12 th International Conference on Ground Penetrating Radar, June 16-19, 2008, Birmingham, UK Recent advances in the application of GPR for trackbed characterisation Qing Zhang, Jon Gascoyne, and Asger Eriksen Zetica Rail. Long Hanborough, Oxfordshire, OX29 8LH, UK email [email protected]In submitting this paper for EuroGPR2008 I hereby assign the copyright in it to the University of Birmingham and confirm that I have had the permission of any third party for the inclusion of their copyright material in the paper. The University of Birmingham will license EuroGPR to use this paper for non-commercial purposes. This will be the sole use of this material.
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12th International Conference on Ground Penetrating Radar, June 16-19, 2008, Birmingham, UK
Recent advances in the application of GPR for trackbed characterisation
Qing Zhang, Jon Gascoyne, and Asger EriksenZetica Rail.
In submitting this paper for EuroGPR2008 I hereby assign the copyright in it to the University of Birmingham and confirm that I have had the permission of any third party for the inclusion of their copyright material in the paper. The University of Birmingham will license EuroGPR to use this paper for non-commercial purposes. This will be the sole use of this material.
Abstract – Trackbed is defined here as including the layers of ballast and sub-ballast above a prepared sub-grade/formation, which altogether play a vital role in the stability of the rail sys-tem. Ground penetrating radar technology is widely used for track-bed scanning to detect areas of material composition change and layer thickness inhomogeneities. A novel method-ology is presented to process and interpret large volumes of GPR data in a consistent way to deliver information which can be readily utilised by railway engineers. Zetica Rail has developed a solution for reliable track-bed quality monitoring, by combining the knowledge of railway engineers and GPR specialists. Various frequencies of GPR antennae and signal processing techniques are used and evaluated, including data quality enhancement techniques, and interface tracking algo-rithms. Colour-index based classifiers have been constructed for ballast fouling and trackbed quality classification. The overall approach developed is proven to automatically identify problem track-bed areas and is invaluable for routine inspec-tion of the railway network pre- and post-renewals..
I. INTRODUCTIONThe functionality of a trackbed is determined by the
quality of the ballast/sub ballast and its supporting subgrade layer. Good ballast materials are dust-free angular stones, with the function of retaining track position by resisting vertical, lateral and longitudinal forces. Sub-ballast is a cheaper option than the otherwise thicker ballast, by reduc-ing the stress levels further to the subgrade. Subgrade is a pre-prepared foundation for supporting the track structure. Altogether, the quality of the trackbed plays a vital role in the stability of the railway system. Accurate knowledge of the substructure has been increasingly seen as essential for trackbed maintenance and efficient planning of renewals. Ground Penetrating Radar (GPR) technology has being widely recognized for its ability of non-destructive testing the subsurface structure and detecting drainage, erosion and moisture content.
Recent papers (Hyslip et. al. 2003 [4], Olhoeft et. al. 2002 [5], Olhoeft 2003 [6] and Selig 1994 [7]) have demonstrated the utility of GPR in solving a variety of problems related to trackbed characterization. Al-Nuaimy, Eriksen and Gascoyne (Al-Nuaimy et. al. 2004 [1]) have shown that GPR can be used to provide rapid, objective and quantitative information about the depth and degree of de-terioration of ballast with minimal disturbance to the actual trackbed. Ballast quality calibration using non-contact methods of calculating the in-situ propagation velocity of electromagnetic (EM) waves through ballast has been ex-plored by Gallagher, Leiper and Forde (Gallagher et. al. 2000 [3]). The use of a multi-channel road-rail GPR for im-proved productivity and reliability of ballast inspection has also been presented by Eriksen et. al. in [2]. Significant ef-fort has also been focused towards the extraction of mean-ingful physical interpretations from the GPR data using novel signal/image processing and pattern recognition tech-niques. Shihab, Nuaimy and Huang have presented a Neu-ral Network target identifier based on statistical features of GPR signals [8].
This paper summarises the research work conducted by Zetica Rail, in using various antennae for the purpose of evaluating trackbed quality and various data interpretation techniques. Zetica’s Advanced Rail Radar (ZARR) system has been developed for use on railways, with a typical sam-pling interval of 5cm at a running speed of 100km/h. The system combines GPR antennae mounted beneath inspec-tion trains, train tachometer inputs, global positioning sys-tem (GPS) and video technologies to achieve the precise data registration required for accurate calibration of the GPR results. To date over 20,000km of GPR data has been collected using ZARR installed on a single ultrasonic in-spection train. The system is currently being installed on an additional 2 inspection trains in the UK.
II. PRE-PROCESSING & INTERPRETATION TECH-NIQUES
The objective of data pre-processing is to enhance the quality of data collected by the system for better visualiza-tion of features of interest and to allow the interpretation to
12th International Conference on Ground Penetrating Radar, June 16-19, 2008, Birmingham, UK
be more accurate and efficient. Various techniques have been developed for the task.
• Drift RemovalIt is a technique to remove the amplitude “drift”, which
skews the signal histogram.• Background RemovalGPR working in the rail environment is subject to spu-
rious reflections, for example, from the metal rail. These are independent of the trackbed material hence need to be removed prior to the interpretation. Care must be taken when applying standard tools for improving the signal to noise ratio which can introduce artifacts into the data. Fig-ure 1 shows a typical example at either end of a bridge deck.
Figure 1. Artifacts introduced by applying standard back-ground removal algorithm (top – highlighted).
• Horizontal/Vertical band-pass FilteringThey are designed to reduce the high frequency noise
introduced by mobile stations/handsets operating in close proximity to the survey. Noisy data areas can be automati-cally excluded from further analysis preventing spurious re-sults.
• Signal amplitude restorationRadar signal is subject to attenuation in the material,
due to the moisture content and scattering loss. An empiri-cal gain function is applied to compensate for such loss. By so doing, the signal amplitude at a certain depth reflects the material dielectric change more accurately.
Any sharp change of dielectric permittivity in the ma-terial will appear as an “interface” or strong reflection in the radar B-scan image. Due to the complexity of material dielectric change in such environment, an algorithm is de-signed to track the interface in a full automatic or semi-au-tomatic way, depending on the complexity of data.
• Identifying subgrade materials with high water content A common feature found in radargrams over moist sub-grade materials is a mirror effect such as shown in Figure 2. Although the actual geometry of the sub ballast-subgrade
interface may be flat, the second interface (blue) appears to be a mirrored version of the first interface (red).
Figure 2. Typical mirror effect caused by the fluctuation of the first interface and the dielectric contrast between two ma-terials.
The mirror effect is caused by the fluctuation of the first in-terface and the dielectric contrast between the two materi-als.If the second layer material has a dielectric value very close to the first layer material, the mirroring effect is very lim-ited. The perfect mirror appears when the EM velocity slows down to half in the second layer. This requires a dielectric value of the second layer four times of that of the first layer. As the di-electric value of the second layer increases, the mirror effect is relatively magnified. Annex 1 elaborates on the theory of mirror reflections and derives a formula for determining the dielectric value of moist sub ballast layers.
• Ballast fouling index (BFI)Based on experiments on track with various conditions of ballast, a classification algorithm has been developed to de-rive a ballast quality ranking index. Figure 3 shows the re-sult of independently deriving the index for a section of track with known ballast conditions (Figure 4).
Figure 3. 1GHz radargram with BFI overlain (red is dirty bal-last, green is relatively clean)
12th International Conference on Ground Penetrating Radar, June 16-19, 2008, Birmingham, UK
Figure 4. ABS samples collected in clean (ABS 1) and dirty (ABS 2) ballast correlating with green and red indices in Fig-ure 3.
• Trackbed quality index (TQI)Ballast thickness, subgrade erosion/pumping, and near-sur-face wet beds, are all examples of issues affecting trackbed integrity. A comprehensive measure has been developed based on GPR data to automatically flag up these problem-atic areas and to provide engineers with a spatial overview of the trackbed quality.
III. USE OF MULTIPLE FREQUENCY ANTENNAE
Extensive research work has been conducted for the evaluation of ballast condition, using a combination of an IDS 400 MHz ground-coupled antenna and GSSI 1GHz and 2GHz air-launched horn antennae. The 1GHz horn antenna is mainly utilised for high resolution scanning of ballast/sub‐ballast layers. The main application of the 400MHz antennae is to identify deeper second-ary layers in the sub‐ballast/sub‐grade section, which may be beyond the range of detection of the 1GHz antenna. This combination has been proved to be extremely success-ful in the work carried out to date [1]. The 2GHz horn an-tenna was determined to be of interest for offer-ing improved resolution of the ballast layers and for detecting subtle changes in ballast quality. Figure 5, 6 and 7 shows radargrams collected by antennae with different frequen-cies over the same section of track.
Figure 2. B-scan radargram collected with a 400 IDS ground-coupled antenna.
Figure 3. B-scan radargram collected by a 1GHz GSSI horn antenna over the same track section.
Figure 4. B-scan radargram collected with a 2GHz GSSI horn antenna.
Comparison of the results demonstrates that the 400MHz antenna provides the lowest resol-ution and limited information from within the shallow ballast section. However, signal penet-ration is relatively deeper – the 400MHz EM wave can penetrate up to 2 metres providing useful information on the subgrade and deeper sup-porting layers.
Signal from the 1GHz horn antenna can penetrate to ap-proximately 1.2 metres depth depending on ballast condi-tions with improved resolution in the ballast section. The ballast interface is usually easily identifiable and is mapped using the interface tracking al-gorithm which locates the transient of signal amplitude within each A-scan and searches for the continuity of the effect in the B-scan.
We have found that signal penetration of the 2GHz horn antenna is limited to approximately 900mm depending on ballast conditions. This data provides the greatest detail in the shallow primary ballast layer. The signal beam of the 2GHz horn antenna is more focussed than 1GHz and 400MHz antennae, which results in increased energy being sent to the ground and less interference from side lobes appearing in the data.
12th International Conference on Ground Penetrating Radar, June 16-19, 2008, Birmingham, UK
IV. ZETICA’S TEST SITE
Zetica Rail has constructed and recently commissioned a test trackbed of approximately 60m length, comprising pre-cisely known conditions of ballast quality, ballast thick-ness, sub ballast materials, sleeper types, cross-cutting drains, and buried obstacles. The track is instrumented with moisture probes in the various trackbed layers and also contains metal plates at specific burial depths to assist in calibrating GPR signal velocities.
Figure 8. Construction of Zetica’s test trackbed, Oxfordshire
Figure 9. 3D CAD drawing of Zetica’s test trackbed with inset showing multi-channel GPR system installed on a trolley.
V. CONCLUSIONSExtensive research has been conducted for the evaluation of trackbed condition, using differ-ent frequencies of antennae and various signal-processing techniques. The mirror effect
caused by the fluctuation of ballast interface is examined. A formula for solving the dielectric relation between two layered materials is de-rived. In particular, this formula can be very useful in locating material with relatively large dielectric values, usually associated with mois-ture or water content. We have examined the radargram characteristics by anten-nae with different frequencies, specifically, 400MHz, 1GHz, and 2GHz. The advantages and limitations of each antenna have been addressed. Finally, we have reported on the construction of a unique test trackbed in the UK which will contribute significantly towards our understanding of, for example, the effect of rainfall on track indices derived from GPR data.
REFERENCES[1] Al-Nuaimy, W., Eriksen, A. and Gascoyne, J., ‘Im-
proved Productivity and Reliability of Ballast Inspec-tion using Road-Rail Multichannel GPR’, Railway En-gineering 2004, 6th – 7th, July 2004, Commonwealth In-stitute, London, UK.
[2] Eriksen, A.,Venables, B., Gascoyne, J., and Bandy-opadhyay, S., 2006, ‘Benefits of high speed GPR to manage trackbed assets and renewal strategies’, PWI Conference, June 2006, Brisbane, Australia.
[3] Gallagher, G.P., Leiper, Q.J. and Forde, M.C., 2000, ‘How to Calibrate Radar Testing of Trackbed without Trial pits’, Int’l. Railway Engineering Conf., 2000, London, UK.
[4] Hyslip, J.P., Smith, S.S., Olhoeft, G.R., and Selig, E.T., 2003, ‘Assessment of Railway Track substructure Condition using Ground Penetrating Radar’, AREMA, 5-7 Oct. 2003, Chicago, 20p.
[5] Olhoeft, G.R. and Selig, E.T., 2002, ‘Ground Penetrat-ing Radar Evaluation of Railroad Track Substructure Conditions’, GPR 2002, 9th Int’l. Conf. On Ground Penetrating Radar, Santa Barbara, CA, April 2002, S.K. Koppenjan and H.Lee, eds., Proc. SPIE vol. 4758, pp. 48-53.
[6] Olhoeft, G.R., 2003, ‘Electromagnetic Field and Mate-rial properties in Ground Penetrating Radar’, Proc. 2nd Int’l. Workshop on Advanced GPR, 14-16 May 2003, Delft, The Netherlands, A.Yarovoy, ed., p.144 –147.
[7] Selig, E.T. and Waters, J.W. (1994). Track geotechnol-ogy and substructure management. Thomas Telford Ltd., London.
[8] Shihab, S., Al-Nuaimy, W., Huang, Y. and Eriksen, A., ‘Neural Network Target Identifier based on Statistical Features of GPR Signals’, International conference on ground penetrating radar, 9th, 2002, vol. 4758, pp. 135-138.
12th International Conference on Ground Penetrating Radar, June 16-19, 2008, Birmingham, UK
ANNEXE 1: EXPLANATION OF A MIRROR EFFECT OBSERVED IN GPR TRACKBED DATA
A typical trackbed consists of three layers, namely ballast, sub-ballast and subgrade. When the radar signal reaches the boundary between two layers, due to the dielectric contrast between two materials, energy is reflected back to cause a “ripple” in the radar A-scan. If the survey is conducted along the layer, a continuous band will appear in the radar B-scan as an indication of the existence of layered material. Theoretically, the time the reflection occurs in the radar A-scan indicates how long the EM wave has been travelling through the material in a round trip. Given a constant of di-electric value for the material, hence a constant velocity, the thickness of the material can be derived by examining the radar B-scan, where the continuous band follows ex-actly the same geometry as the layer. However, this is the case only when the first interface is flat. Quite often, the ballast/sub ballast interface becomes wavy because of con-tamination, pumping or corrosion. The trackbed geometry has been altered such that a lot of variation happens be-tween the ballast and sub ballast layers. Although the sub ballast/subgrade interface is still flat, it will exhibit a mirror effect in the radargram due to the change of the dielectric permittivity. Figure 1 shows a typical example of such mir-ror effect. Although the actual geometry is flat, the second interface appears to be a mirrored version of the first inter-face. The mirror effect is in fact caused by the fluctuation of the first interface and the dielectric contrast between the two materials. In the following context, we are going to present the mathematics behind the mirror effect.Figure A1 simulates the perfect mirror effect, where the top panel illustrates the ground-truth formation geometry and dielectric permittivity values of top three layers and bottom panel simulates the radar arrival time at each interface.
Figure A1. Simulated mirror effect based on known geometry and dielectric permittivity values of the formation.
There are altogether four layers, which are air gap, ballast and sub-ballast and subgrade, with the top three having di-electric index 1, 7 and 28 respectively. The arrival time at the interface between ballast/sub ballast is denoted by
and is the arrival time of sub ballast/subgrade interface. Based on the permittivity values, the relation between two arrival times is given by,
where is an imaginary line with the dielectric value of the sub-ballast exactly the same as the ballast. As the dielectric value of sub-ballast increase, the mirror effect starts to ap-pear. When the dielectric value of the sub ballast is four times that of the ballast, the perfect mirror appears, because the EM traveling velocity in the sub ballast is exactly half of that in the ballast. To further elaborate, Figures A2 and A3 illustrate two examples of “no-so-perfect” mirror ef-fects.
12th International Conference on Ground Penetrating Radar, June 16-19, 2008, Birmingham, UK
Figure A2. Limited mirror effect due to the similar values of the dielectric permittivity of two layers.
Figure A3. Magnified mirror effect when the dielectric value of the sub-ballast is significantly larger than the ballast layer
To summarize the findings, If the second layer material has a dielectric value
very close to the first layer material, the mirroring effect is very limited, as illustrated in Figure A2.
The perfect mirror appears when the EM velocity slows down to half in the second layer. This re-quires a dielectric value of the second layer four times of that of the first layer. Figure A1 illustrates the scenario.
As the dielectric value of the second layer in-creases, the mirror effect is more obvious magni-fied, as shown in Figure A3.
A.1 Solving for the dielectric values. Apparently, this mirror effect is particular useful for esti-mating the dielectric relations of the local area. By examin-ing how “good” the mirror is, it is possible to derive the ra-tio of the dielectric values between two layered materials. Within a mirrored section, let
where is a coefficient introduced to evaluate the degree
of mirroring. The perfect mirror corresponds to . A
close-to-zero corresponds to very limited mirror effect
and a large value of corresponds to magnified mirror ef-fect. Substitute into the previous equation, it yields,
Now the problem converts to finding the best and to match the mirrored layers. This can be achieved by a chi-square fitting procedure,
where is the number of points taken for calculation within the mirrored section. By calculating the zero gradients, the value of k, hence the ratio of dielectric values can be esti-mated as,
where , , , and is the number of points taken for calculation.
