Railway Track Geometry Research Articles

Effect of unsupported sleepers on vertical levelling loss of heavy-haul railway track geometry under cyclic loadings

With an emphasis on the combined degradation of railway track geometry and components, an improved numerical approach is proposed for predicting the track geometrical vertical levelling loss (VLL). In contrast to previous studies, this research unprecedentedly considers the influence of unsupported sleepers (US) configuration on VLL under cyclic loadings, elasto-plastic behaviour, and different operational dynamic conditions. The nonlinear numerical models are performed adopting an explicit finite element (FE) package, and their results are validated by field data. The outcomes are iteratively regressed by an analytical logarithmic function that cumulates permanent settlements, and by a power function factor, which innovatively extends the response of US on VLL over a long term. Results shows that at 3 million cycles (or 60 MGT) the worst configuration for 20-ton axle load is at 5 US with 5-mm gap (5,51%), whereas for 30 and 40-ton axle loads is at 5 US with 2-mm gap (1.23% and 0,89%, respectively). This indicates that the axle load affects considerably the VLL as expected, however, the US condition plays an important role to accelerate it. Based on this study, the acceptable configuration of US can be specified for a minimum effect on VLL (thresholds) and, therefore, supports the development of practical maintenance guidelines to prolong the railway track service life.

Non-linear influences of track dynamic irregularities on vertical levelling loss of heavy-haul railway track geometry under cyclic loadings

Abstract With an emphasis on the combined degradation of railway track geometry and components, a new numerical-analytical method is proposed for predicting the track geometrical vertical levelling loss (VLL). In contrast to previous studies, this research unprecedentedly considers the influence of initial track irregularities (ITI) on VLL under cyclic loadings, elastic-plastic behaviour, and different operational dynamic conditions. The non-linear numerical models are simulated using an explicit finite element package known as LS-Dyna, and their results are validated by full-scale experimental and field measurement data. The outcomes are iteratively regressed by an analytical logarithmic function that cumulates permanent settlements, which innovatively extends the effect of ITI on VLL in a long-term behaviour. For a typical heavy-haul railway operating under 30 tons axle load and 60 km/h train velocity, the result indicates that the set of ITI with the highest standard deviation (SD) of vertical profile (VP) degrades faster (37% on average) than that with the lowest SD. Additionally, our new findings reveal that the worst scenario is related to a train running at 60 km/h and carrying a load of 20 tons/axle in an uneven track whose SD of VP evolves from 3.23 mm at N = 0 (ITI) to 7.20 mm, whereas the best one corresponds to a train at 60 km/h and 30 ton axle load in an uneven track whose SD of VP downgrades from 0.48 to 1.50 mm, both at 3 M cycles (or 60 million gross tons). These findings indicate the importance of considering the ITI for predicting track geometrical VLL under cyclic loadings. Therefore, based on this research, an acceptable condition (thresholds) of ITI can be redefined for a minimum effect on VLL, which can support the development of practical maintenance guidelines to extend the railway track service life.

A numerical framework for modelling settlements of railway ballast layers

Permanent deformation in ballast layers is a major contributing factor to the railway track geometry deterioration. In spite of a considerable amount of research on understanding and predicting performance of ballast layers, accurately capturing their settlements remains a challenge. In order to contribute to solving this important issue, a new numerical method for predicting ballast settlements is presented in this paper. This method is based on the finite element (FE) method combined with a constitutive model that captures permanent deformation accumulation in unbound materials under cyclic loading. This allows predicting permanent deformations of large structures and at large number of load cycles in a computationally efficient manner.The developed constitutive model is validated based on triaxial test measurements over wide range of loading conditions. Stress state in ballast layers has been examined with a 3D FE model, for several embankment structures and traffic load magnitudes. The determined stress distributions and loading frequencies were used as an input of the constitutive model to evaluate permanent strains and settlements of ballast layer. The influence of embankment structural designs and traffic loading magnitudes on the ballast layers settlements is examined and the results obtained are compared with the existing empirical performance models.

AN APPROACH TO TECHNOLOGICAL EQUIPMENT SELECTION FOR MEASURING RAILWAY TRACK GEOMETRY

The most-used railway track geometry measurement systems are considered in this study. The advantages and disadvantages of considered systems and the necessity and application of these technologies in European railways were examined. The decisive factors of the operation of measurement equipment were considered. Real-time railway track condition monitoring systems were evaluated by 14 experts according to a scale of 13 criteria. Questionnaire data were processed using Kendall’s rank correlation method, and the mean ranks were normalized using the average rank transformation into weight method. The most relevant evaluation criteria describing the principles of operation and quality of the most promising technologies were defined. The most important criteria for assessing the suitability of technologies were determined by applying the technique for order of preference by similarity to ideal solution. Finally, basic conclusions and recommendations were formulated.

Acceleration-based condition monitoring of track longitudinal level using multiple regression models

In this paper, a condition monitoring system for railway track geometry is presented. The methodology has been designed for high-speed application, where the train travels at the maximum allowed speed for most of the trip. The system is designed to rely on acceleration data recorded by in-service vehicles to provide estimations of the track longitudinal level, based on pre-built regression models. It exploits synthetic indicators sampled over predefined track sections 100 m long. Different predictors are considered, computed both from acceleration data and from track geometry measured by the diagnostic train. The proposed modelling strategy allows distinguishing between isolated and distributed defects that populate the railway track as well as reproducing the evolution over time of the maximum longitudinal level registered in the considered track section; moreover, also accurate predictions of the defect amplitude are made. The results have been validated against track geometry data recorded by the diagnostic train during a monitoring period of 2 years. It is proven that the proposed system could support current maintenance strategies, providing a continuous flow of data to monitor the track infrastructure.

Long-term prediction for railway track geometry based on an optimised DNN method

Railway transportation becomes increasingly important due to the rapidly emerging needs of global trade, environmental protection, and high oil costs. Thus, railway infrastructure companies are required to provide higher reliability and safety for railway transportation. With the fast development of computer science, Deep Neural Network (DNN) models are developed for track geometry prediction to improve the efficiency of track maintenance. However, existing DNN models are still not widely adopted by railway companies because they are often needed to be re-programmed intensively by experienced engineers for customization. Therefore, an optimised DNN model using the Random Search algorithm is developed in this paper for track geometry prediction. The track longitudinal level measured for 2 years on a monthly basis is used to train and validate the prediction model. The results show short-term and long-term prediction accuracy of the optimised DNN model are both high while the customization time is shortened. The growth pattern and the location characteristics of track geometry can be predicted and the maintenance effect can be considered. The prediction results can advise the time and locations for track maintenance.

Railway slab vs ballasted track: A comparison of track geometry degradation

Concrete slab railway tracks typically have higher initial capital costs yet lower operational costs in comparison to ballasted tracks. One reason for the elevated operational costs on ballasted lines is the need for more frequent maintenance (e.g. tamping to maintain track geometry). Therefore on higher linespeed routes where maintenance windows are limited, concrete slab track is a common choice of trackform. In contrast, on lower linespeed routes it’s common to opt for ballasted track structures. However, the speed ranges at which the two different trackforms are most suitable has received limited scientific analysis. Therefore this paper aims to investigate the train-induced differential settlement of ballast and non-ballasted tracks, considering typical modern intercity (200 km/h) and high speed (300 km/h) lines. Vertical railway track geometry is the metric used to analyse maintenance requirements. This is because it defines the longitudinal level of railway tracks and is a common metric used for the scheduling of track maintenance. First a novel numerical model is presented capable of computing track geometry deterioration due to repeated load passages. The model is then validated for both ballast and concrete slab tracks. Finally it is used to study the differential settlement of both track structures at the two train speeds. It is shown that the ballasted track exhibits higher train-induced differential settlement compared to the slab and at higher linespeeds the degradation of track geometry is increasingly pronounced for the ballasted track.

Configuration of the Geometric State of Railway Tracks in the Sustainability Development of Electrified Traction Systems.

The state-space interface of the railway track (track) geometry system with an electrified traction system (ETS) constitutes the geometric configuration that is utilised in this study. Importantly, driving comfort, smooth operation (smooth running), and compliance with the ETS are the desired aims. Direct measurement methods were used in the interaction with the system, especially in regard to the fixed-point, visual, and expert methods. In particular, track-recording trolleys were used. The subjects belonging to the insulated instruments also included the integration of certain methods, such as in the brainstorming, mind mapping, system approach, heuristic, failure mode and effect analysis, and system failure mode effects analysis methods. These were based on a case study and are representative of three real objects, i.e., electrified railway lines, direct current (DC), and scientific research objects (which specifically cover five research objects). The aim of the scientific research work is to increase the interoperability of the railway track geometric state configurations in regard to the sustainability development of the ETS. The results of this work confirmed their validity. By ensuring that the six-parameter defectiveness D6 was defined and implemented, the D6 parameter of the railway track condition was first estimated. The new approach reinforces the improvement in preventive maintenance and reductions in corrective maintenance; moreover, it is an innovative supplement to the existing direct measurement method in the configuration of the geometric condition of railway tracks and in the sustainability development of the ETS via interacting with the indirect measurement method.

The effect of the wavelength of lateral track geometry irregularities on the response measurable by an instrumented freight wagon

Condition monitoring of railway track geometry using instrumented in-service vehicles has been the subject of extensive research aimed at replacing or enhancing the data provided by dedicated track recording vehicles. Lateral track irregularities present difficulties compared to vertical irregularities due to the lateral clearance between the wheel flanges and the rail head. Wheelsets don’t necessarily follow the lateral trajectory of the track. Simulations of a freight wagon with three-piece bogies negotiating both discrete and cyclic lateral track irregularities showed that the resulting lateral displacements were highly dependent on the wavelength of the irregularities. The wheel/rail profiles also had a significant effect whilst the effect of speed was relatively minor. These findings highlight the complexities of identifying exceedances of track geometry standards using only inertial sensors on instrumented freight wagons.

Upravljanje kvalitetom geometrije koloseka

The deterioration rate of railway infrastructure and track geometry increases during their life cycle, and the performance of maintenance activities decreases over time. The average age of tracks in Serbia is over 50 years old, which requires exceptional knowledge and skill of the Infrastructure Manager to adapt the maintenance strategy to the infrastructure condition and achieve safe traffic. Every subsequent derailment on the railway network in Serbia indicates the need to adapt the technical regulation and maintenance strategy to the condition of the infrastructure. The paper presents the requirements and suggestions of the harmonized European standards for managing track geometry quality. It points out the need to adapt the prescribed and recommended limit values to the state of the railway infrastructure.

RAILWAY TRACK CRACK DETECTION SYSTEM

Railways are large infrastructures, and in many countries they are the primary mode of transport. Because of their capacity, speed and reliability the railways have become a prime means of transportation. Only a small increase in railway efficiency has significant economic benefits for the rail industry. Therefore, a proper maintenance plan is required to control inspection frequency optimization and/or skill and efficiency improvements. Accidents that occur due to track breakage have been a major problem for the safety of life and timely service management of the railways. It is important to detect this breakage in real time before a train eventually reaches the damaged track and is subject to an accident. In this paper, the integration of ultrasonic crack detection method and complete station for eternal railway track geometry survey system into use and operation. This system consists of a GPS module, GSM modem, IR sensor, PIR sensor to put crack detection, contact intent and identification of any living being crossing the railroad track into operation. The GPS module and GSM modem lead to railway geometric parameter recognition and transmission of crack detection to nearby railway station. This paper also discusses the summation of a non-destructive ultrasonic test (NDT) and wireless sensor networks (WSNs) to hold the information in a continuous record without interruption in nobility during run-time. The PIR sensor is executed to keep manual patrolling and track finding of living beings away. This can work both during the night and the daytime. The summing up of both WSN and NDT technologies will form different advanced and trending applications to make wireless material scanning more cost-effective in real time.

Water content variation of railway track sub-ballast layer in seasonal frost area: A case study from Finland

Many previous international studies have found a clear relationship between railway track water content and track geometry problems. The proper drainage of substructures is important in ensuring the safe and efficient operation of tracks. This importance will be emphasized in the future with the increasing frequency of extreme weather phenomena and the raising axle weights of train traffic. In many studies, excessive water content has been found to cause excessive geometry deterioration and mud pumping. These problems are usually related to thin substructure layers. However, in this case study, Finland, track substructures are thick due to frost protection requirements and behave differently to thin substructures. In this study, the long-term water content of three instrumented test sites was monitored to study the seasonal and freeze–thaw effects, as well as the effects of drainage improvement made at one site. In a previously reported part of the same research project, the measured water contents were used in an extensive cyclic loading triaxial test series evaluating the loading resistance of substructure materials taken from the same sites. The drainage improvement resulted in a marked decrease in the average water content at the drainage improvement test site. Simultaneously, high momentary water content values also disappeared. The role of capillary water was observed to be less than expected on Finnish railways.

Review of Data Analytics for Condition Monitoring of Railway Track Geometry

Railway track geometry varies along routes depending on topographical, operational and safety constraints. Tracks are prone to degrade over time due to various factors, with deviations from the original geometry design having potential implications for comfort and safety. Regular inspections are carried out to evaluate track condition and determine whether maintenance interventions should be undertaken to correct track geometry. The dynamic measurement of track geometry parameters generates large volumes of data that must be analysed to evaluate track degradation. This work comprehensively explains how track quality is evaluated, introducing four main categories of factors affecting it. These are track design, loading, environment and maintenance. The most common techniques applied to evaluate track condition and predict degradation and faults, categorised into statistical, Machine Learning, Big Data and other, are also introduced. Specifically, the influence of each factor on track geometry is stated and the common techniques applied to each factor determined from this review. The utility of loading and maintenance data for fault prediction depend on the availability of records, whilst the impact of environmental conditions is expected to become increasingly important due to climate change. Artificial Neural Networks, Bayesian models and regression are the most applied techniques for determining track degradation behaviour and fault prediction, considering several different factors in their models. Increasingly sophisticated algorithms can consider multiple factors in tandem to predict faults based on the unique conditions of specified tracks.

Feasibility of Onboard Smartphones for Railway Track Geometry Estimation: Sensing Capabilities and Characterization

The performance and sensitivity of smartphone sensors developed rapidly in recent years. Due to their accessibility and low costs compared to other industrial solutions, the use of smartphone sensors has become more and more common. In this article, the validity and reliability of a smartphone application in railway track geometry estimation are tested on a conventional rail line. This work focused on Galaxy S-series smartphones of Samsung and proposes an evaluation of the onboard sensing capabilities of their inertial sensors with the comparison of synchronous measurements by a multi-functional Track Recording Vehicle equipped with a contactless Track Geometry- (TGMS), and a Vehicle Dynamic Measuring System (VDMS). The raw accelerometer recordings showed a high-degree correlation with VDMS in both signal magnitude and waveform. The accuracy of gyroscope angular tracking in heading and pitching angle calculation was in the range of 0.2°–0.6°, which allowed the acceptable estimation of the central angle and the radius of horizontal curves. Based on the kinematic analysis techniques, the roll flexibility coefficient of the vehicle was determined, which allowed calculating the cross-level and the twist of the track. Furthermore, the local extreme values of the roll-rate gyro correlate with the isolated track geometry defects of the track twist gathered by TRV’s TGMS. Despite its limitations, the application of smartphones represents a prospective technological opportunity to explore new approaches and support rail asset management.

Sequential State and Unknown Parameter Estimation Strategy and its Application to a Sensor Fusion Problem

Soft-sensing and state-space-based methods play crucial roles in fault-tolerant sensor fusion schemes. Kalman filtering is one of the most employed sensor fusion methods. In practice, filtering (or state estimation) is sensitive to initial conditions, variations in model parameters, and faults in sensors and actuators. Cumulatively, these uncertainties that affect the state estimator can be modeled as unknown parameters. In this article, we present the development of a discrete-time minimum variance unbiased estimator (MVUE) that is robust to a class of unknown parameters. The estimation gain of MVUE is designed to facilitate unbiased state estimates in the presence of unknown parameters. We then use the innovations that are generated by the proposed state estimator (MVUE) to estimate the unknown parameters, thus proposing a sequential state and unknown parameter estimation strategy. The development of the decoupled state and unknown parameter estimation strategy is motivated by the fault-tolerant sensor fusion exercise for railway track geometry inspection. This exercise aims to formulate the state-space realization of the track geometry inspection problem to enable sequential state and unknown parameter estimation. The track geometry parameters, initial conditions, and sensor faults are considered unknown parameters for the application. The simulation analysis shows the importance of developing the trinity of the state-space realization for track geometry inspection, the state estimation framework, and the decoupled unknown parameter estimator.

Preparatory Railway Track Geometry Estimation Based on GNSS and IMU Systems

The article discusses an important issue of railway line construction and maintenance, which fundamentally is the verification of geometric parameters of the railway track. For this purpose, mobile measurements have been performed using a measuring platform with two properly arranged GNSS receivers, which made it possible to determine the base vector of the platform. The measuring functionality of the system was extended by IMU. In this article, the effect of measuring conditions on the accuracy of the results collected from GNSS receivers is analyzed. In particular, the advisability of digital filtering of the recorded coordinates to eliminate disturbances is indicated. The article also presents the possible use of GNSS devices and the IMU unit for determining the direction angle and the longitudinal and lateral inclination angles of the railway track. This makes it possible to verify the track geometry in the horizontal plane by determining the positions of straight sections, circular arcs, and transition curves. It is indicated that the results of measurements are repeatable despite the dynamic interaction between the railway track and the measuring platform. The results confirm the usefulness of the applied GNSS and IMU signal processing method for monitoring the geometrical parameters of the railway track in operating conditions.

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review.

Keeping railway tracks in good operational condition is one of the most important tasks for railway owners. As a result, railway companies have to conduct track inspections periodically, which is costly and time-consuming. Due to the rapid development in computer science, many prediction models using machine learning methods have been developed. It is possible to discover the degradation pattern and develop accurate prediction models. The paper reviews the existing prediction methods for railway track degradation, including traditional methods and prediction methods based on machine learning methods, including probabilistic methods, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Grey Model (GM). The advantages, shortage, and applicability of methods are discussed, and recommendations for further research are provided.

Big-data driven assessment of railway track and maintenance efficiency using Artificial Neural Networks

• Big data analysis of railway track geometry using machine learning algorithms. • High-speed railway track quality assessment using artificial neural networks. • High-speed track maintenance (tamping) efficiency evaluation. • Techniques for railway track maintenance optimization. • Data taken from high-speed recording cars – not track based sensors. This study will address the opportunity of using a big data driven approach to providing a more specific description of track quality, mainly selecting segments of the track exhibiting higher settlement with the use of data analytics and machine learning. The focus will be on a high-speed line in the UK with data covering over 15 years of track geometry. Data sets describing track geometry quality have an enormous volume, which means that it is impractical to apply conventional methods to process it fully. The overall aim of this work was to apply an AI technique to analyse the big data. An Artificial Neural Network (ANN) was developed on features from the available data set and used to identify segments of the track where the condition has either improved or deteriorated in the period between two inspection runs. The model achieved an accuracy of ∼ 98 % during training and was able to reliably identify segments of the track which underwent significant changes in measurements. The ANN was used to perform an initial analysis of the geometry data, which revealed that maintenance works (mainly large-scale tamping) may include healthy portions of the track and potentially reduce its life span. Approximately 50 % of the tamped track was not found to improve significantly, based on several different geometric features. On the other hand, local works with a sprinter tamper were much more efficient at eliminating defects.

The effect of rolling stock characteristics on differential railway track settlement: An engineering-economic model

Railway track geometry deteriorates under repeated train loading. When linespeed is increased or new rolling-stock is introduced this can alter the future rate of change of differential settlement and track geometry. Therefore this paper presents a novel combined engineering-economic approach to investigate the effect of increasing train speeds, adding additional passenger movements, and adding additional freight movements to an existing line. Firstly a numerical algorithm is presented to compute differential track settlement. An important novelty of the model is its use of the wavenumber finite element method coupled with settlement relationships in a manner that allows for track irregularities to evolve after every load passage (i.e. taking into account the evolution of the track unevenness profile before applying each subsequent train passage). Unlike traditional approaches this allows the model to faithfully simulate mixed traffic conditions, including the coupled interactions between different rolling stock types and track geometry. The engineering model is used to predict tamping intervals, and then coupled with an economic model capable of calculating deterioration elasticities and marginal costs. It is shown that higher speeds result in higher dynamic forces and cause a faster rate of deterioration of track geometry, thus increasing marginal cost. The model is then used to investigate the effect of adding additional train movements to a passenger line. It is shown that additional movements increase the rate of track degradation and marginal costs, particularly if the additional traffic is freight. This is because freight vehicles typically have one only layer of (stiff) suspension, thus generating elevated dynamic forces compared to passenger vehicles.

Modification of correlation optimized warping method for position alignment of condition measurements of linear assets

This paper proposes a modification to a well-known alignment method, correlation optimized warping (COW), to improve the efficiency of the method and reduce the positional errors in the measurements of linear assets. The modified method relaxes the restrictions of COW in aligning the start and end of datasets and decreases the computational time. Furthermore, the method takes advantage of the interdependencies between simultaneously measured channels to overcome the missing data problem. A case study on railway track geometry measurements was conducted to implement the proposed method and assess its performance in reducing the positioning inaccuracy of the measurements. The findings revealed that the modified method could decrease the positional errors of defects to below 25 cm in 94 % of the trials.