied to specimens using the traditional triaxial equipment. Therefore, for the waste matrix with higher RC contents extrapolation of the measured volumetric strains had to be adopted to obtain the appropriate critical state parameters. Moreover, the influence of energy absorbing property by adding RC on the critical state behaviour has also been captured through an empirical equation.

Geometry mapping and additional stresses of ballastless track structure caused by subgrade differential settlement under self-weight loads in high-speed railways

Abstract Rail irregularities caused by subgrade differential settlement will accelerate track degradation and lower ride comfort and safety. Since the track substructure is normally inaccessible, these problems are hard to be detected early. To study the mapping characteristics of deflection profiles in the ballastless track-subgrade system, a 3D FEM model considering the contacts between different layers was established to simulate different settlement scenarios. The numerical results were first validated by the comparisons of rail deflections with a full-scale physical model testing. Then the influences of subgrade differential settlement on the CRTS II type ballastless track were analyzed, including the deflection profiles, additional tensile stresses and contact stresses. The settlement transfer characteristics from the subgrade surface to the rail were revealed, which were largely dependent on the track equivalent flexibility. A unified formula in terms of the settlement amplitude and track equivalent flexibility was proposed to describe the geometry mapping relationship. The scenario of hanging track structure occurred at the subgrade differential settlement with wavelengths shorter than 15 m, or wave-length between 15 m and 20 m and amplitude larger than 15 mm. The thresholds of unacceptable settlement wavelengths for the additional stresses were 10–20 m for the concrete base and 10–15 m for the subgrade with the settlement amplitudes smaller than 15 mm. Differential settlement with shorter wavelengths was more vulnerable, and the critical scenarios of soil yielding at subgrade surface happened earlier than the concrete cracking in the track structure. Subgrade maintenance works, such as settlement restoration and soil improvement, were suggested to be implemented before the wavelength reached 10 m to avoid further development of settlement and potential threats to the track structure.

Characterization of Ballast Particle Movement at Mud Spot

AbstractMud pumping has been known to cause severe track degradation and compromise the safety of train operations. Mud pumping is normally the result of simultaneous upward intrusion of fine-grain...

25 years of heavy axle load railway subgrade research at the Facility for Accelerated Service Testing (FAST)

Abstract This paper presents a summary of more than two decades of heavy axle load (HAL) railway subgrade research conducted at the Facility for Accelerated Service Testing (FAST). Railway subgrade, if inadequate and not reinforced/improved, can deform and fail progressively, causing significant track geometry degradation under HAL train operations. To understand how railway subgrade behaves under HAL and develop guidelines for design, maintenance and remediation, a number of tests were conducted in multiple phases at FAST to determine how a “soft” subgrade test section would affect track performance and to evaluate remediation methods that can be used to improve soft subgrade support for HAL train operations. Since its inception in the early 1990s, this subgrade testing program has produced a body of knowledge about railway subgrade behavior under 35.4-tonne axle loads. In addition to generating track degradation and maintenance demand data, the testing program provided valuable insight into the relationship between subgrade behavior and track performance. Results were obtained to understand how wheel loads were transferred to the subgrade, subgrade failure mechanisms, and how various soft subgrade remediation techniques performed.

Reliability Modeling and Analysis of Load-Sharing Systems With Continuously Degrading Components

This paper presents a reliability modeling and analysis framework for load-sharing systems with identical components subject to continuous degradation. It is assumed that the components in the system suffer from degradation through an additive impact under increased workload caused by consecutive failures. A log-linear link function is used to describe the relationship between the degradation rate and load stress levels. By assuming that the component degradation is well modeled by a step-wise drifted Wiener process, we construct maximum likelihood estimates (MLEs) for unknown parameters and related reliability characteristics by combining analytical and numerical methods. Approximate initial guesses are proposed to lessen the computational burden in numerical estimation. The estimated distribution of MLE is given in the form of multivariate normal distribution with the aid of Fisher information. Alternative confidence intervals are provided by bootstrapping methods. A simulation study with various sample sizes and inspection intervals is presented to analyze the estimation accuracy. Finally, the proposed approach is illustrated by track degradation data from an application example.

Numerical simulation of track settlements based on an iterative holistic approach

A track settlement algorithm implemented in a multibody simulation software (MBS) is presented. The purpose is to extend the capabilities of a railway track model so a ballast degradation analysis can be performed. The main idea is to implement a methodology that enables a simulation of the train/track interaction and its influence on the overall track settlement mechanism of a regular track section. For this purpose, the common moving track model widely used in MBS codes is changed to a model in which the track stands still. This is done by inserting, in a discrete way, a set of mass–spring–damper systems along the track. The new way in which the track is modelled allows us, in a more accurate way, to take into account different capabilities of the track model, such as track flexibility and damping conditions, discrete support conditions given by the sleeper distance, and the possibility of modelling each track component independently along the track section. In the novel methodology presented in this work, a settlement algorithm has been directly implemented in the MBS package. The latter makes it possible to simulate the track degradation process given by the settlement of the ballast layer. The process consists of two main phases. In the first one, the train/track interaction analysis is carried out. In the second one, the dynamic forces are obtained at the supports along the track, which are then taken and transformed, by applying a settlement law, into vertical displacements that in turn are applied as longitudinal level irregularities in the wheel/rail contact. The process is completed by linking the aforementioned phases in a cyclic mode, considering as many iterations as desired. This work presents an efficient and novel technique that makes MBS code capable of predicting the impact of the accumulated track settlement on the train/track interaction.

Position synchronization for track geometry inspection data via big-data fusion and incremental learning

Track geometry inspection data is important for managing railway infrastructure integrity and operational safety. In order to use track geometry inspection data, having accurate and reliable position information is a prerequisite. Due to various issues identified in this research, the positions of different track geometry inspections need to be aligned and synchronized to the same location before being used for track degradation modeling and maintenance planning. This is referred to as “position synchronization”, a long-standing important research problem in the area of track data analytics. With the aim of advancing the state of the art in research on this subject, we propose a novel approach to more accurately and expediently synchronize track geometry inspection positions via big-data fusion and incremental learning algorithms. Distinguishing it from other relevant studies in the literature, our proposed approach can simultaneously address data exceptions, channel offsets and local position offsets between any two inspections. To solve the Position Synchronization Model (PS-Model), an Incremental Learning Algorithm (IL-Algorithm) is developed to handle the “lack of memory” challenge for the fast computation of massive data. A case study is developed based on a dataset with data size of 18 GB, including 58 inspections between February 2014 and July 2016 over 323 km (200 miles) of tracks belonging to China High Speed Railways. The results show that our proposed model performs robustly against data exceptions via the use of multi-channel information fusion. Also, the position synchronization error using our proposed approach is within 0.15 meters (0.5 feet). Our proposed data-driven, incremental learning algorithm can quickly solve the complex, data-extensive, position synchronization problem, using an average of 0.1 s for processing one additional kilometer of track. In general, the data analysis methodology and algorithm presented in this paper are also suitable to address other relevant position synchronization problems in transportation engineering, especially when the dataset contains multiple channels of sensors and abnormal data outliers.

A knowledge-based prognostics framework for railway track geometry degradation

This paper proposes a paradigm shift to the problem of infrastructure asset management modelling by focusing towards forecasting the future condition of the assets instead of using empirical modelling approaches based on historical data. The proposed prognostics methodology is general but, in this paper, it is applied to the particular problem of railway track geometry deterioration due to its important implications in the safety and the maintenance costs of the overall infrastructure. As a key contribution, a knowledge-based prognostics approach is developed by fusing on-line data for track settlement with a physics-based model for track degradation within a filtering-based prognostics algorithm. The suitability of the proposed methodology is demonstrated and discussed in a case study using published data taken from a laboratory simulation of railway track settlement under cyclic loads, carried out at the University of Nottingham (UK). The results show that the proposed methodology is able to provide accurate predictions of the remaining useful life of the system after a model training period of about 10% of the process lifespan.

A Bayesian assessment for railway track geometry degradation prognostics

Advanced PHM techniques have the potential to substantially reduce railway track maintenance costs while increasing safety and availability. However, there is still a significant lack of knowledge and experience in relation to suitable PHM models and algorithms within the context of railway track geometry degradation. This paper proposes a Bayesian model class methodology for prognostics performance assessment whereby different prognostics algorithms can be rigorously assessed and ranked according to their relative probability to predict the future degradation process. The proposed framework is exemplified and tested for a case study about track degradation prognostics using published data about track settlement, taken from a simulated traffic loading experiment carried out at the Nottingham Railway Test Facility.

Long-term prediction of track geometry degradation in high-speed vehicle–ballastless track system due to differential subgrade settlement

To predict long-term track degradation of ballastless track due to evolution of differential subgrade settlement in high-speed railway, an iterative approach is put forward. A detailed vehicle–track coupled dynamic model taking account of track weight and local contact loss between track and subgrade is employed to obtain the short-term behavior of the system in terms of wheel–rail interaction, vehicle acceleration and interlaminar forces of track structures. The calculated track–subgrade dynamic stresses induced by a high-speed vehicle are imported into an empirical power model for long-term subgrade settlement. The profile of the subgrade settlement is updated by a self-adaptive passing number of vehicles governed by a settlement threshold, and the dynamic responses of the coupled system are re-calculated consequently. On this basis, a demonstration case is carried out aiming at a typical Chinese high-speed vehicle–double-block ballastless track system with an initial subgrade settlement described by the cosine wave. The attained results reveal the high resistance of deformation on high-speed rail subgrade, together with the significant influence of the initial differential subgrade settlement on track geometrical evolution, in particular, the initial condition with severe unsupported areas between track and subgrade. The abnormal dynamic responses inflicted by the differential settlement are gradually alleviated during the long-term operation.

Corrective countermeasure for track transition zones in railways: Adjustable fastener

Transition zones in railway tracks are the locations with considerable variation in the vertical stiffness of supporting structures. Typically, they are located near engineering structures, such as bridges, culverts, tunnels and level crossings. In such locations, the variation of the vertical stiffness and the differential track settlement result in amplification of the dynamic forces acting on the track. This amplification contributes to the degradation process of ballast and subgrade, ultimately resulting in the increase of maintenance costs.The paper studies a corrective countermeasure that can mitigate the track degradation in transition zones when differential settlement appears. The countermeasure is the adjustable rail fastener and its working principle is to eliminate the gap under hanging sleepers by adjusting the shims (height of the fastener). The adjustable fasteners are first tested on three transition zones, wherein the adjusted heights of fasteners (accumulated voiding) are recorded after the 2-month and 5-month operation. The test results show the adjustable fasteners are effective to mitigate the track degradation in the transition zones. The effect of the adjustable fasteners on the dynamic behaviour of transition zones is analysed using the FE method. The results show that the adjustable fasteners are effective to reduce the amplification of wheel forces, achieve a better stress distribution in ballast, and decrease the normal stresses in rails in transition zones. Parametric studies are also performed to study the applicability of the adjustable fasteners.

Uncertainty Propagation Assessment in Railway-Track Degradation Model Using Bayes Linear Theory

AbstractPeriodic inspection could be regarded as a form of insurance to check for variation in the performance of the empirical-based track degradation model. Excessive track inspection, however, h...

Heat required and kinetics of sugarcane straw pyrolysis by TG and DSC analysis in different atmospheres

Herein, it was investigated the heat required and the kinetics of oxidative pyrolysis of sugarcane straw by thermogravimetric analysis and differential scanning calorimetry. Thermal analyses were carried out at different temperatures (5, 10, and 20 K min−1) and atmospheres (different oxygen concentrations with N2). The pyrolysis process had lower track degradation in oxidative atmospheres. The Flynn–Ozawa–Wall model kinetics parameters revealed that the activation energy under the atmosphere with 3% O2 had the lowest range (101–130 kJ mol−1). Hence, it was demonstrated that oxidative pyrolysis could be a low energy cost and environmental friendly process for converting sugarcane straw to valuable products.

Degradation Prediction of Rail Tracks: A Review of the Existing Literature

In the past few decades, the railway infrastructure has been widely expanded in urban and rural areas, making it the most complex matrix of rail transport networks. Safe and comfortable travel on railways has always been a common goal for transportation engineers and researchers, and requires railways in excellent condition and well-organized maintenance practices. Degradation of rail tracks is a main concern for railway organizations as it affects the railway’s behaviour and its parameters, such as track geometry, speed, traffic and loads. Therefore, the prediction of the degradation of rail tracks is very important in order to optimise maintenance needs, reduce maintenance and operational costs of railways, and improve rail track conditions.This paper provides a comprehensive review of rail degradation prediction models, their parameters, and the strengths and weaknesses of each model. A comprehensive discussion of existing research and a comparison of different models of degradation of rail tracks is also provided. Finally, this review presents concluding remarks on the limitations of existing studies and provides recommendations for further research and appraisal practices.

Enhancing rail infra durability through freight bogie design

ABSTRACTThe extensive usage of railway infrastructure demands a high level of robustness, which can be achieved partly by considering (and managing) the track and rolling stock as one integral system with due attention to their interface. A growing number of infra managers consider, in this framework, the track-friendliness of vehicles that have access to their tracks as a key control parameter. The aim of this study is to provide further insight into potential contributions to track-friendliness, assessed in relation to track deterioration mechanisms and cost, understanding how potential benefits are best to be utilised. Six proposed freight bogie design measures are evaluated with respect to the improvement in curving behaviour, switch negotiation and related track degradation mechanisms. To this purpose a sensitivity analysis has been carried out by means of track–train simulations in the VAMPIRE® multi body simulation software. Additionally, the impact on track deterioration costs has been calculated for those track-friendly design modifications identified as most promising. Conclusions show that the standard Y25L freight bogie design displays rather a track-friendly behaviour. Tuning the primary yaw stiffness shows a high potential to further improve track-friendliness, significantly reducing track deterioration cost at narrow radius curves and switches (by, respectively, 30% and 60%). When calculating the overall deterioration cost for the travelled route, the calculation model should include a well-balanced representation of switches and narrow radius curves.