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With rapid advances in sensor and condition monitoring technologies, railway infrastructure managers are turning their attention towards the promise that digital information and big data will help them understand and manage their assets more efficiently. In addition to the existing track geometry records, it is evident that track stiffness is a key...
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Transport system provides one of the basic infrastructures and acts as a prerequisite for socioeconomic development of a country. To ensure a cost-effective, environment friendly and safe freight transport, choosing the best mode of transport is necessary. Dhaka and Chittagong are the two major metropolitan cities of Bangladesh. Dhaka is the main c...
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... For the past three decades, many researchers have investigated the track settlements in a smooth track (without track geometry irregularities) and have proposed different approaches to predict the track geometrical VLL, mostly focusing on ballast settlement, as pointed out by Melo et al. [2,19]. Abadi et al. [20], Thom and Oakley [21], Dahlberg [22], and Grossoni et al. [23] carried out outstanding literature reviews revealing that there were no typical proceeds among investigation conditions and their outcomes. Also, most methods indicate dependency only on the number of cyclic loadings without taking into consideration any difference into railway dynamic conditions. ...
... They showed that it was possible to assess the development of both the dynamic interaction forces between the vehicle and the track and the vertical track irregularities with low computational time compared to Finite element method (FEM). Grossoni et al. [23] investigated analytically the role of track stiffness and its spatial variability through a set of computational experiments estimating the track geometry degradation rates. They suggested that the vertical track interactive model can calculate the evolution of the rail track irregularities under a particular cumulative empirical settlement law. ...
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.
... However, the variance of the predicted settlements in the literature and even the variance of the measured settlements in the same experimental tests are high. Figure 2 shows the range of the predictions of the settlement accumulation by the present phenomenological models for the in situ measurements and full-scale tests (Grossoni and Andrade 2019;Lichtberger 2005). The high range of variance from about 1 to 16 mm for 500,000 of load cycles could be explained by different test conditions. ...
The high influence of impact and vibration on the behavior of crushed stone and ballast materials has been known for a long time. The zones with unsupported sleepers, which are always present in transition zones, crossings, welds, etc., are typically characterized by impact interaction, ballast full unloading, and additional preloading. However, no studies on ballast layer settlements consider impact vibration loading. Moreover, the influence of the cyclic loading on the ballast settlement intensity is considered ambiguously, with both decelerating and accelerating trends. The comprehensive literature review presents the influence of factors on settlement intensity. The present study aims to estimate the long-term processes of sleeper settlement accumulation depending on the loading factors: impact, cyclic loading, and preloading. The typical for a void zone ballast loading pattern was determined for various void sizes and the position along the track by using a model of vehicle-track interaction that was validated by experimental measurements. The loading patterns were parametrized with four parameters: maxima of the cyclic loading, impact loading, sleeper acceleration, and minimal preloading. A specially prepared DEM simulation model was used to estimate the ballast settlement intensity after initial settlement stabilization for more than 100 loading patterns of the void zone cases. The settlement simulation results clearly show that even a low-impact loading pattern causes many times increased settlement intensity than ordinary cyclic loading. Moreover, the initial preloading in the neighbor-to-void zones can cause even a decrease in the settlement intensity compared to the full ordinary or partial unloading. A statistical analysis using a machine learning approach and an analytic one was used to create the model for the intensity prediction regarding the loading patterns. The analytic approach demonstrates somewhat lower prediction quality, but it allows to receive plausible and simple analytic equations of the settlement intensity. The results show that the maximal cyclic loading has a nonlinear influence on the settlement intensity that corresponds to the 3–4 power function, and the impact loading is expressed by the linear to parabolic function. The ballast’s minimal preloading contributes to the reduction of the settlement intensity, especially for high cyclic loadings that are typical for neighbor-to-void zones. The results of the present study could be used for the complementing of the present phenomenological equations with the new factors and further application in the algorithms of the settlements accumulation prediction.
... Track stiffness variations occur over time and space due to dynamic train loading and aging of track components, such as worn railpads, hanging sleepers, and ballast fouling. Track stiffness variations may further lead to geometric deterioration [1,2] and consequently, major maintenance costs. Therefore, it is essential to continuously monitor track stiffness variations and related track component degradations over time and space. ...
... Fig. 1(b) shows the structure of the wheel assembly. A wheel (1) with a diameter of 130 mm was mounted on the guiding block (3) through the axle box (2). The wheel assembly was mounted on the arm of the steel frame (4) and vertically loaded using two springs. ...
While various train-borne techniques have been developed for measuring railway track stiffness, differentiating stiffness at different track layers remains a challenge. This study proposes a digital twin framework for the vehicle–track interaction system, which enables track stiffness evaluations based on axle box accelerations (ABA). The digital twin consists of a physics-based model, a model library and data-driven models. Compared to existing techniques, the proposed method simultaneously evaluates the stiffness of the railpad, sleeper and ballast layers at a sleeper spacing resolution, while being robust to varying track conditions, such as track irregularities and vehicle speeds. This is accomplished by employing a localized frequency-domain ABA feature capable of distinguishing between the characteristics of different track layers. Furthermore, track stiffness is evaluated in near real-time. This is achieved using a model library derived from physics-based simulations of a range of track conditions. Two data-driven models that can quickly select or interpolate model instances contained in the library are developed. During operation, the data-driven models use the measured ABA features as input and then infer the stiffness for the different track layers. The proposed method is applied to evaluate the track stiffness of a downscale test rig in a case study. The track stiffness evaluated by the proposed method is compared with that obtained through hammer tests and with the observations of the track component conditions. These comparisons show that the proposed method can capture the stiffness variations due to periodically fastened clamps and substructure misalignments at different speeds. In addition, the proposed method is demonstrated to be superior to the commonly used hammer test method for evaluating track stiffness under loaded conditions.
... The results show that track settlement is sensitive to the variation of track stiffness, and constant track stiffness almost does not accelerate track geometry irregularity. The studies conducted in [8,40,62] also indicated that the track stiffness irregularity had significant influence on track geometry deterioration. According to these studies, the increase of track geometry irregularity is mainly caused by track differential settlement and the nonuniformity of track stiffness, and the railway sections with high track stiffness irregularities were difficult to be maintained. ...
The dynamic performance of a railway track subjected to moving trains depends strongly on track support conditions. In reality, even for the well-constructed and well-maintained tracks, sleeper support stiffness and global track stiffness vary substantially along the track, which affects the train-track dynamic interactions, causing rapid track geometry degradation as well as the riding comfort and safety issues. Consequently, track stiffness irregularity (TSI, the spatial variation of track stiffness along the track) is important for railway construction and maintenance in addition to track geometry irregularities. So far, extensive research has been published on the TSI whereas the relevant issues have not been paid sufficient attention. In this paper, a summary and comments have been made in the field of TSI about the current research status and future trends from a critical point of view. Novel concepts of the critical values of TSIs and the integrated management of the track geometry and stiffness irregularities are proposed. The review presented in this work is valuable to advance the research on TSI and can help guide the design, construction and maintenance of railway tracks.
... In recent years, data-driven predictive models [5][6][7] have been proposed to explore the degradation of track geometry as a function of the traffic load and track parameters, whereas experimental work was published to elucidate more explicitly the relationship between track stiffness and track geometry [3,8,9]. Another category of studies dealt with the relationship between track geometry, variation in track properties and train-induced vibration, both numerically and analytically, such as reported in [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Most of these contributions considered randomness of support stiffness and its influence on vehicle and/or track vibration [10][11][12][13][14][15][16]18,19,21,23,24]. ...
... Another category of studies dealt with the relationship between track geometry, variation in track properties and train-induced vibration, both numerically and analytically, such as reported in [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Most of these contributions considered randomness of support stiffness and its influence on vehicle and/or track vibration [10][11][12][13][14][15][16]18,19,21,23,24]. Local variation of support stiffness was considered in the form of transition zones in [20,27,28] and hanging sleepers in [22,26]. ...
... On the contrary, the dynamic axle load does not belong to this essence, but is a kind of by-product generated by different sources as a consequence of the moving character of the load. Most importantly, the primary sources are: wheel out of roundness of the rolling stock, and on the side of the infrastructure, the track geometry (deviation from the straight line) and the stiffness profile (variation in support stiffness along the rail) [9,16,18,19,[21][22][23][24][25]. The presence of a dynamic axle load has negative influences on the rail transport system as a whole: it increases the energy consumption of the locomotive, and the mechanical energy it generates in the wheel-rail contact interface leads to the emission of environmental vibration (by means of wave propagation or radiation of energy) [18,32,33] and the degradation of the track geometry (by means of dissipation of energy along the radiation path) [22,23]. ...
This work addresses the contribution of the wavelength composition of the spectrum of the rail support stiffness profile to the expected long-term settlement. To that aim, purely harmonic stiffness variations of different wavelength are studied. The frequency-domain model with a double periodicity level previously developed by the first and last authors is adopted to embed the stiffness profile in one of the periodicity layers. Additional resonance velocities at which the resonance frequency of the track system coincides with the support-passing frequency or its multiples are found. The susceptibility to degradation is assessed both by quantifying the mechanical energy dissipated in the substructure under a moving train axle within one wavelength of the support stiffness variation, and the work performed by the wheel-rail contact force. It is shown that shorter wavelengths and larger standard deviations of varying ballast/subgrade stiffness result in an increasing energy dissipation in the substructure, and increase the work performed by the wheel-rail contact force, therefore leading to a reduced lifetime of the track. The energetic quantities increase for lower mean values of the stiffness profile, confirming the proneness of tracks on soft soils to degradation. The influence of varying stiffness vanishes for wavelengths of approximately 16 times the sleeper span, which is equivalent to a track length of about 10 m. High railpad stiffness values result in increased energy dissipation but the influence is limited. In general, an increasing train velocity amplifies the rate of track degradation, with no stabilizing trend in the high-speed regime (300 km/h).
... Common techniques to predict fault development and degradation include derivations of ANN [50], [146], [147] and combinations with other ML techniques, such as Convolutional NN with Long Short-Term Memory [148], and Regressive NN with nearest neighbour search [154]. Other techniques are based on Big Data [52], [155], GAs [76], SVR, Bayesian optimization [156], [150] and ML [88]. Data are typically acquired from sensors on inspection vehicles or from maintenance routines -these sensors are often inertial or visual (laser or Light Detection and Ranging, LiDAR). ...
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.
... Such models are useful for predicting magnitudes of forces acting on the track due to the unevenness of the rail or abrupt stiffness changes along the track, which is the case with transition zones to engineering objects [4][5][6][7][8][9]. Another approach is based on analytical or empirical settlement models, which serve for predicting settlements of the track due to unevenness of the rail or spatial variations in track-bed stiffness (ballast + subgrade) [10][11][12][13]; this is especially covered in work in which the author summarised a dozen existing empirical formulas for track settlement prediction [14]. This kind of approach, although very useful, does not answer questions about the behaviour of the subgrade in deep layers. ...
As a consequence of increased axle loads and speeds of trains on modernised railway lines, there may occur problems with bearing capacity and stability of the subgrade in some sections of the railway network. This is the situation we are dealing with right now on the Polish State Railways network. Therefore, as a case study, a fragment of an existing railway embankment based on a weak foundation was chosen for the analysis of train–track–subgrade interaction. A two-stage train–track–subgrade model has been developed. The model consists of the upper part (train–track) and the lower part (subgrade-foundation). The first part is modelled as a self-contained system of differential equations which are solved by means of finite difference method and yield the stress levels on the subgrade. These stresses are treated as a load for the lower system modelled using FEM. The model has been validated using experimental data from literature, authors’ measurements, and railway staff measurements of the track geometry. Several cases of strengthening methods were calculated and compared with measurements on the railway section under consideration. Good agreement between the prediction and the measurement was found. The novelty of the model is including the heterogeneity of the subgrade, the strengthening methods, and very deep layers of its foundation as well as adding the influence of vibration on the weakening of soils. It was found that this influence is noticeable and should be included in the prediction of railway subgrade behaviour
... The rails should be unfastened, which is different from the actual conditions. [16,62,63] Train Loading vehicle (TLV) ...
... (2) Accurate measurements are required [92]. As a result, the measured track stiffness will be influenced by the measuring method [63,93]. Table 2 presents a summary of continuous measurement methods. ...
The track structural performance is ensured by understanding the track vertical response to the applied loads and bearing and transmitting these loads with minor permanent deformation. There is a global trend toward higher axle loads and train speeds, particularly on heavy haul lines. Therefore, it has become critical to examine the track's structural state promptly and correctly. As one of the most influential factors for recognizing track vertical behavior, vertical stiffness has been a concern in ballasted railway track design, analysis, defect detection, and maintenance procedures. So far, extensive research has been done on the concepts, measuring methods, quantifying, and influential parameters on track vertical stiffness. This paper comprehensively reviews the vertical stiffness measurement methods, values, and effective ballasted track parameters and their contribution to railway track condition monitoring.
... Understanding how support conditions and its variability affects the track behaviour is crucial for increasing the track life. Grossoni et al. 22,23 studied the influence of support conditions on track behaviour using a vehicle/track interaction (VTI) model. A sophisticated understanding of track stiffness is necessary and a comprehensive review on available theoretical foundation models for railway tracks has been carried out by Younesian et al. 24 Understanding how track nonlinearity influences load distribution is also a benefit. ...
The dynamic behaviour of a complete crossing panel under a transient moving load is considered using finite element method (FEM). A 3D model is developed in ANSYS software and validated using recent field measurements data from the UK. Four assumptions of trackbed stiffness distribution are tested. The best matching assumption to the field measurements is suggested and taken forward in the current study. Vibration modes are classified by comparing the modal and transient analysis results in frequency domain based on physical interpretation of the modes. A comparison is made between the so-called ‘P2’ dynamic force value in the current study and that calculated by Jenkin’s formula as specified in the UK Network Rail standard NR/L2/TRK/012 relating to crossing fatigue calculation. It is shown that this standard over-estimates the P2 dynamic force. The crossing stress results are presented and the most critical point on the foot, where the stress is considered to be in the elastic domain is introduced, in relation to the various support conditions. It is concluded that approximating the dynamics load with an equivalent P2 force envelope is sufficient to capture peak stresses for foot fatigue analyses. Finally, two scenarios for modelling hanging bearer effects are analysed in terms of foot stresses. Considering 5 mm initial gap at three consecutive bearers leads to a significant 40% increase in stress value.
... A large number of VLL predictive approaches have been derived empirically (directly or indirectly) from laboratory (triaxle, reduced scaled box or full-scale box tests) and¯eld experiments, by various researchers worldwide, mostly focusing on ballast settlement. Reference 17 carried out an excellent critical review, which was mentioned by Ref. 3, and updated and well-illustrated recently by Ref. 18. Figure 3 summarizes the comparison of these approaches graphically. It can be noted that those VLL investigations are presented within three di®erent ranges of initial ballast compaction: softer, medium and sti®er, respectively, \1-5 mm", \5-10 mm", \> 10 mm", for 900 thousand cycles. ...
With an emphasis on the integrated deterioration of railway track geometry and components, a new hybrid numerical-analytical method is proposed for the predictive analysis of track geometrical vertical levelling loss (VLL). In contrast to previous studies showing a dependency on the number of cycles, this research unprecedentedly incorporates the influence of operational, vehicle and track conditions. The numerical models are carried out using an explicit finite element (FE) package under cyclic loadings, and then, their outcomes are iteratively regressed by an analytical logarithmic function that accumulates permanent deformations in order to quantify VLL over a long term. The results are first compared with other previous studies, indicating a very good agreement with them. Then, field measurements have been used to further verify the results. In this study, parametric simulations are performed varying three key parameters: axle load, train velocity and ballast tangent stiffness. The parametric studies exhibit that the rate of VLL raises about 50% if the axle load increases only from 30 ton to 40 ton for a freight train running at 70 km/h on a stiffer ballast track. In contrast, for a 25-tonnes-axle-load train running from 60 km/h to 100 km/h on a similar track, the vertical levelling degradation reduces by approximately 20%. The main findings suggest that higher axle loads contribute significantly to the VLL due higher contact forces and, on the other hand, a lower train speed does not necessarily imply a low rate of VLL since the influence of train velocities on track geometry (VLL) is associated with the natural frequencies (or wavelengths) of the ballasted railway track. The insight demonstrates that the load frequencies play a key role on the deterioration of VLL.
