SUBSTRUCTURE MAINTENANCE MANAGEMENT: ITS TIME HAS COME

Transcription

1 SUBSTRUCTURE MAINTENANCE MANAGEMENT: ITS TIME HAS COME by James P. Hyslip, Ph.D., P.E. HyGround Engineering, Williamsburg, Massachusetts, USA (Text = 2,650 words) (5 Figures) September 2007

2 ABSTRACT This paper presents a methodology for Substructure Maintenance Management (SuMM) that utilizes recent advancements in technology. Technology advancements in the past decade are making the near-term implementation of a robust and effective SuMM program a reality. These technology advancements in the railway industry include: ground penetrating radar (GPR), moving track stiffness measurements, track geometry analysis techniques and software/hardware for analysis and visualization of information. The latest developments in these areas are presented. This paper includes a discussion of the need and benefit of a holistic SuMM approach on heavy haul corridors. Keywords: track, ballast, subgrade, substructure, maintenance management

3 1.0 INTRODUCTION Substructure Maintenance Management (SuMM) is defined as the process of engineering investigations and decisions which have the objective of optimizing the use of the railroad s resources for maintenance of track substructure performance at the required level (Selig, 1997). In other words, SuMM is the process to effectively and economically monitor and maintain the track substructure to acceptable performance levels. The Substructure Maintenance Management (SuMM) approach relied on a number of technologies that have significantly matured since introduced in 1997 by Dr. Ernie Selig, to the point where these technologies are now making the near-term implementation of a robust and effective SuMM program a reality. This paper is intended to update the SuMM approach in the light of present technology advancements. Specifically, this paper will highlight the key areas of SuMM related technologies, namely: ground penetrating radar (GPR), moving track stiffness measurements, track geometry analysis, and integrated databases. 1.1 Need On many railroad lines in North America the track substructure layers (ballast, subballast, subgrade) are being increasingly subjected to unprecedented stress levels due to increased traffic and heavier axle loads. Additionally, some existing freight mainlines are being considered for joint use with high-speed passenger service where new and different dynamic loads and vibrations will need to be addressed. The increased repetitions of higher and higher loads are profoundly affecting the performance and life of the track (Selig & Hyslip, 2003). In fact, track that has been historically stable may begin to deteriorate rapidly after the onset of HAL traffic. Deteriorating substructure condition results in rough track geometry, variation in track stiffness and premature deterioration of track components. Deterioration of the substructure is

4 typically the result of deformation of the substructure layers. The layers deform most commonly due to progressive settlement which is exacerbated by such things as poor drainage and excessive fouling levels in the ballast. A significant part of a railroad s track maintenance budget is allocated, either directly or indirectly, to correct problems associated with poor substructure performance. Direct costs include tamping, ballast cleaning, ballast renewal and ditching, as well as remediation of discrete chronic problem locations. Indirect costs are associated with the deleterious effects of poorlyperforming substructure on superstructure components, such as ties, rails and fastening, and to a lesser extent, the rolling stock.. An increasingly significant indirect cost is the lost opportunity cost associated with taking the track out-of-service. Providing high levels of track availability for revenue service, with minimum maintenance-related interference, can be a big incentive to address the root-cause of bad track performance (Ebersöhn, 1997). 1.2 Benefit An effective SuMM program provides the following benefits: Overall smoother track for a given level-of-service. Increased availability of track by minimizing maintenance interference. Reduced rate of track deterioration. Reduced maintenance and capital costs by virtue of: focusing resources to when and where they are most effective, and; addressing the root-cause of the underlying problem rather than continuing to treat only the symptoms. Ability to address poorly-performing substructure issues on a network-based level where budgets are available for a systematic and economy-of-scale approach.

5 2.0 APPROACH The approach to SuMM has three fundamental tasks (Selig, 1997): 1) Distinguish areas with different maintenance demands. 2) Identify factors affecting performance in each area. 3) Develop most cost effective maintenance plan. The key aspect of the SuMM approach is the effective diagnostics and treatment of the substructure problem. The specific approach to SuMM has four main tasks (Hyslip & McCarthy, 2000): 1) Investigation, including review of background information (desk study) and field investigation. 2) Engineering analysis, including both technical and economic aspects of the solution. 3) Implementation, including strategic and tactical planning of the work. 4) Monitoring, in order to evaluate the effectiveness of the work and to provide a basis for determining the applicability of the solution to other sections of track. The following sections of this paper will focus on the investigation aspects of SuMM. 2.1 Investigation The fundamental indicator of track condition is track geometry, and in most cases, the condition defining the need for SuMM is poorly-performing track geometry. Track geometry data from the Track Geometry Measurement Vehicle (TGMV) provides an objective indication of the roughness of track, and is useful in distinguishing the sections of track with different performance. When integrated with traffic and maintenance information, the track geometry data can help prioritize the section of track, or areas of the railroad, for application of SuMM.

6 Once high-priority areas are determined the inspection phase of SuMM begins. The inspection should start with a review of background information in order to develop an understanding of a track substructure problem (Hyslip & McCarthy, 2000). The background information can be separated into two general categories: 1) railway information, which includes track geometry condition and performance, maintenance history and traffic characteristics; and, 2) geotechnical information, such as topography, soil, ground water, rainfall and geology. Background for a specific section of track used to require a site reconnaissance through a walking or hi-rail inspection. However, with the prevalence of high-resolution video on track geometry cars and the increasing availability of high resolution videos and photos from track-based or aerial surveys, the initial site reconnaissance can oftentimes now be performed in the office. In some instances, even relatively coarse resolution images from sources such as GoogleEarth or from USGS digital ortho-photoquads can be used. The initial field reconnaissance should specifically look for such things as evidence of ballast fouling, drainage problems, and surrounding topographic condition. The review of background information is followed by the field investigation phase, which is described later in this paper. 2.2 Engineering Analysis Technical evaluation and analysis of the collected field and laboratory information naturally follows the investigation phase of SuMM. The analysis is needed to determine the cause of the problem and select appropriate solutions. The engineering analysis that is needed can include such tasks as assessing drainage requirements, evaluating adequacy of granular layer thickness to protect the subgrade, slope stability analysis, evaluating suitability of subballast to provide drainage and to maintain separation between subgrade and ballast, determining support characteristics of subgrade, and determining type and source of ballast fouling (Hyslip &

7 McCarthy, 2000). Detailed discussions of these topics can be found in Track Geotechnology and Substructure Management (Selig & Waters, 1994). The engineering analysis task should also include an economic analysis of remedial alternatives to determine the most cost-effective solution. The appropriate solution should be selected considering such factors as effectiveness, cost, least disruption to train traffic and ease of implementation. Often various degrees of remediation can be applied to substructure problems, with the level of fix determined by economic factors, as well as the practicality and timing of the work. (Hyslip & McCarthy, 2000). 3.0 INVESTIGATION TECHNIQUES The field investigation phase is intended to identify the factors affecting the track performance. A number of techniques for investigation of track substructure condition are now reaching maturity, and are providing a means to effectively carry-out an effective SuMM approach. In particular, ground penetrating radar (GPR), moving track stiffness, track geometry data analysis and robust inexpensive databases and computing power. In some localized areas, where landslide and sinkhole instability is a problem, the investigation may require conducting test borings and installing geotechnical instrumentation. The following is a brief summary of some of the latest developments in field investigation techniques that underlie an effective SuMM approach. 3.1 Ground Penetrating Radar The development of Ground Penetrating Radar (GPR) for use on railroad tracks has advanced significantly in recent years. GPR is being used on railroad tracks to provide continuous measurement of the condition of track substructure layers at fast measurement speeds and

8 providing information for maintenance and rehabilitation decision making. (Hyslip et al, 2005; Silvast et al, 2006; Eriksen et al, 2006; Roberts et al, 2006). GPR is useful for: Determining the layer thickness and configuration (lateral and longitudinal variation) of individual substructure layers. Assessing moisture content and water holding tendencies of the substructure. Evaluating the fouling condition of the ballast. Targeting more invasive investigation, e.g., cross-trenches, automatic ballast sampling. Performing quality control of substructure maintenance, in particular, undercutting. Table 1 presents various substructure problems and the corresponding GPR information that can be measured for use in determining the root cause of the problem. The GPR method requires transmitting pulses of radio energy into the subsurface and receiving the returning pulses that reflected off interfaces between materials with different electromagnetic properties, as depicted in Figure 1. Antennas are moved across an area with a continuous series of radar pulses, giving a profile of the subsurface. Antennas are designed to operate at various frequencies from hundreds of MHz (megahertz) to a couple GHz (gigahertz). There are various types of antennas that are currently being used on North American railroads, including 400 MHz and 1-GHz systems, as shown in Figure Moving Track Stiffness Measurements Track stiffness is a measure of the vertical stiffness of the entire track substructure including the rails, ties, fasteners and substructure. A related term, track modulus, is a measure of the vertical stiffness of the track foundation without the effects of the rail. Both track stiffness and modulus are condition measurements of the track structure and are thus related to track performance (Li &

9 Selig, 1994). Several parameters from track stiffness measurements can be used for track condition diagnostics, namely (Sussmann et al, 2001): Low track stiffness for location of areas with soft subgrade problems. Variable track stiffness or location of non-uniform track support. Void deflection for location of fouled ballast, hanging ties and poor tie fastener condition. Variable total deflection for diagnosis of loaded rail profile deviations that could result in ride quality problem areas. Inconsistent rail deflection for diagnosis of tie center-binding. Furthermore, too high or too low track stiffness can result in premature component wear, and variations in track stiffness can result in increased dynamic loading and high component deterioration rates. The Transportation Technology Center Inc. (TTCI) in Pueblo, CO has been developing vertical track stiffness measuring techniques for a number of years (Thompson et al, 2001; Li et al, 2004). TTCI s system directly measures deflection of the railhead under various loaded and unloaded conditions using chordal-offset measurements from a reference frame mounted on the measurement vehicle (Li et al, 2004). Researchers at the University of Nebraska have developed a non-contact modulus measuring system that measures vertical rail displacement relative to the wheel-rail contact point and in turn theses relative measurements are combined with an analytical model of the track and the vehicle to estimate the track modulus (Farritor, 2006). The key to this system is the sensor which consists of a digital vision system and two line lasers, as depicted on Figure 3. As shown in Figure 3 (from Farritor, 2006), the deflection of the rail is computed from the distance between the two line lasers, d, and this distance increases or decreases depending on the deflection of the

10 rail. The wheel on which the bracket is attached has a know weight that is used, along with the deflection value, to calculate the track stiffness. Banverket, which is Sweden s maintenance and operational railway authority, has developed a Rolling Stiffness Measurement Vehicle (RSMV) for continual vertical stiffness measurements speeds from approximately 5 mph for detailed surveys to 35 mph for broader assessments (Smekal et al, 2006). This system works by dynamically exciting the track using two oscillating masses above a load wheel on the measurement vehicle, as depicted on Figure (Berggren, 2005). Track stiffness is calculated from measured axle box forces and accelerations (Berggren, 2005; Berggren et al, 2005). 3.3 Track Geometry Data Analysis Meaningfully quantifying track geometry measurements that are obtained from Track Geometry Measurement Vehicles (TGMV), i.e., track geometry cars, can lead to the data becoming a much greater part of the railroad SuMM approach. As discussed earlier, track geometry data is useful in the SuMM approach to distinguish track with different performance. But more than this, quantifying track geometry data and developing the trends of values over time from survey to survey can help predict the future condition of the track (Ebersöhn & Selig, 1994). Maintenance and/or remedial measures, including the allocation of capital resources, can then be planned based on these predictions. Also, comparing quantified geometry data for different sections of track can be used to rank the track sections for maintenance prioritization. Track geometry patterns, in particular vertical profile (surface) patterns, are influenced by the track's substructure condition. By meaningfully quantifying the vertical profile geometry pattern it is possible to obtain useful information on the substructure condition of the track (Hyslip, 2002). The functional condition of railway track, i.e. the loaded position of the rails, depends on both the unloaded geometry profile and the elastic deflection of the track under load,

11 and is directly indicated by the geometry data obtained by track geometry recording cars. The elastic deflection component of the geometry measurement is related to the structural behavior of railway track. Longitudinal variation of substructure condition, from such common things as variable ballast fouling, changing drainage condition and variable subgrade stiffness, profoundly affects vertical profile TGMV data. One particular area showing promise is the use of fractal analysis of vertical surface data to determine the fouling condition of the ballast (Hyslip, 2002). Specifically, research is showing the higher the fractal roughness of vertical surface data, the greater the amount of ballast fouling. This higher fractal roughness is associated with high and non-uniform plastic (non-recoverable) settlement of the ballast layer when clogged with fouling material. The amount of plastic settlement of fouled ballast can vary from tie to tie, depending upon factors such as degree of fouling and water content. Therefore, the structural support conditions, including the amount of void (slack) under individual ties, can vary longitudinally in short distances. This short-distance longitudinal variation can translate to high-frequency roughness in TGMV measurements. Fractal dimensions (Hyslip, 2002) and FRA-TQI (El-Sibaie & Zhang, 2004) can quantify the high-frequency component of TGMV data. 3.4 Integrated Database A key aspect of SuMM is the integration of the disparate track condition data and presenting the information in an intuitive and useful way. The combination of continuous measurements of track geometry quality, track stiffness and GPR provides important information about the conditions of the track. An integrated database and easy to use graphical user interface (GUI) system allows for understanding of complex interaction between condition and structural performance (Ebersöhn & Ruppert, 2002). A database and viewer system are key to interrelating the various disparate data

12 (e.g. topography, geometry data, GPR, stiffness) in order to develop a clear and accurate picture of the factors affecting track performance. The use of integrated software is very important since the combined evaluation of several different measurements is a key success in finding root causes to existing problems and predict future problems (Smekal et al, 2006). A few robust database systems for integration and viewing railway track condition data are commercially available and are able to run on standard desktop or laptop computers (Terrill et al, 2004; Silvast et al, 2006; Jovanovic, 2006). RailDoctor (Silvast et al, 2006) is a particularly effective tool for integrating GPR, stiffness, geometry data, video and mapping. Figure 5 presents an example of the RailDoctor graphical user interface. 4.0 CONCLUSION In consideration of the profound ways in which the track substructure affects the overall track performance it is imperative that the railroad industry take a holistic view of the track system, and subsequently, more strongly emphasize the functional relationship between the track substructure and the track s overall health. Instituting an effective Substructure Maintenance Management program, following the approach outlined herein and utilizing the new advances in technology discussed in this paper, will enable the railroads to better address the root cause of bad track performance. In today s high volume and heavy axle load environment, the root cause of bad track is increasingly becoming the deterioration of track substructure.

13 REFERENCES 1. Berggren, E. (2005). Dynamic Track Stiffness Measurement A New Tool for Condition Monitoring of Track Substructure. Licentiate Thesis TRITA AVE 2005:14, Royal Institute of Technology (KTH), Stockholm, Sweden. 2. Berggren, E., Jahlenius, A, Bengtsson, B. and Berg, M. (2005). Simulation, Development and Field Testing of a Track Stiffness Measurement Vehicle. Proc. of 8 th International Heavy Haul Conference, Rio de Janeiro, June. 3. Ebersöhn, W. and Selig, E. T. (1994). "Use of Track Geometry Measurements for Maintenance Planning". Transportation Research Record No. 1470, Transportation Research Board, Washington, DC. 4. Ebersöhn, W. (1997). "Track Maintenance Management Philosophy". Proc. 6 th International Heavy Haul Railway Conference, International Heavy Haul Association, Cape Town, South Africa, April. 5. Ebersöhn, W. and Ruppert, C. (2002). Railway Infrastructure Maintenance Management. Workshop on Track Maintenance Management, Transportation Research Board Annual Meeting, Washington, DC, January. 6. El-Sibaie, M. and Zhang, Y.J. (2004). Objective Track Quality Indices. 83rd TRB Annual Meeting, January 11-15, Washington, DC. 7. Eriksen, A., Venables, B., Gascoyne, J. and Bandyopadhyay, S. (2006). Benefits of High Speed GPR to Manage Trackbed Assets and Renewal Strategies. Permanent Way Institute (PWI) Conference Proc., Brisbane, Australia, June. 8. Farritor, S. (2006). Real-Time Measurement of track Modulus from a Moving Car. NTIS Report No. FRA/ORD-05/05, Federal Railroad Administration Office of Research and Development, US Dept. of Transportation, Washington, DC, January.

14 9. Hyslip, J. P. and McCarthy, B. M. (2000). Substructure Investigation and Remediation for High Tonnage Freight Line. Proc. of the 2000 Annual Conference, AREMA, Dallas, TX, September. 10. Hyslip, J. P. (2002). Fractal Analysis of Railway Track Geometry. Transportation Research Record No. 1785, Transportation Research Board, Washington D.C. 11. Hyslip, J. P., Olhoeft, G. R., Smith, S. S., and Selig, E. T. (2005). Ground Penetrating Radar for Railroad Track Substructure Evaluation. NTIS Report No. FRA/ORD-05/04, Federal Railroad Administration Office of Research and Development, US Dept. of Transportation, Washington, DC, October. 12. Jovanovic, S. (2006). Railway Track Quality Assessment and related Decision making. Proc. of the 2006 AREMA Annual Conference, Louisville, KY, September. 13. Li, D., Thompson, R. and Kalay, S. (2004). Update of TTCI s Research in Track Condition Testing and Inspection. Proc. of the 2004 AREMA Annual Conference, Nashville, TN, September. 14. Roberts, R., Al-Qadi, I., Tutumluer, E., Boyle, J. and Sussmann, T. (2006). Advances in Railroad Ballast Evaluation Using 2-GHz Horn Antennas. Proc. of 11 th International Conferences on Ground Penetrating Radar, Columbus, OH, June. 15. Selig, E. T. and Waters, J. M., (1994). Track Geotechnology and Substructure Management. Thomas Telford Services Ltd., London. 16. Selig, E. T. (1997). "Substructure Maintenance Management". Proc. 6 th International Heavy Haul Railway Conference, International Heavy Haul Association, Cape Town, South Africa, April. 17. Selig, E. T. and Hyslip, J. P. (2003). Effects of Heavy Axle Loads on Track Substructure. Proc. Of Implementation of Heavy Haul Technology for Network Efficiency, International Heavy Haul Association Specialist Technical Session, Dallas, TX, May. 18. Silvast, M., Levomaki, M., Nurmikolu, A. and Noukka, J. (2006). NDT Techniques in Railway Structure Analysis. Congress Proc. 7 th World Congress on Railway Research, Montreal, Canada, June.

15 19. Smekal, A., Berggren, E.G. and Silvast, M. (2006). Monitoring and Substructure Condition Assessment of Existing Railway Lines for Upgrading to Higher Axle Loads and Speeds. Congress Proc. 7 th World Congress on Railway Research, Montreal, Canada, June. 20. Sussmann, T., Ebersöhn, W. and Selig, E. (2001). Fundamental Nonlinear Track Load- Deflection Behavior for Condition Evaluation. Transportation Research Record No. 1742, Transportation Research Board, Washington D.C. 21. Sussmann, T.R., Hyslip, J.P., Selig, E.T. (2003). Railway Tracks Condition Indicators from Ground Penetrating Radar. NDT&E International, 36 (2003) Thompson, R., Marquez, P. and Li, D. (2001). Track Strength Testing using TTCI s Track Loading Vehicle. Railway Track & Structures, December. 23. Terrill, V.R., Selig, T., and Hyslip, J.P. (2004). A Visual Track Maintenance Management System. Proc. of Railway Engineering 2004 Conference, London, June.

16 List of Tables Table 1: Substructure Problems and Corresponding GPR Measurement (adapted from Hyslip et al, 2005) List of Figures Figure 1: The Generation of a GPR Profile. (a) Transmitted energy is reflected off the boundaries in the substructure. (b) A single trace or scan composed of the reflection amplitudes for the reflections in (a). (c) Multiple scans are generated in quick succession. (d) Adjacent scans are combined to build a gray-scale image profile. (from Hyslip et al, 2005). Figure 2: GPR Hi-Rail Setup with three pairs of 1-GHz antennas (top), and two sets of 400 MHz antennas (bottom) Figure 3: Representation of University of Nebraska s track stiffness measurement system (from Farritor, 2006) Figure 4: Schematic figure of Banverket s RSMV measurement principal (from Berggren 2005). Figure 5: Sample user interface of RaiDoctor showing integrated GPR data, continuous track stiffness data, geometry roughness, video and map.

17 Table 1: Substructure Problems and Corresponding GPR Measurement (adapted from Hyslip et al, 2005) Substructure Problem Poor drainage trapped water Poor drainage layer depression (bathtub) Fouled ballast Subgrade failure or deformation Subgrade attrition Subgrade excessive swelling and shrinking Longitudinal variation of the condition Transitions GPR Indices Based On: Intensity of GPR reflection and moisture contents of ballast/subballast layers. Difference in depth to impermeable subgrade surface laterally across the track. (i.e., lateral layer thickness variation). Dielectric dispersion, GPR pattern scattering textures, and correlated dielectric permittivity of ballast layer. Ratio of layer thickness and/or subgrade surface depth from middle to edge of tie. Also, moisture content & consistency of subgrade soil along with thickness of granular layer. Lack of subballast layer in combination with fine-grained fouling. Variation of clay subgrade surface. Also, moisture content and consistency of subgrade soil. Variation (roughness) of layer thickness, moisture content, composition. Rate of variation of substructure layers along or across track.

18 Transmit Antenna Receive Antenna Amplitude Time Air Surface Ballast Subballast Subgrade Top of Subballast Top of Subgrade (a) (b) (c) (d) Figure 1: The Generation of a GPR Profile. (a) Transmitted energy is reflected off the boundaries in the substructure. (b) A single trace or scan composed of the reflection amplitudes for the reflections in (a). (c) Multiple scans are generated in quick succession. (d) Adjacent scans are combined to build a gray-scale image profile. (from Hyslip et al, 2005).

19 Figure 2: GPR Hi-Rail Setup with three pairs of 1-GHz antennas (top), and two sets of 400 MHz antennas (bottom).

20 Figure 3: Representation of University of Nebraska s track stiffness measurement system (from Farritor, 2006). Figure 4: Schematic figure of Banverket s RSMV measurement principal (from Berggren 2005).

21 Figure 5: Sample user interface of RaiDoctor showing integrated GPR data, continuous track stiffness data, geometry roughness, video and map.