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Maintenance Planning for Rail Asset Management—Current Practices (2020)

Chapter: Chapter 3 - Survey Results

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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
×
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
×
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
×
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Suggested Citation:"Chapter 3 - Survey Results." National Academies of Sciences, Engineering, and Medicine. 2020. Maintenance Planning for Rail Asset Management—Current Practices. Washington, DC: The National Academies Press. doi: 10.17226/26012.
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11 The survey questionnaire was sent to 28 transit agencies, 16 of which replied, as presented in Appendix A. The respondents represented a significant portion of U.S. heavy- and light-rail transit systems, including all of the top five (and seven of the top 10) U.S. heavy-rail transit systems, representing 80% of heavy-rail transit route miles, and seven of the top 10 U.S. light- rail transit systems, representing approximately 40% of light-rail transit route miles. This chapter summarizes the results of the survey. Note that the complete tabulation of survey results can be found in Appendix C. 3.1 Inspection for Rail Defects: Technologies and Hardware 3.1.1 Currently Used Technologies The majority of transit agencies found the most defects through ultrasonic testing (UT), with virtually all agencies reporting that they conducted UT at least once annually. Of the 14 agencies that reported the percentage of defects found, 10 found the majority of their defects through UT, two through visual inspection by a track inspector, and two by track circuits (see Figure 1). Note that two agencies did not respond to the question. Although some transit agencies (New York City Transit Authority [NYCT] and Washington Metropolitan Area Transit Authority [WMATA]) reported using other NDT methods such as video inspections of the rail, they did not provide specific details. WMATA did note that such methods were part of a pilot program and have not yet been incorporated into the agency’s routine inspections. Of the 16 responding agencies, four own testing equipment, two are in the process of purchas- ing testing equipment, and the remainder contract for inspection equipment and services (Figure 2). All 16 agencies either contract some testing currently or have done so until recently. 3.1.2 Emerging Technologies Although several new technologies are now available for use by transit agencies to inspect track for rail defects and broken rails, those technologies have yet to be widely adopted. Such technologies include eddy current testing, which is used to measure the depth of RCF cracks in the gauge corner of the rail, and video inspections. Only two agencies, WMATA and NYCT, reported using NDT other than UT on a regular basis, and WMATA is planning to imple- ment a pilot eddy current testing program for RCF (Figure 3). It stands to reason that these two agencies would be among the first to have regular testing programs that incorporate this emerging technology, as their systems are large enough to warrant additional testing beyond UT and have sufficient capital to purchase newer, more specialized equipment. C H A P T E R 3 Survey Results

12 Maintenance Planning for Rail Asset Management—Current Practices 0 2 4 6 8 10 12 14 16 UT and Supplemental UT Alone Figure 3. Distribution of systems using UT and supplemental NDT (routine), versus UT alone. 0 2 4 6 8 10 12 UT Visual Inspection Track Circuits Figure 1. Number of agencies that found majority of defects by testing, by method. 0 2 4 6 8 10 12 Owned In Process of Purchasing Do Not Own Figure 2. Distribution of testing equipment ownership.

Survey Results 13 3.2 Inspection for Rail Defects: Scheduling 3.2.1 Standards and Regulations In part because of the nature of transit operations and the current structure of oversight at the federal, state, and local levels, there is no current industrywide standard or regulation for schedul- ing inspections. Thus, transit agencies must either determine which external standards they will follow or develop their own internal standards. For those agencies that decide to look for external regulations or guidelines, several options are available, including Title 49, Part 213.237, of the Code of Federal Regulations (CFR) (FRA, 2019), and FTA or APTA standards. Of the 16 agencies that responded to the survey, seven reported using the standards in CFR Title 49, Part 213.237, and one agency (WMATA) reported adapting the standards for its own system. Appendix C provides a full set of responses by agency. In addition, three agencies reported using APTA standards; Metro Transit in Minneapolis specified that it used the APTA standards for embedded track. The remaining systems used FTA standards, their own internal standards, state standards, or some combination of standards. Figure 4 contains the distribution of standards used for testing. Note that Denver’s Regional Transportation District (RTD) uses both FRA and FTA standards, so that agency was distributed between those two columns in the chart. 3.2.2 Current Practices Of the 16 agencies that responded to the survey, the majority (11) reported conducting UT once per year, with the distribution of testing frequencies shown in Figure 5. Three agencies 0 1 2 3 4 5 6 7 8 9 CFR Title 49, Part 213.237 APTA Standards FTA or Other N um be r o f S ys te m s Figure 4. Method of scheduling UT and other rail NDT testing. 0 2 4 6 8 10 12 1x per year 2x per year 3x per year 4x per year N um be r of S ys te m s Frequency of Testing Figure 5. Frequency of UT.

14 Maintenance Planning for Rail Asset Management—Current Practices reported testing twice per year, one reported testing three times annually, and one reported test- ing four times annually. Generally, the larger the system, the more frequent its testing regimen (see Figure 6). Figure 7 presents this information as a bubble chart, with the bubble size repre- senting the number of passenger miles traveled each year. All agencies that responded conduct visual inspections. Of the 16 agencies that responded, 10 conduct visual inspections twice per week, one conducts them three times per week, four conduct them once per week, and one agency did not provide this information (Figure 8). There does not seem to be any trend between system size or passenger miles carried and the frequency of visual inspections among agencies surveyed (Figure 9). The frequency of UT and visual inspection is further discussed in Section 4.2. Transit systems also use track circuits or signal systems as an additional means of detecting broken rails and minimizing the risk of a derailment caused by broken rails—that is, as long as the rail does not break under a passing train or is not bridged by a metal rail seat plate. If a rail is broken, the continuous circuit will be broken and the signals will change to a “stop” indication. Of 11 agencies that responded with the distribution of track circuits along their network, eight stated that they had track circuits on both rails, while three stated that they had track circuits on a single rail or multiple lines with different signaling on each (Figure 10). 0 1 2 3 4 5 0 100 200 300 400 500 600 700 Te st s pe r Ye ar System Size (Track Miles) Figure 6. Frequency of UT as function of system size. 0 1 2 3 4 5 0 100 200 300 400 500 600 700 800 Te st s pe r Ye ar System Size (Track Miles) Figure 7. Frequency of UT as function of system size (bubbles represent passenger miles in 2019).

Survey Results 15 Figure 8. Frequency of visual inspection. 0 2 4 6 8 10 12 1x per week 2x per week 3x per week N um be r of S ys te m s Frequency of Inspection Figure 9. Frequency of visual inspection as function of system size. 0 1 2 3 4 0 100 200 300 400 500 600 700 In sp ec tio ns p er W ee k System Size (Track Miles) Figure 10. Use of track circuits. 0 1 2 3 4 5 6 7 8 9 Primarily Double Rail Other (Single Rail or None) N um be r of S ys te m s

16 Maintenance Planning for Rail Asset Management—Current Practices Of the systems that reported conducting NDT other than UT or visual inspection, two had regular NDT programs, and both employed video inspection. WMATA has a pilot program whereby track is inspected by video annually. NYCT, a much larger system, conducts video testing three times per year. Several systems also reported using supplemental testing on a limited basis. Tren Urbano and the Massachusetts Bay Transportation Authority (MBTA) use NDT on every field weld performed. Metro Transit in Minneapolis uses NDT if a surface defect is found by visual inspection. The San Diego Metropolitan Transit System (SDMTS) uses optical rail measurement after rail grinding, which may in fact be rail profile inspection rather than rail defect inspection. 3.2.3 Constraints and Challenges Agencies were also surveyed on the constraints and challenges they face when testing. As might be expected for transit agencies that run trains every day from early in the morning until late at night, 11 of the agencies that replied stated that maintenance windows for testing were of concern. Six agencies reported issues with availability of equipment. Because all the agencies contract their UT services, those issues are likely scheduling problems with the contractor. One agency even reported availability issues for its own equipment; however, this was attrib- utable to the fact that the agency-owned equipment often requires maintenance, thereby restricting its testing availability. The ability to remove defects in a timely manner was not a challenge for almost all reporting agencies. In many systems, detected defects are removed as soon as is feasible. Figure 11 presents a visual representation of these challenges and constraints, and how many systems they affect. 3.2.4 Cost of Testing Cost of testing can likewise be a challenge for some agencies. Figure 12 presents the testing cost per mile for the 10 agencies that provided these data, ranked by system size from left to right. Figure 13 presents a plot of testing cost per mile as a function of system size. It can be observed that the highest testing cost per mile is for Tren Urbano in Puerto Rico. This cost might be explained by the system’s location separate from the contiguous United States, and the associated need for the testing contractor to travel to that location (Tren Urbano owns UT equipment but still contracts services), along with the system’s short overall length. With the exception of San Jose’s Santa Clara Valley Transportation Authority (VTA), testing costs 0 2 4 6 8 10 12 Maintenance Windows Availability of Equipment (from Contractor) Availability of Owned Equipment (Maintenance) N um be r o f S ys te m s Figure 11. Major challenges and constraints for testing.

Survey Results 17 then decrease continuously with increasing system size until those for the SDMTS, at which point they begin to climb again. The initial decrease followed by an increase may be attributable to the fact that (a) smaller systems pay higher up-front costs for their contractors to mobilize equipment, which drives up the cost per mile, and (b) larger systems have higher costs simply because of their size. 3.2.5 Emerging and Next-Generation Practices As discussed in the literature review portion of this report, risk-based scheduling has emerged as an effective way to schedule and manage ultrasonic testing of rail. Of the survey respondents, only six agencies stated that they had heard of risk-based scheduling (Figure 14). Of those six, two agencies (WMATA and NYCT) had adopted or were in the process of adopt- ing this type of scheduling. One agency stated that it had informally adopted risk-based $0 $200 $400 $600 $800 $1,000 $1,200 $1,400 $1,600 $1,800 $2,000 0 50 100 150 200 250 300 350 Te sti ng C os t ( pe r m ile ) Track Miles Figure 13. Testing cost per mile versus track miles. $0 $200 $400 $600 $800 $1,000 $1,200 $1,400 $1,600 $1,800 $2,000 Te sti ng C os t ( pe r m ile ) Figure 12. Testing cost per mile, ranked left to right from smallest to largest system (CATS = Charlotte Area Transit System; CTA = Chicago Transit Authority).

18 Maintenance Planning for Rail Asset Management—Current Practices scheduling. Another agency said that it did not consider risk-based scheduling to be effective given the number of defects the agency found. Still another agency stated that it was con- strained by state law. The sixth agency stated that it would be open to adopting risk-based scheduling. The remaining agencies had not heard of risk-based UT scheduling. 3.3 Defect Types and Detection Methods One of the most important metrics measured was the number of defects found by a transit agency for calendar years 2018 and 2019. The number of defects can vary greatly from system to system, and some trends are expected—for example, the larger the system, the higher the number of defects. In addition, not all rail transit systems have thorough and easily accessible defect and maintenance records. Several rail transit systems indicated that they are in the pro- cess of introducing more robust data management systems to reduce errors caused by poorer records. Going forward, it is important for rail transit systems to have quality defect history data, particularly as more sophisticated inspection scheduling techniques, such as risk-based UT scheduling, are introduced. To more accurately show trends and to prevent system size from skewing the data, a metric of defects per mile was calculated. This scaled the data to bring parity among the 16 responding transit systems, which ranged from small to very large. The number of defects per mile, plotted as a function of track miles, is displayed in Figure 15. As might be expected, larger and older systems tend to have more defects per mile, as they tend to have more traffic and longer time in service for their rails, which result in higher cumulative loading on their rails. Transit systems were also asked for the distribution of the defects found, by defect type. The defect types listed were detail fractures, vertical split heads, horizontal split heads, joint defects, weld defects, engine burn defects, and other defects. The distribution of defects, as a percentage of total defects in a particular transit system, is illustrated in Figure 16. The defects were then grouped into three categories: detail fracture and vertical and horizontal split head defects (collectively known as internal or fatigue defects); joint and weld defects; and engine burn defects. Also included with engine burns were other defects, among them those that require further verification before corrective action is taken, such as negative hand test (NHT) defects (where UT identifies a defect from a rail-bound unit but hand testing does not confirm 0 1 2 3 4 5 6 7 8 9 10 In Process of Adopting or Have Adopted Have Heard of It Have Not Heard of It N um be r o f S ys te m s Figure 14. Status of implementation of risk-based UT scheduling.

Survey Results 19 it as a defect); non-testable rail; and split web defects. This distribution, presented as a per- centage of total defects in Figure 17 and Figure 18, shows the relative density of defect types. In all three figures, the metro systems are ranked from left to right by size in track miles. Next, data were collected on the distribution of defects found by detection method. This information is summarized in Table 3. The bar chart in Figure 19 plots this information, and Figure 20 further groups the data by system size (smaller and larger than 80 track miles). These figures clearly show the average percentage of defects detected by UT and visual inspection for each system size. Figure 20 is interesting in that it shows the reliance of smaller systems on visual inspectors to locate defects. However, UT can examine the interior of the rail and thus can find defects 0 0.2 0.4 0.6 0.8 1 1.2 0 100 200 300 400 500 600 700 D ef ec ts /M ile Track Miles Defects per track mile 2019 Defects per track mile 2018 Figure 15. Defects per mile as function of track miles. Note: Larger systems also tend to be older systems. 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% Other Engine Burn Defects Weld Defects Joint Defects Horizontal Split Heads Vertical Split Heads Detail Fracture Figure 16. Distribution of defects, ranked from smallest to largest system by track miles (PAAC = Port Authority of Allegheny County; CTA = Chicago Transit Authority; SEPTA = Southeastern Pennsylvania Transportation Authority).

20 Maintenance Planning for Rail Asset Management—Current Practices that visual inspectors cannot. This capability suggests that more extensive use of UT might yield a higher proportion of defects found. Finally, Figure 21 presents the age of each system relative to the number of defects per track mile found in 2019. Note that the older systems, which also tend to be larger in size and to have more developed inspection techniques, find defects more effectively, hence the larger number of defects per mile. 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% Engine Burn, Other Joint and Weld Defects Detail Fracture, Vertical Split Head, Horizontal Split Head Figure 17. Distribution of defects by type (consolidated), ranked by system size in track miles (PAAC = Port Authority of Allegheny County; CTA = Chicago Transit Authority; SEPTA = Southeastern Pennsylvania Transportation Authority). 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% Detail Fracture, Vertical Split Head, Horizontal Split Head Joint and Weld Defects Engine Burn, Other Figure 18. Distribution of defects by type (alternate format), ranked by system size in track miles (PAAC = Port Authority of Allegheny County; CTA = Chicago Transit Authority; SEPTA = Southeastern Pennsylvania Transportation Authority).

Survey Results 21 Percentage of Defects Found Agency Name/Location Ultrasonic Testing Eddy Current or Other NDT Track Circuits Visual Inspection by Track Inspector Tren Urbano, Puerto Rico 100.0 0.0 0.0 0.0 Metro Transit, Minneapolis, MN 0.0 0.0 0.0 100.0 Central Puget Sound Regional Transit Authority, Seattle, WA 5.0 0.0 0.0 95.0 Port Authority of Allegheny County, Pittsburgh, PA 100.0 0.0 0.0 0.0 Valley Metro, Phoenix, AZ 5.0 0.0 90.0 5.0 Santa Clara Valley Transportation Authority, San Jose, CA 90.0 0.0 0.0 10.0 San Diego Metropolitan Transit System, San Diego, CA 100.0 0.0 0.0 0.0 Regional Transportation District, Denver, CO 96.0 0.0 4.0 0.0 Los Angeles County Metropolitan Transportation Authority, Los Angeles, CA 100.0 0.0 0.0 0.0 Chicago Transit Authority, Chicago, IL 88.0 0.0 6.3 9.2 Washington Metropolitan Area Transit Authority, Washington, DC 17.0 0.0 56.0 28.0 Southeastern Pennsylvania Transportation Authority, Philadelphia, PA 92.7 0.0 2.1 5.2 Massachusetts Bay Transportation Authority, Boston, MA 96.0 0.0 2.0 2.0 New York City Transit Authority, New York, NY 90.0 0.0 0.0 10.0 Table 3. Distribution of defects found, by method.

22 Maintenance Planning for Rail Asset Management—Current Practices 0 0.2 0.4 0.6 0.8 1 1.2 1880 1900 1920 1940 1960 1980 2000 2020 D ef ec ts P er T ra ck M ile Date of System Opening Figure 21. Defects per track mile as function of system age. 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% Visual Inspection by Track Inspector Track Circuits Eddy Current or Other NDT Ultrasonic Testing Figure 19. Distribution of defect detection methods by agency (PAAC = Port Authority of Allegheny County; CTA = Chicago Transit Authority; SEPTA = Southeastern Pennsylvania Transportation Authority). 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% <80 miles >80 miles Average Percentage of Defects Detected by UT Average Percentage of Defects Detected by Visual Inspection Figure 20. Distribution of defect detection by size of system.

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The occurrence of rail defects, broken rails, and broken rail derailments is consistent with the rate of development found in other studies that look at larger populations of rail defects. Likewise, the larger and more heavily used transit systems develop increased levels of defects, which is again consistent with what is seen in the railroad industry at large.

The TRB Transit Cooperative Research Program'sTCRP Synthesis 151: Maintenance Planning for Rail Asset Management—Current Practices presents the results of a survey and the analysis of the response data in an effort to synthesize current practices.

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