National Academies Press: OpenBook

Pavement Management Systems: Putting Data to Work (2017)

Chapter: Chapter Four - Case Examples

« Previous: Chapter Three - State of the Practice
Page 45
Suggested Citation:"Chapter Four - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2017. Pavement Management Systems: Putting Data to Work. Washington, DC: The National Academies Press. doi: 10.17226/24682.
×
Page 45
Page 46
Suggested Citation:"Chapter Four - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2017. Pavement Management Systems: Putting Data to Work. Washington, DC: The National Academies Press. doi: 10.17226/24682.
×
Page 46
Page 47
Suggested Citation:"Chapter Four - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2017. Pavement Management Systems: Putting Data to Work. Washington, DC: The National Academies Press. doi: 10.17226/24682.
×
Page 47
Page 48
Suggested Citation:"Chapter Four - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2017. Pavement Management Systems: Putting Data to Work. Washington, DC: The National Academies Press. doi: 10.17226/24682.
×
Page 48
Page 49
Suggested Citation:"Chapter Four - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2017. Pavement Management Systems: Putting Data to Work. Washington, DC: The National Academies Press. doi: 10.17226/24682.
×
Page 49
Page 50
Suggested Citation:"Chapter Four - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2017. Pavement Management Systems: Putting Data to Work. Washington, DC: The National Academies Press. doi: 10.17226/24682.
×
Page 50
Page 51
Suggested Citation:"Chapter Four - Case Examples." National Academies of Sciences, Engineering, and Medicine. 2017. Pavement Management Systems: Putting Data to Work. Washington, DC: The National Academies Press. doi: 10.17226/24682.
×
Page 51

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

46 ApproAch Based on the analysis of the survey responses, several intriguing uses of pavement management data were identified and believed to warrant further examination. Each of these uses is featured as a case example to provide the opportunity to explore the reasons for their application and how they were implemented. Telephone interviews were performed with highway agency representatives to cap- ture the information associated with each application. However, the case examples are not intended to provide a comprehensive summary of the practices in any of the DOTs that participated in the interviews, but rather serve to highlight the analysis objectives, findings, and overall lessons learned. The five case examples described in this chapter highlight the use of pavement management data to: • Improve data quality (Kansas DOT). • Evaluate treatment effectiveness (North Carolina DOT). • Conduct a safety analysis (Maryland DOT). • Improve agency performance measures (Washington State DOT). • Establish performance measures for highway concession agreements (Texas DOT). cAse exAmple 1: Using pAvement mAnAgement DAtA to improve DAtA QUAlity Having spent many years contributing to the development of standards for pavement condition data collection through AASHTO and FHWA, the pavement management engineer from Kansas DOT decided to adopt the existing standards in his agency to learn more about the challenges that agen- cies face as they implement these processes and bring those experiences to the FHWA Pooled Fund working on refining the standards. Potential advantages to using the AASHTO standards on a national basis are listed here: • Automated data collection activities could be more consistent and less expensive if vendors did not have to modify procedures for each agency. • Pavement performance models could be transferable among agencies within a particular region. • The data could be more transferable to support other asset management activities. • From a national perspective, the information that is being reported would have more meaning because it is more consistent. To evaluate the issues associated with the implementation of the AASHTO standards, Kansas DOT negotiated a 3-year contract with a vendor who was to follow the standards closely. Throughout the contract period, the intent was to use the findings to guide the committee working on finalizing the standards, including recommendations for changes based on the state implementation experience. However, a number of issues emerged that are providing lessons much earlier than expected. From those experiences and discussions with other entities trying to use the standards it became evident that implementers make assumptions during the processing of the data that are not well documented and are not made public. Although outputs are provided in the format specified in the standards, each vendor could be making different assumptions to compile the data and produce results. For instance, typical distress data collection equipment records relative elevations and reflective intensities with a resolution between 1 and 3 mm. During processing, these individual measurement locations must be chapter four cAse exAmples

47 combined to form a continuous surface profile. From this surface profile, implementers must deter- mine if each point is part of a crack and how each point might be connected to other cracked points in determining cracking distress. The assumptions made in determining cracked points and turning those into cracking distress outputs are not well known and are typically considered to be proprietary by the vendors. Many agencies may consider that they are following the standards because their outputs are reported in a format that complies with the standards. However, these agencies may not be aware that assumptions are being made during the processing of the data that do not necessarily comply with the AASHTO standards. The lessons learned from this experience included the following: • To facilitate the use of data collection standards, it would help if they are written in a way that they will be followed by DOTs and meet both FHWA’s and state DOT’s needs. • It would be beneficial to have data collection vendors provide a flowchart showing how outputs are developed, including how cracks are identified and evaluated, as well as the development of a crack map that defines choices and hierarchies used to link cracks, so assumptions can be better understood. • The team developing the national standards would benefit from participation by users of the data as well as individuals who understand the capabilities of the technology. • A national research study to develop models that take the raw data collected by the vendors and process it in a consistent manner on a national basis would benefit the pavement management community. The results of such a study would allow comparisons of state data and improve the accuracy of the HPMS data. Kansas DOT will continue to address these lessons learned to help them meet their internal needs for quality pavement condition information and to help FHWA meet its requirements for reporting to Congress without conflicting with the data that states use for pavement management decisions. cAse exAmple 2: Using pAvement mAnAgement DAtA to evAlUAte treAtment effectiveness Following an extended process of updating the North Carolina DOT’s life-cycle cost (LCC) proce- dure, its pavement management engineer decided to use pavement management data to ascertain if the assumptions used in the LCC models could be supported. Concurrently, the North Carolina DOT pavement management engineer was being asked questions about how long friction courses were lasting in the state; therefore, the combination of these two events prompted an analysis of the per- formance of two different types of friction courses used in the state: an open-graded friction course (OGFC) and a surface with an FC-2 gradation. The pavement management database provided several types of information that were key to the analysis including inventory data, type of friction course, date of construction, and pavement con- dition data. The performance data were plotted against the survey year for each pavement section that had one of the two friction courses applied. The data were also combined and in some cases, as shown in Figure 42, there was a significant amount of scatter in the combined data. The data were then modeled to show the average Pavement Condition Rating for all sections that were constructed in any given year, as shown in Figure 43. A similar analysis was conducted for the pavement sections with an FC-2 gradation. In both cases, the results provided good guidance regard- ing the performance period that can be expected for these types of treatments. For instance, OGFC performance historically drops off at year 10 and FC-2 performance in year 8. The study also found that all FC-2 sections had received another treatment by year 11. The information has been used to support decisions being made by field divisions as they program rehabilitation activities and has led to greater confidence in the pavement management system. The results can also be used in a tradi- tional LCC analysis to support the assumptions that are made for treatment frequencies. One of the challenges encountered during the analysis concerned the variability of some of the pavement performance data. Evaluating the effectiveness of a treatment requires condition data over

48 a full performance cycle to learn about the life of a treatment. Over the 15 years that OGFC have been applied and the 13 years that FC-2 treatments have been used, performance data were collected using two different survey approaches: manual and automated. The manual data had more variability than the automated survey, which may have influenced some of the results. However, the performance results that were generated from the analysis replicate field observations; therefore, the influence is not perceived to be too great. North Carolina DOT analysis led to the following lessons learned: • It is helpful to eliminate complications in the analysis by selecting pavement sections that have received the treatment after any major change in design or rating procedures. FIGURE 43 Average OGFC pavement condition since time of construction (provided by NCDOT). FIGURE 42 Sample plot showing OGFC performance for all sections (provided by the NCDOT).

49 • The analysis results have to be updated if materials and other conditions in the field change. • Reliable construction history records are important to the analysis. In North Carolina, the DOT concentrated its analysis on Interstate and primary routes where construction histories are kept current. cAse exAmple 3: Using pAvement mAnAgement DAtA to conDUct A sAfety AnAlysis The Strategic Highway Safety Plan developed by Maryland DOT includes a focus on improvements to highway infrastructure that would lead to a reduced number of crashes in and around curves. In a related but separate effort, the Maryland Office of Materials Technology, where pavement manage- ment is located, was evaluating friction management policies. When the project manager from the Office of Highway Development contacted the Assistant Division Chief for Pavement Management from the Office of Materials Technology to determine whether pavement management data could be used to assist with identifying high-risk curve locations, the two recognized an opportunity to col- laborate to address the goals of both initiatives. Starting with data from one county, the team began using the geometric information collected during the pavement condition surveys to study how it could be leveraged. The study considered hori- zontal curve, radius, cross slope, and design measures and compared the data to a separate database containing speed limit information. The data were analyzed in an ArcGIS environment, combining known crash locations by frequency and severity, design speed, and curve radius, along with pave- ment age and existing surface friction (using an A to F rating) for the state route data. The curve radius, pavement age, and surface friction ratings were all provided from the pavement management system. Only pavements 10 years old or less were considered as candidates for high-friction surface treatment. To analyze the data, the team produced histograms for each individual route (such as the one shown in Figure 44) to identify high-crash locations at various speeds. The analysis was used to iden- tify high-risk curve candidates to be considered for some type of action, including better signage, the application of a surface friction treatment, or modifications to the cross slope. The same type of analysis was used recently when the state legislature proposed raising the speed limit from 65 to 70 mph on Interstate highways. The analysis identified areas where the higher speeds were not recommended unless some type of improvement was made to reduce the possibility of crashes. Maryland DOT recognizes the potential of this type of analysis for two primary purposes. First, it can be used to determine where speed limits might be too high for given conditions and, second, it FIGURE 44 Sample histogram showing crash locations along a route in Maryland (provided by the Maryland DOT).

50 provides the information needed to identify locations where some type of countermeasure could be used to address a high-risk location. There were several challenges associated with performing these types of analysis, including the following: • The precision and availability of crash reports can be somewhat dependent on the police officer at the crash scene. In Maryland, reports prior to 2014 depended on a linear referencing system (e.g., mile markers) so the crash location data may not be reliable. Since that time, crash report- ing has been done with a global positioning system; however, there are still some officers who do not collect this information at the time of the accident. • The friction trucks used by Maryland DOT did not have global positioning system capabilities; therefore, the locations were based on a route mile referencing system. • The ArcGIS maps include shape files in one direction. However, curve data and pavement condition data are collected in both directions. As a result, one direction may not have shape files available. The work involved the coordination of four different offices within the DOT: the Office of Materi- als Technology, which provided the pavement management data; Office of Highway Development, where the GIS analysis was conducted; Office of Planning and Preliminary Engineering, which maintains the road inventory; and Office of Traffic and Safety, which provided the crash data and the safety-related key performance indicators. cAse exAmple 4: Using pAvement mAnAgement DAtA to improve Agency performAnce meAsUres According to the State Pavement Management Engineer at WSDOT, using pavement condition per- formance measures exclusively does not provide an adequate indication of whether available funds have been effectively used. As a result, the DOT had difficulty conveying to the state legislature why certain investment decisions were being made. To address this deficiency, the state pavement engineer decided to investigate whether cost information could be used to improve the way pave- ment investment decisions are conveyed to the legislature and state agency executives. A study was initiated to investigate the use of several different measures (RSL, an Asset Sustainability Index, and a Deferred Preservation Liability), but found that each of these metrics required significant explana- tion. These measures, which are related to network-level performance, are not reported annually in WSDOT’s Gray Notebook. Ultimately, the analysis determined that the most effective approach for project-level evaluation was to calculate an “actual life cycle cost” for each pavement section using construction, mainte- nance, and preservation costs over a single performance period. The results could be combined with observed pavement performance data to evaluate how cost-effective the treatment has been. As shown in Figure 45, the lowest annual cost can be found where the total annual costs from mainte- nance and preservation costs and pavement rehabilitation costs are minimized. This point represents the ideal time to apply rehabilitation because additional maintenance and preservation treatments are no longer proving to be cost-effective. WSDOT found that even a 1-year difference in the timing of rehabilitation can make a substantial difference in total costs, increasing the cost of a chip seal project by 14% to 20% and the cost of an asphalt concrete resurfacing project by 4% to 8%. To conduct the analysis, the actual Equivalent Uniform Annual Cost was determined and expressed in terms of the dollar spent per lane mile per year ($/lane mile/year). By incorporating traffic into the metric (i.e., $/lane mile/per truck/per year), it can be used to prioritize capital project recommenda- tions at the central office to help ensure that resurfacing investments are being made on the right roads at the right time. One of the advantages to this approach is that it treats all regions equally in the prioritization process because higher treatment costs on the western side of the state where most of the traffic occurs are balanced out by the lower treatment costs and lower traffic volumes on the eastern side of the state.

51 One of the challenges that the agency faced in conducting the analysis was the difficulty in separat- ing the pavement-related costs from other costs on projects where multiple issues were being addressed at the same time. This is primarily because contract cost data stored in financial cost tracking systems are typically developed for contract administration rather than post-contract evaluation. WSDOT could not develop a way to automate the process of separating costs for different types of work; therefore, complicated, manual processes had to be implemented and rules were established so that the same que- ries could be used in the future. The calculated performance measures are now stored in the pavement management system so that pavement system costs can be tracked by location and time. There were several factors that contributed to the success of this application, reflecting sound decisions made by the agency more than 30 years earlier. These included: • WSDOT had reliable construction cost data in a database dating back to the early 1990s. • For at least 30 years, the DOT has had a consistent location referencing system so that all data- bases were based on a robust and common referencing system. • The project evaluation process is a centralized activity, which enables the agency to implement these types of prioritization rules. Future activities will involve using the new performance metric to set performance targets on a regional and statewide basis to further improve transparency and trust in the DOT. cAse exAmple 5: Using pAvement mAnAgement DAtA to estAblish performAnce meAsUres for highwAy concession Agreements Texas DOT (TxDOT) uses comprehensive development agreements (CDAs) for its public-private partnership agreements. There are two types of CDAs currently being used; one for design-build con- tracts and another for concessions. A design-build contract includes property acquisition, design, and construction that occur under a single contract, but does not include financial participation by the pri- vate entity, nor does it include any provisions for the on-going use of the facility. A concession agree- ment addresses the ongoing maintenance of a facility and can include private-sector responsibilities for development, financing, operation, and maintenance of the facility for up to 52 years. In exchange, FIGURE 45 Graphic showing annualized costs associated with different life cycle strategies (provided by Washington State DOT).

52 the developer receives an on-going revenue stream, usually in the form of tolls collected from the users of the facility. In some cases, TxDOT is paid a fee upfront, which can be used to fund other projects. During the conduct of a concession agreement, a contractor is held responsible for providing a level of service that is agreed upon in the contract. Therefore, a DOT entering into this type of con- tract must establish reasonable performance measures that can be used for this purpose. The perfor- mance measures also serve as the basis for the bid price that a contractor charges for the concession agreement. If the performance measures are set too high, the contract price will be expensive. A lower set of performance measures typically results in a lower contract cost, but could also lead to customer complaints and a lack of public interest in using the facility because of its poor condition. TxDOT turned to pavement management to help set pavement performance measures that could be used in a Request for Proposals and in the contracting process when these new CDAs were established. The pavement management engineer considered IRI, rutting, and cracking data as performance measures for its asphalt pavements. An analysis of pavement management data was conducted to determine the conditions that were met by 95% of the equivalent highway network. The 95% level was selected because it was equivalent to two standard deviations from the mean and was relatively easy to determine. For IRI, this meant that the asphalt-surfaced performance measure would be set at a limit of 120 in./mi and fatigue cracking was limited to 10% of the area. Once the contract was in place, the contractor assumed responsibility for evaluating highway conditions each year and submitting a report to the DOT. The surveys must be conducted using a TxDOT-certified profiler and a TxDOT-certified rater for the windshield distress survey. An inde- pendent engineer reviews the submittals and notifies the agency if any discrepancies are found in what was reported. Because these types of contracts have not been in place for long, it is difficult at this time to know whether the performance measures will be adequate over the life of the agreement. However, there have already been some lessons learned regarding the use of the pavement management data for this purpose. For example, pavement management condition surveys are currently collected on ½-mile sections. At some point when the contract was being developed, the DOT set the performance measure interval at a 0.1-mile interval instead. This was discovered when the pavement management results did not agree with information submitted by the contractor. The differences in the reporting interval were found to have a significant impact on the resulting values, implying that the pavement manage- ment data could not be used in its typical format to monitor the performance of the concessioner. The information collected by the contractor is not incorporated into the pavement management database because it is not submitted in an electronic format. This is one change that the pavement management engineer would prefer to make so the highways can be tracked as part of the total high- way network. Other lessons learned from TxDOT’s experiences include the following: • Interfacing the pavement management system with other systems, including the maintenance man- agement system and the project management system, helps facilitate the analysis. These interfaces are important when evaluating data and ensuring that pavement management recommendations are being followed. Otherwise, it is difficult to ensure that statewide goals can be achieved. • Reliable work history information is important to improve the accuracy of the analysis. TxDOT has been working on improving this aspect of its pavement management system by adding pave- ment layer information (e.g., surface, base, and subgrade) to the database. Maintenance work history is in a program that interfaces with the pavement management system so that information is readily available. • It is beneficial if the concessioner reports performance data using the same segmentation that is being used in pavement management for consistency purposes. • Performance trends change with time; therefore, it is helpful to review performance models at least every 5 years to ensure they continue to reflect actual performance trends. • Documenting the process used to establish the performance measures allows the same process to be used in the future.

Next: Chapter Five - Conclusions »
Pavement Management Systems: Putting Data to Work Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

TRB's National Cooperative Highway Research Program (NCHRP) Synthesis 501: Pavement Management Systems: Putting Data to Work documents current pavement management practices in state and provincial transportation agencies. The report focuses on the use of pavement management analysis results for resource allocation, determining treatment cost-effectiveness, program development, and communication with stakeholders.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!