Track Quality Index Research Articles

Enhanced vertical railway track quality index with dynamic responses from moving trains

The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research shows that dynamic responses from moving trains via axle box acceleration (ABA) measurements can quantify abrupt changes in the vertical dynamic responses. Thus, this paper proposes a framework to design an enhanced vertical TQI, called EnVTQI, by integrating track longitudinal levels and dynamic responses from ABA measurements. First, measured ABA signals are processed to mitigate the influence of variation in measurement speed. Then, substructure and ballast-related features are extracted, including scale average wavelet power (SAWP) in the ranges 0.04 m-1 to 0.33 m-1 (substructure) and 1.25 m-1 to 2.50 m-1 (ballast). This enables identifying track conditions at different track layers. Finally, EnVTQI is determined by weight averaging between the conventional vertical TQI and the ABA features from moving trains. The performance of EnVTQI is evaluated based on 48 segments of a 200-m track on a Dutch railway line. The results indicate that EnVTQI helps to distinguish track segments that cause poor train-track interaction, which the conventional TQI does not indicate. EnVTQI can supplement the conventional TQI, improving the effectiveness of track maintenance decision-making.

Use of Zero-crossings Segmentation for Track Quality Assessment

This study concerns track quality assessment of standard-gauge railways in the context of the Hungarian railway system. Data gathered by multipurpose track recording vehicles matched the EN 13,848 requirements. Track Quality Index (TQI) measurement systems (The Federal Railroad Administration (FRA), the Netherlands’, and the Chinese TQI) are considered where three types of predetermined segment techniques: separate, moving, and Zero-crossings segmentation are employed. The importance of track segmentation in quality assessment, which affects maintenance planning, is shown by key findings. For heterogeneous data, the TQIs might be deceptive, highlighting the need for alternatives. The robustness of the Zero-crossings method makes it possible to analyze deterioration factors in great detail and in some efficient way. Longer analytical segments and smoothing of the data improved precision. Based on empirical data, we advise considering a Zero-crossings strategy for precise and efficient track-quality evaluations. With the help of this study, track quality can be better evaluated for train systems.

Multiple-axle box acceleration measurements at railway transition zones

This paper presents a methodology for monitoring transition zones using responses of multiple-axle box acceleration (multi-ABA) measurements. The time–frequency characteristics of the vertical ABA signals from four wheelsets are analyzed. The major contributions are as follows. (1) We propose four key performance indicators (KPIs) to quantify local multi-ABA energy differences at different abutments, tracks, entrance and exit sides, and inner and outer rails. (2) The same dominant spatial frequencies are obtained with different measurement speeds, so the proposed method is suitable for multi-ABA systems mounted on operational trains. Transition zones at nine double-track railway bridges are selected as the case study. The KPIs indicate that (1) the energy differences between abutments are above 80% in three bridges; (2) two abutments show that the energy differences between tracks are higher than 100%; (3) three tracks have energy differences above 100% between the entrance and exit sides; and (4) the energy differences between rails are above 80% on three sides. Finally, using measurement with 7 years of difference, the KPIs and track quality index are discussed. These findings suggest that multi-ABA measurement can be used as a health condition monitoring method for railway transition zones to support condition-based maintenance.

Analysis of the Influence of Pile-Raft Foundation Reinforcement on an Adjacent Existing Line Foundation

Pile-raft foundation reinforcement will have a certain impact on the adjacent existing line foundation, which will affect the normal service of the subgrade, and even lead to the instability of the subgrade. Until now, there have been few studies on adjacent construction problems, and there are few field experimental data available for us to consult. Therefore, this study relies on the construction project of Shanghai–Nanjing intercity high-speed railway close to the existing line, using in situ monitoring methods, such as stress shovels, horizontal strain gauges, and inclinometers combined with finite-element calculation and rail-inspection vehicle-data analysis. The stress, displacement, and geometric linearity of the adjacent existing line foundation during the reinforcement construction of a pile-raft foundation were studied. Our aim was to measure the impact and optimize the existing roadbed-protection measures employed during the construction period. We found that the cumulative horizontal displacement of the existing line foundation slope toe during the construction period was 24.25 mm, and the lateral displacement rate was less than 0.59 mm/d. The distance between the two lines was 9 m. The horizontal stress of the foundation soil in the depth range varied according to extrusion and retraction with the different construction stages, and the extrusion stress was less than 10 kPa. The horizontal stress changes in different construction stages were not obvious; the track quality index (TQI) in the existing track inspection during the construction period increased by 129.58%, and the existing track geometric linearity fluctuated greatly. According to the test results, the excavation stage of the subgrade foundation pit was the most dangerous stage of the existing subgrade during the construction of the new line pile-raft foundation. Although the change of the horizontal stress in different construction stages was not obvious, the horizontal displacement of the slope toe was sensitive to the construction process. Therefore, it could be used as a key indicator to monitor the deformation and stability of the existing subgrade. The correction coefficient was obtained by combining the detection data of the track-inspection vehicle with the finite element calculation data, based on which the accurate estimation of the horizontal displacement of the slope toe after the excavation of the foundation pit was realized. The monitoring and evaluation method of the stability of the existing line foundation under the influence of the pile-raft foundation reinforcement construction was preliminarily established through field monitoring and the analysis of the track-inspection data. Based on this method, relevant early warning values were proposed. The test results and engineering measures ensured the safe operation of the existing line foundation. This has important theoretical significance for guiding the construction of a new subgrade of adjacent existing lines.

Railway infrastructure maintenance efficiency improvement using deep reinforcement learning integrated with digital twin based on track geometry and component defects

Railway maintenance is a complex and complicated task in the railway industry due to the number of its components and relationships. Ineffective railway maintenance results in excess cost, defective railway structure and components, longer possession time, poorer safety, and lower passenger comfort. Of the three main maintenance approaches, predictive maintenance is the trendy one, and is proven that it provides the highest efficiency. However, the implementation of predictive maintenance for the railway industry cannot be done without an efficient tool. Normally, railway maintenance is corrective when some things fail or preventive when maintenance is routine. A novel approach using an integration between deep reinforcement learning and digital twin is proposed in this study to improve the efficiency of railway maintenance which other techniques such as supervised and unsupervised learning cannot provide. In the study, Advantage Actor Critic (A2C) is used to develop a reinforcement learning model and agent to fulfill the need of the study. Real-world field data over four years and 30 km. is obtained and applied for developing the reinforcement learning model. Track geometry parameters, railway component defects, and maintenance activities are used as parameters to develop the reinforcement learning model. Rewards (or penalties) are calculated based on maintenance costs and occurring defects. The new breakthrough exhibits that using reinforcement learning integrated with digital twin can reduce maintenance activities by 21% and reduce the occurring defects by 68%. Novelties of the study are the use of A2C which is faster and provides better results than other traditional techniques such as Deep Q-learning (DQN), each track geometry parameter is considered without combining into a track quality index, filed data are used to develop the reinforcement learning model, and seven independent actions are included in the reinforcement learning model. This study is the world’s first to contribute a new guideline for applying reinforcement learning and digital twins to improve the efficiency of railway maintenance, reduce the number of defects, reduce the maintenance cost, reduce the possession time for railway maintenance, improve the overall safety of the railway operation, and improve the passenger comfort which can be seen from its results.

Penentuan Prioritas Perawatan Berdasarkan Hasil Track Quality Index (TQI) Jalur Kereta Api Antara Stasiun Semarang Poncol – Stasiun Alastua

PT. Kereta Api Indonesia (Persero) has a measuring train that is used to determine the condition of the railroad tracks. The output of measuring train is a railroad quality value called the TQI (Track Quality Index). The TQI value can used to determine priority rail road maintenance. This study aims to determine the TQI category of the railroad between Semarang Poncol - Alastua, then determine improvement priorities and maintenance programs. The results of the analysis of the Semarang Poncol-Alastua cross-section railway data have an average category II for 20 < TQI ≤ 35. For priority treatment based on the results of the first point TQI on the SMT-ATA upstream track KM 0+156/188 Type Device LK (arch) with a TQI value of 48.1 categories III with an alert condition, an immediate corrective action program and track laying. Downstream track ATA-SMT KM 1+993/975 with a TQI value of 48.1 categories III with an alert condition, an immediate corrective action program and for the type of repair is track laying.

Prediction model for the maintenance of rail infrastructure in Java

Maintenance is the most prolonged phase after constructing a railway track is completed and operated. The initial indication of the need for railway track maintenance can be seen from the track quality index (TQI) value. Maintenance of railway tracks can be based on the TQI data category, which is a track superelevation, leveling, lining, and gauge width with TQI categories ranging from very good, good, fair, and poor. In the existing condition, only one-track measurement train, the EM-120, is owned by PT Kereta Api Indonesia (Persero) operates on the island of Java, so there are still railway tracks that still need to be measured by track measurement trains and require a TQI value. Implementing the TQI categorization is necessary for maintenance; hence a comprehensive study is essential to monitor and track the advancement of the research. This paper will map the literature on railway track maintenance, TQI, and prediction models. The literature database was taken from Google Scholar and analyzed using the VOS viewer tool with a mapping of previous research. The results of this research are highly useful in understanding the current development of railway track maintenance research; however, a study has yet to be identified that predicts the TQI category for railway tracks that have not been surveyed by track measurement trains.

Evaluasi Kinerja Jalur Ganda Kereta Api Solobalapan - Yogyakarta

Solobalapan-Yogyakarta railway track serve up to 100 trains each day that get near to maximum lane capacity on one section of that line. Evaluation consisted of reliability, availability, maintainability and safety criteria. Ministry of Transportation issued Rencana Induk Perkeretaapian Nasional (RIPNas) 2030, so existing track’s conditions need to be estimated in 2030. On the evaluation, reliability rated by accuracy of travel duration, arrival and departure time. On availability rated by lane capacity using Scott method, and conformity of its lane carrying capacity. On maintainability and safety rated by Track Quality Index (TQI). Then, predicted number of trains pass and planned scenario analysis for 2030. Based on reliability criteria, 97% included on tolerance travel duration, but delays on arrivals and departures more than 40%. Based on availability criteria, lane capacity sufficient up to 515 trains per day, and lane carrying capacity reaches 18,171,648 tons per year. Maintenance and safety criteria based on TQI on the upstream lane of 75% is on very good condition and 61% on downstream lane is in good condition. Based on RIPNas in 2030 there will be 289 trains pass everyday, so the existing railway track is not capable to serve properly and the scenarios are increase the maximum speed to 120 km/hour and add eight block.

A smoothness optimization method for horizontal alignment considering ballasted track maintenance

AbstractThis paper proposes a smoothness optimization method of horizontal alignment based on orthogonal least squares and the theory of controlling the track smoothness. With this method, multiple curve types can be fitted simultaneously according to the maintenance requirements, and the residual deviation in the track is optimized according to the smoothness standard. The target alignment of the tamping operation is constructed, providing support for the maintenance of the ballasted track. The implementation results show that the proposed method fully adapts to operating railway maintenance conditions, and the optimized design parameters resemble the field observation results. The lateral profile track quality index is reduced from 0.78 to 0.39, the amplitudes of the 10 and 60 m mid‐chord offset decrease by 73% and 52%, respectively, and the 30‐m vector distance difference decreased by 53%, indicating effective improvement.

Experimental investigation on the use of multiple very low-cost inertial-based devices for comfort assessment and rail track monitoring

The periodic rail track inspection is mandatory to ensure ride comfort and operational safety. However, conventional monitoring technologies have high costs, stimulating research on low-cost alternatives. In this regard, this paper presents the first experimental results on the use of multiple very low-cost sensors aboard trains for vibration monitoring, proposing a collective approach to provide more accurate and robust results. Nine devices comprising commercial-grade inertial sensors were tested in different distributions aboard a high-speed track recording train. Frequency weighted accelerations were calculated in accordance with ISO 2631 standard as comfort and indirect track quality index. As expected, vertical and lateral results were correlated with, respectively, track longitudinal level (range D1, maximum correlation coefficient of 0.86) and alignment (range D2, maximum correlation coefficient of 0.60), with numerically similar results when considering the fused signal. The collective approach's potential was proven as a result of the noise reduction and the discrepant sensor identification.

Risk Identification Method for High-Speed Railway Track Based on Track Quality Index and Time-Optimal Degree

Risk Identification Method for High-Speed Railway Track Based on Track Quality Index and Time-Optimal Degree

Long-Term Monitoring of Local Track Irregularity and Its Influence for Simply Supported Girder Bridge of HSR

By analyzing dynamic track geometric irregularity data of high-speed railway (HSR) with ballast track in China, there is a peak of [5, 11] m wavelength for the longitudinal level at the beam joint of simply supported girder bridges. The difference between the absolute value of track longitudinal level at the beam joint and that at the midspan is one to three mm. Under annual temperature cycle, the ballast at the beam joint loosens because of the bridge–track interaction, which results in the longitudinal level at the beam joint gets larger in winter and smaller in summer, that means the longitudinal level is negatively correlated with temperature, and at the same time, the development of the track longitudinal level and Track Quality Index (TQI) with the temperature are given. In addition, the vertical acceleration of axle box and bogie, and wheel–rail vertical force are larger when the train passes the beam joint than when it passes the midspan, which threatens running safety significantly. This article established a three-dimensional vehicle–track–bridge finite element model (FEM), thus the thresholds of track longitudinal level at the beam joint for simply supported girder bridge with ballast track in 250 km/h HSR were proposed, i.e., the maximum of dynamic track longitudinal level is 14 mm, and the maximum of static value is 11 mm. Once one of the two thresholds exceeds the corresponding limit, the train speed should be reduced to 160 km/h or below.

Using large datasets for finding the correlation between the rate of track settlement and changes in geometry indices

The allowable speed limit of railroad tracks is dependent upon maintaining track conditions that will ensure the safety and ride quality. Settlement of railway tracks amplifies the dynamic loads of trains, which results in high maintenance costs and poor ride quality. Direct settlement measurement in the field is costly due to the requirement for installing many gauges along the many kilometers of track. Different datasets from the US passenger tracks consisting of more than 300 million elements were employed to propose a novel method for measuring the rate of settlement from the reported space curve. In this method, Cross Power Spectral Density (CPSD) was employed for determining the quality of data and choosing reliable observations for calculating the settlement. After calculating the rate of settlement for two railway tracks with a total length of 287 miles, its correlation with changes in different geometry indices was investigated. Equations were proposed for predicting the rate of settlement by changes in geometry indices. Results indicate that standard deviation index has the best correlation with the rate of settlement. Track quality index (TQI) is sensitive to high-frequency noise and does not yield highly accurate results when there is noise in the dataset.

Analysis Effect Of Thick Ballast On Track Quality Index (TQI) Value Route Wonokromo – Mojokerto

In Operational Area 8 Surabaya there are several railway lines, one of which is the Wonokromo - Mojokerto line. This lane has a fairly heavy train traffic frequency including the connecting lane south. This will affect the quality of roads on the Wonokromo - Mojokerto line. To assess damage to a railroad seen from several aspects in the structure of the railroad. These aspects are the structure of the railroad, the structure of the railroad and the geometrical structure of the railroad. In Indonesian railways, the railroad uses railroad geometry consisting of several parameters (track gauge, cant, longitudinal level, and lateral level.) as a basis for assessing railroad quality. In this study will analyze the structure of the railroad under the thickness of the ballast to the Track Quality Index (TQI). In this study using a simple linear regression analysis to determine the variation of the thickness of the ballast to TQI. From the results of the analysis conducted, obtained thickness changes that occur in ballast that affect the value of the Track Quality Index (TQI). Where the greater ballast, the greater the value of TQI.

Study of Concrete Bearing Behavior for 1067 mm Track Width with Variation in Track Quality Index (TQI) Values

Penelitian ini di lakukan untuk mengetahui pengaruh ketidakstabilan geometri terhadap ketahanan struktur jalan rel yang difokuskan pada struktur atas jalan rel. Dalam mengkaji nilai penyimpangan geometri lintasan menggunakan perhitungan standar deviasi pada setiap parameter pengukuran, yang selanjutnya diolah menjadi nilai track kualitas indeks (TQI). Data parameter pengukuran di dapatkan dari kereta ukur Galunggung untuk tahun 2018 dan kereta ukur EM-120 untuk tahun 2019. Perbedaan hasil perhitungan nilai kualitas jalan rel (TQI) dengan tahun yang berbeda, dijadikan patokan untuk penentuan lokasi survey manual dengan menggunakan alat ukur matisa dan meter listring. Hasil pengukuran geometri dilapangan dimodelkan dengan program bantu perhitungan kekuatan struktural. Simulasi model dengan melakukan analisis menggunakan Finite Elemen Method (FEM) terhadap beberapa kondisi sesuai dengan kategori penyimpangan geometri (TQI). Hasil penelitian ini didapatkan bahwa, propertis material bantalan, dengan kaut tekan beton (Fc’) 54 MPa dan kuat tarik wire prestressed (Fy) 1400 MPa, tipe rel adalah R.54, serta properti balas sesuai PM.60 tahun 2012. Beban aksial yang bekerja sebesar 16.241 Kg, Dari hasil analisa pada nilai penyimpangan geometri kategori 3 (25<TQI<40), didapatkan spesimen bantalan (B4) mengalami tegangan tekan 15.566 MPa dan tegangan tarik sebesar 5.178 MPa. Analisa kondisi tersebut, material beton tidak dapat menumpu gaya tarik yang dipersyaratkan yaitu, 4.632 MPa (0.32 Fc0.67). Demikian terjadi juga, pada kondisi penyimpangan geometri kategori 4 (TQI > 40).

Analisis Kondisi Jalan Rel Kereta Api Pada Lintas Sragen-Solo Berdasarkan Nilai Track Quality Indeks (TQI)

One way to keep the train service flow runs safely, maintenance of railroad geometry is required. Maintenance of railroad tracks uses the Track Quality Index assessment to determine which railroad compartments or corridors are prioritized for repair. The research method used is a quantitative research method. The calculation of the TQI of railroad tracks includes force, height, spoor width and lining. The results of the calculation of the TQI value (manual) give the cumulative total number of standard deviation index is 21.6. While the TQI calculation (mechanical) total cumulative number index standard deviation is 21.2. Based on the results of the TQI calculation, the condition of the railroad tracks can be classified into the criteria for the TQI level II (standard deviation of 20 - 35), namely that the railroad can be passed by trains at speeds of 80-100 km/hour. The railroad maintenance based on the TQI is the repairs to rail compartments or corridors that experience leveling damage in segment 9, lining damage in segment 7, wide damage spoor (gauge) in segments 5,7 and 10 and elevation damage (cant) in segment 3.

Correlation analysis method of track irregularity indexes

Research on the correlation analysis method of track irregularity indexes has an important guiding role in selecting targeted maintenance decision-making indexes and guiding maintenance decision making related to track irregularity state. In this study, the track irregularity index structures and track quality index were used to design the correlation analysis method of track irregularity indexes. To verify the effectiveness of this method, a total of 48 track inspection car historical data from September 2014 to February 2017 and November 2017 to December 2018 of the Shenmu-Shuozhou railway Hexi transportation division was used as the case study. The results show that the correlation analysis method proposed in this study can be used to effectively analyze the correlation between the track irregularity indexes. This is verified by comparing with the correlation analysis method based on the standard deviations of track irregularity indexes.

Hybrid rail track quality analysis using nonlinear dimension reduction technique with machine learning

Track geometry parameters from rail track inspection are regulated within unique safety limits for different track classes. This study focuses on developing an index that combines safety and track quality because of the inefficiency of corrective maintenance activities between routine maintenance cycles when federal geometry limits are violated. This combination is achievable by summarizing multivariate track geometry parameters as an improvement to previous linear approaches to address the problem of inefficient track geometry maintenance programs. The use of nonlinear dimension reduction (T-stochastic neighbor embedding (T-SNE)) for hybrid track quality index development and the influence of time-based parameters on track quality is evaluated in this study. The results show that the probability of geometry defects is correlated with principal components, but T-SNE had the best prediction on train-test splits despite its poor performance on a blind validation set. The absence of an observable correlation between the track geometry and acceleration data requires further investigation.

Prediction of Track Quality Index (TQI) Using Vehicle Acceleration Data based on Machine Learning

Prediction of Track Quality Index (TQI) Using Vehicle Acceleration Data based on Machine Learning

Analyzing Major Track Quality Indices and Introducing a Universally Applicable TQI

Adequate railway track condition is a prerequisite for safe and reliable railway operation. Many track quality indices (TQIs) have been developed with the aim of assessing the track condition holistically. These indices combine measurement signals of some or all relevant geometry parameters with different mathematical models. In this paper, a selection of important TQIs is evaluated. Using measurement data of a five kilometer track section, the indices are calculated and their properties are discussed. This study reveals that all indices exhibit drawbacks to varying degrees. As a consequence, a new index has been developed—the track quality index of Graz University of Technology (TUG_TQI). Its favorable characteristics are presented by means of the above-mentioned test section. The TUG_TQI combines all relevant track geometry parameters, which are normalized beforehand to eliminate over or underrepresentation of different parameters. Thus, the index reliably describes the overall geometrical track quality.