Quantifying the Impact of Subgrade Stiffness on Track Quality and the Development of Geometry Defects

Roghani, Alireza; Hendry, Michael T.

doi:10.1061/jtepbs.0000043

Cited by 20 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As evidenced in Le Pen et al (2014), hanging sleepers reoccur soon after the tamping applied at a level crossing approach. Results from investigating the dependency between track geometric quality and longitudinal variations of vertical stiffness in Nielsen et al (2020); Roghani and Hendry (2017) also confirmed the observation: uneven profiles with high degradation rates often occur on track sections with a combination of a high gradient and low magnitude in substructure stiffness. Further, Yurlov, Zarembski, Attoh-Okine, Palese, and Thompson (2019) linked the occurrence of track geometry defects with subgrade parameters measured by ground-penetrating radar (GPR).…”

Section: Maintenance Intervention Planningsupporting

confidence: 60%

Systems thinking approach for improving maintenance management of discrete rail assets: a review and future perspectives

Shang

Macho

Wang

et al. 2021

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

Performance evaluation and maintenance planning are gaining importance with ageing rail infrastructure and increasing demand on track safety and continuous availability. The discrete/point railway assets (e.g. bridges, level crossings) together with extended track sections constitute the main railway network infrastructure. The former has important implications in train safety, riding comfort and operating expenditures due to local intensified degradation and plays a role in effective network capacity due to their large quantity. The heterogeneity in asset features and operating environment also adds difficulties to efficient maintenance planning of multiple discrete assets. The current review screens the issue to level crossings, as little concern has been engaged to this asset type, and draws together different perspectives related to their maintenance management. The systems thinking approach is integrated and two levels of asset management (i.e. micro-and macro-level) are used to structure the synthesis, which are interdependent and synergistic. Two major approaches, namely, the mechanistic and data-driven modelling are synthesised. Both contribute to the maintenance knowledge and their comparisons are elaborated. Limitations in existing studies are identified and directions for future research are provided, aiming to contribute to a more refined 'inspection and diagnosis' process to properly capture the local track issues and move towards system-level maintenance approach for multiple level crossings.

show abstract

Section: Maintenance Intervention Planningsupporting

confidence: 60%

Systems thinking approach for improving maintenance management of discrete rail assets: a review and future perspectives

Shang

Macho

Wang

et al. 2021

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

show abstract

“…The surface (profile) measurement is the main parameter affected by track substructure conditions such as ballast fouling, drainage issues, soft subgrade, and frost heave and track softening. [5][6][7] In this paper, running roughness 19 was used to quantify the surface geometry measurement collected after 2018's thawing season. Figure 1(c) shows the variation of surface roughness over 10 km of track with higher values representing rougher track sections.…”

Section: Track Geometry Carmentioning

confidence: 99%

“…In both cases, the train speed contributes to the vibration magnitude and the level of comfort felt by the passengers. In addition, the track substructure condition including ballast and subballast thickness, ballast fouling index, drainage condition, and subgrade stiffness contribute to the development of geometry defects and surface irregularities [5][6][7] which further affect the ride quality. These parameters are known by railway personnel to impact the ride quality, but the extent of their impact is not quantified.…”

Section: Introductionmentioning

confidence: 99%

Procedure for combining ﬁeld measurements and machine learning to quantify impact of different track parameters on ride quality of railway tracks

Roghani

Pall

Toma

2021

Proceedings of the Institution of Mechanical Engineers, Part F:

Self Cite

View full text Add to dashboard Cite

Ride quality in terms of vibration is a fundamental factor affecting passengers’ satisfaction. Every year, passenger carriers invest significantly in various aspects of their system, including track and infrastructure, to improve ride quality. The assessment of ride quality and understanding the extent of the impact of different parameters on its magnitude is essential for railway operators to make informed decisions regarding capital expenditures. This paper presents a methodology for using machine learning techniques to find the correlation between various parameters (including train speed, weather conditions, presence of track features, and composition of the track substructure) and ride quality (quantified using measurements from accelerometers mounted on a rail car). The statistical model was developed using field measurements collected over a 50 km section of VIA Rail’s track in Canada. This paper describes the collected field data, the development of the statistical model, and discusses the importance of each parameter on the accuracy of the model.

show abstract

“…Tests on several 200 m intervals have shown a linear deterioration in track geometry between two maintenance periods [ 79 , 80 , 81 ]. Linear prediction models for track geometry degradation have been widely proposed to predict the development of the track quality index (TQI) [ 82 , 83 , 84 , 85 ].…”

Section: Introductionmentioning

confidence: 99%

State-of-the-Art Review of Ground Penetrating Radar (GPR) Applications for Railway Ballast Inspection

Wang

Liu

Jing

et al. 2022

Sensors

View full text Add to dashboard Cite

In the past 20 years, many studies have been performed on ballast layer inspection and condition evaluation with ground penetrating radar (GPR). GPR is a non-destructive means that can reflect the ballast layer condition (fouling, moisture) by analysing the received signal variation. Even though GPR detection/inspection for ballast layers has become mature, some challenges still need to be stressed and solved, e.g., GPR indicator (for reflecting fouling level) development, quantitative evaluation for ballast fouling levels under diverse field conditions, rapid GPR inspection, and combining analysis of GPR results with other data (e.g., track stiffness, rail acceleration, etc.). Therefore, this paper summarised earlier studies on GPR application for ballast layer condition evaluation. How the GPR was used in the earlier studies was classified and discussed. In addition, how to correlate GPR results with ballast fouling level was also examined. Based on the summary, future developments can be seen, which is helpful for supplementing standards of ballast layer evaluation and maintenance.

show abstract

Quantifying the Impact of Subgrade Stiffness on Track Quality and the Development of Geometry Defects

Cited by 20 publications

References 11 publications

Systems thinking approach for improving maintenance management of discrete rail assets: a review and future perspectives

Systems thinking approach for improving maintenance management of discrete rail assets: a review and future perspectives

Procedure for combining ﬁeld measurements and machine learning to quantify impact of different track parameters on ride quality of railway tracks

State-of-the-Art Review of Ground Penetrating Radar (GPR) Applications for Railway Ballast Inspection

Contact Info

Product

Resources

About