Integrating trackbed inspection data

Integrating trackbed inspection data

Integrating trackbed inspection data for improved condition-based maintenance planning and hazard identification

Figure 1: Data Integration for maintenance recommendations

The routine measurement and integration of track condition data provides track engineers with tools to conduct more effective and predictive maintenance. Traditionally, regular measurement of track geometry (TG), a functional condition of the track, has provided key insight into the deterioration of track performance.

The addition of ground penetrating radar (GPR) for the continuous measurement of trackbed parameters such as ballast fouling and layer thicknesses provides a quantitative structural measure of the trackbed condition that allows track engineers to detect the early signs of failure of ballast and substructure components.

Maintenance recommendations can be derived from customised decision criteria utilising GPR survey metrics (Figure 1) combined with track geometry. This results in more effective utilisation of high output ballast maintenance machinery, leading to a direct savings in maintenance costs and time on track and improved asset reliability.

The ability to also address the root cause of track failures through improved knowledge of trackbed condition reduces the number of repeat track geometry faults, resulting in improved track availability and thereby traffic velocities.

Figure 2: Example illustrating track geometry faults developing from irregular subsurface layering.

RASC® Survey

The RASC® concept involves integrated data capture of a suite of complementary track inspection technologies (including GPR), which together allow a comprehensive assessment of both the above and below ground condition of the trackbed. Zetica Rail’s RASC® systems are used to undertake over 30,000 kilometres of trackbed surveys worldwide each year, utilising inspection train and hi-rail vehicle platforms.

GPR is a well-established non-invasive inspection method utilised by railways around the world to determine the condition of ballasted trackbed, both in terms of ballast condition and the trackbed profile. Surveys typically utilise both high and low frequency ultra-wideband antennas to obtain sufficient resolution and depth of investigation for the analysis of both ballast condition and for mapping formation and subgrade layer depths.

The primary aim of GPR surveys is to provide metrics for use in planning condition-based trackbed maintenance (ballast cleaning/undercutting, shoulder cleaning, surfacing/tamping) and to provide information on the anomalous condition of sub-ballast and formation layers for helping to determine the root cause of more localised trackbed problems.

The metrics are designed to provide a standardised means of quantifying the information contained within the often complex GPR datasets.

Typical GPR trackbed condition metrics include:

  • Ballast Fouling Index
  • Fouling Depth Layer Index
  • Layer (Interface) Roughness Index
  • Moisture Likelihood Index
  • Ballast Pocket Index.

Data integration examples

Making better use of track inspection data is key to the continuing efforts to drive down maintenance costs. The integration of GPR and other track measurements, such as track geometry (TG), ballast surface profiling, and surface water runoff gradients, has the potential to significantly improve the effectiveness of condition based trackbed maintenance and of identifying potential hazard locations.

Identify areas most prone to a deterioration in trackbed quality

GPR can help assess the nature and severity of underlying trackbed defects at the early stages of a developing geometry fault.

Repeat surveys enable the progression of the defect to be monitored in detail (often revealing the effects of seasonal influences on trackbed stability), information which can be fed into predictive track deterioration models. Areas were the GPR-derived metrics are stable over time may be considered less likely to undergo rapid or catastrophic failure.

In the example illustrated in Figure 2 short wavelength trackbed defects (due to settlement and ballast pumping) are associated with deteriorating track geometry condition. None of the TG anomalies represented an actionable exception at the time of the survey.

Figure 3: Example of merged point cloud with GPR-derived MLI overlay, indicating likely moisture and corresponding intensity of the points showing seeping of water from the track

GPR integrated with 3D terrain models

High density point cloud data captured by Zetica’s ZRL200 mobile terrestrial laser scanner (MTLS) integrated with GPR metrics provide tools for assessing track drainage. Merging the terrain and GPR derived metrics such as moisture likelihood index (MLI) and fouling depth layer (FDL) track substructure features can be assessed in a single platform (Figure 3). The data can further be used for the assessment of drainage potential, side drain levels and determining whether enough fall exist between formation surface and cess drains.

Figure 4: Example of correlation of poor sleeper (tie) condition with range of trackbed metrics including ballast condition (average fouling and fouling depth), trackbed drainage and track geometry. The blue outline shows where sleepers (ties) might be considered to be at risk of accelerated degradation.

Predict possible sleeper/tie condition deterioration

Poor trackbed condition does not only affect the functional condition of the track through track geometry deterioration, but can also lead to sleeper deterioration and breakages. Correlating typical trackbed conditions to sleeper condition can assist in predicting situations that lead to poor sleeper condition (Figure 4).

Figure 5: Example of trackbed inspection report (TBIR) combining track geometry exceptions (Top, Alignment, Twist) with a trackbed layer interpretation, a moisture likelihood index, GPR radargram, fouling indices and overall Trackbed Condition summary (TCS). Geometry exceptions correlate with incipient subsurface mud spots.

Assess the cause of a track geometry fault

As well as determining the extent of the problem, having a detailed picture of the state of the trackbed down to subgrade, can assist in determining the underlying cause of a fault (e.g. ballast pocket, subgrade failure, moisture). Figure 5 presents an example of a Trackbed Inspection Report (TBIR).

Where GPR data is collected in conjunction with geometry on track inspection vehicles the RASC® system can be configured to generate outputs in response to specific track geometry faults, enabling track engineers to quickly diagnose the potential cause of the fault.

Figure 6: Work order recommendation based on modelled residual ballast life categorized based on MGT as shown above left. Approximately 57 per cent of the 38-mile section is classified as having a residual ballast life of 250MGT or less. (CAT 5).

Residual ballast life prediction

GPR derived metrics and track geometry data can be used to predict remaining ballast life and help plan ballast maintenance.

Utilising the GPR-derived ballast fouling index (BFI) and track geometry roughness the residual ballast life (RBL) can be determined. Using a threshold of less than 250 MGT and clustering provides a tool for ballast renewals and planning (Figure 6).

The BFI data were combined with a track geometry Track Quality Index (TQI), to generate a ballast cleaning Work Order Recommendation (WOR). The WOR identified locations where either the shoulders or centre BFI was higher than a specified threshold and where the TQI was poor. The high resolution (15-ft) WOR results were clustered in order to identify minimum quarter-mile work sites, with sites constrained by road crossings, under-bridges and turnouts (switches).

Updating hazard locations register

RASC® surveys are effective at highlighting problem areas such as poor drainage, failed formation, poor sleeper support, slope instability, fixed structure clearances, vegetation encroachment and floodwater runoffs. These can be used to update a hazard location register.

Tel: 01993-886682 

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2019-03-01T10:47:31+00:00 March 1st, 2019|Magazine, March 2019|