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Page 73
Suggested Citation:"Chapter 5 - Summary of Findings." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Data Collection and Quality Management for Pavement Condition Reporting. Washington, DC: The National Academies Press. doi: 10.17226/26717.
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Suggested Citation:"Chapter 5 - Summary of Findings." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Data Collection and Quality Management for Pavement Condition Reporting. Washington, DC: The National Academies Press. doi: 10.17226/26717.
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Suggested Citation:"Chapter 5 - Summary of Findings." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Data Collection and Quality Management for Pavement Condition Reporting. Washington, DC: The National Academies Press. doi: 10.17226/26717.
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Suggested Citation:"Chapter 5 - Summary of Findings." National Academies of Sciences, Engineering, and Medicine. 2022. Automated Data Collection and Quality Management for Pavement Condition Reporting. Washington, DC: The National Academies Press. doi: 10.17226/26717.
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73   Assessment of pavement condition has evolved over the past 30 to 40 years, from manual methods that use data collection crews walking or driving slowly on the shoulder to vehicles traveling at posted highway speeds capturing roadway profile and surface images for quantify- ing distress. While the transition to APCS has improved the safety and efficiency of data collec- tion, it has also resulted in challenges that include keeping pace with rapidly advancing digital image technology, data quality concerns, year-to-year consistency, and modifications to existing pavement management components (e.g., performance measures, prediction models, treatment decision trees). Transitioning to an APCS assists agencies with meeting PM2 requirements for reporting pavement condition at 0.10-mile increments on the NHS. The majority of SHAs (34 agencies) had already transitioned to an APCS prior to the PM2 legislation; however, nearly all responding agencies (42 agencies) were required to make modifications to the pavement condition survey process and to pavement management activities to meet the legislative reporting requirements. The objectives of this synthesis project are to document the experiences, challenges, and state- of-the-practice solutions used by SHAs that are in the midst of transitioning, or have already transitioned, to automated/semiautomated processes for collecting pavement data and to sum- marize the data for state and federal requirements for reporting pavement condition. This synthesis is based on the results of a literature review, a survey of SHAs (48 respond- ing agencies), and follow-up questions with 14 agencies that indicated a willingness to support development of the synthesis case examples. Overall Findings On the basis of the literature review, advancements in technologies for assessing pavement condition have allowed agencies to move away from manual and toward fully automated pave- ment condition surveys. The majority of SHAs have transitioned to semi- or fully automated pavement condition surveys or a combination of methods. Agencies have faced a number of challenges in transitioning from manual to automated surveys. These include a lack of method standardization, information on how agencies successfully transitioned to an APCS, compatibil- ity with historical records, assessing the impact on performance prediction models, the cost and time of conducting the APCS, and data quality concerns. Transitioning to an APCS has assisted SHAs in collecting, analyzing, and submitting pavement condition measures for PM2, HPMS, and GASB-34 and supported agency activities (e.g., TAMP, budgeting, pavement design). For PM2, SHAs report IRI, rutting, faulting, and percent cracking or PSR for highways with posted speed limits of less than 40 mph; this reporting is done annually for the Interstate highway system and every 2 years for the non-Interstate NHS. For HPMS, SHAs report the same pavement condition C H A P T E R   5 Summary of Findings

74 Automated Data Collection and Quality Management for Pavement Condition Reporting measures along with other reporting requirements. For GASB-34, SHAs report general infra- structure assets along with depreciation or preservation cost. A review of SHA DQMPs was conducted, and the condition types assessed and the data quality control and acceptance criteria were summarized. In general, all agency DQMPs included criteria for IRI, rutting, faulting, and cracking. Equipment calibration and certification criteria include resolution, accuracy, and repeatability requirements for each quantified condition. Common criteria for equipment calibration and certification for each condition (resolution, accuracy, and repeatability, respectively) are as follows: • IRI: 1 inch/mile, ±5% reference value, ±5%; • Rutting: 0.01 inch, ±0.06 inch reference value, ±0.06 inch; • Faulting: 0.01 inch, ±0.06 inch reference value, ±0.06 inch; and • Cracking: varies, ±20% reference value, na. Similarly, for data quality and acceptance, the most common criteria include, respectively: • IRI: SD ≤ 5%; 95% compliance with verification testing; • Rutting: ±0.04 inch; 95% compliance with verification testing; • Faulting: 95% compliant with standards; 95% compliance with verification testing; and • Cracking: SD < 15% of multiple runs or historical values, or both; 95% compliance with veri- fication testing. To capture current state of the practice, a web-based questionnaire was developed to quantify agency methods of conducting pavement condition surveys and agency and national require- ments for reporting pavement condition. In total, 48 agencies responded to the survey. The majority of agencies indicated using fully automated (13 agencies), semiautomated (6 agencies), or a combination of manual, semi-, and fully automated methods (27 agencies) for quantify- ing pavement condition. The majority of agencies also indicated having more than 10 years of experience with an APCS (24 agencies). Noted APCS challenges included assessing, validat- ing, and verifying data quality (24 agencies); obtaining consistent results (22 agencies); modify- ing distress definitions (19 agencies); addressing significant differences between manual and APCS results (19 agencies); and modifying index and rating calculations (15 agencies). Noted APCS advantages included compatibility with national reporting requirements (36 agencies), increased rater safety (31 agencies), and efficient data collection (30 agencies). Agencies also indicated several APCS disadvantages, including challenges with direct correlation with histo- rical data (18 agencies), increased cost of data collection (17 agencies), and year-to-year variability of results (14 agencies). The cost of conducting a pavement condition survey varies by the number of lane miles evalu- ated, the survey method (e.g., manual versus semiautomated versus fully automated, vendor versus agency data collection and analysis), and the number of identified distress types. Agency- provided information indicated an average weighted pavement condition survey cost (regard- less of survey method) to be approximately $62 per lane mile (40 agencies), $104 per lane mile for manual surveys (2 agencies), $72 per lane mile for semiautomated surveys (6 agencies), $69 per lane mile for fully automated surveys (9 agencies), $67 per lane mile for a combination of manual and fully automated surveys (6 agencies), $56 per lane mile for a combination of semi- and fully automated surveys (14 agencies), and $51 per lane mile for a combination of manual and semiautomated surveys (3 agencies). In addition, on average, agencies store the results of the pavement condition survey on data storage drives (34 agencies) that require approximately 10 terabytes of storage for every 10,000 lane miles collected. Data quality control and acceptance requirements are maintained by all responding SHAs per PM2 requirements. However, the sample size for data acceptance varies by SHA. Of the

Summary of Findings 75   responding agencies, 14 use a sample size of less than 5% of the total annual lane miles, 10 use a sample size of 5% to 15%, 4 use a sample size of 15% to 25%, 1 uses a sample size of 50% to 75%, and 12 use a sample size of 75% to 100%. The responding agencies indicated experiencing challenges in PM2 reporting related to establishing pavement performance targets (22 agencies), determining baseline conditions (15 agencies), and adjusting targets (12 agencies). HPMS has been in place for a number of years; therefore, the majority of agencies indicated no challenges in providing the needed infor- mation (24 agencies). Case examples were provided to describe the challenges, modifications, and benefits agencies experienced when transitioning to an APCS in greater detail. A number of agencies noted the need to revise distress definitions (13 agencies) and the process for calculating percent crack- ing on asphalt pavements specific to alligator and longitudinal cracking within the wheel path (6 agencies). Consistency and repeatability of crack identification have improved; however, 2 agencies noted challenges in identifying raveling on asphalt pavements. While the results of the APCS are more consistent and data quality validation and verification is less subjective than in manual surveys, 6 agencies noted a significant increase in the time needed to conduct data quality management activities. Three agencies reported implementing or developing automated processes to assist in conducting data quality activities. Five agencies noted the need to adjust decision trees to account for the higher occurrence of low-severity cracking with an APCS, and five also noted challenges with establishing PM2 performance prediction targets with limited years of data based on the HPMS performance measures. Finally, in addition to national reporting requirements, the majority of agencies provide results from pavement condition survey to upper management (12 agencies), district and main- tenance offices (11 agencies), the asset management office (9 agencies), and the budget office (8 agencies). Areas for Future Research Following are areas for research related to APCS and requirements for reporting pavement condition. • Improve methods for identifying surface irregularities. Current APCS algorithms and AASHTO standards are improving the process for quantifying surface cracking. However, identification of distress types related to surface inconsistencies, such as raveling, weather- ing, and spalling, are still difficult to assess with APCS methods. An area of future research is to identify efficient and effective methods for quantifying, validating, and verifying surface texture measurements for non-cracking-based distress types and to develop appropriate standards. • Improve methods for quantifying JPCP faulting. Determining joint faulting on JPCP is dependent on the ability to identify transverse joint locations, distinguish between trans- verse cracks and transverse joints, and consider the impact of variable length joint spacing. An area of future research is to improve the accuracy of current methods or to develop new methods for processing and analyzing profile measurements to determine joint faulting accurately. • Develop universal APCS data format. The data format published by AASHTO in 2021, MP 47, Standard Specification for File Format of Two-Dimensional and Three-Dimensional (2D/3D) Pavement Image Data (AASHTO 2021c), is expected to be refined by the AASHTO Committee on Materials and Pavements. An area of future research is to develop a framework

76 Automated Data Collection and Quality Management for Pavement Condition Reporting to assist agencies in using the refined standard, showcase agencies that have successfully adopted the standard, and quantify any needed refinements. • Conduct summary of local agency and MPO efforts in transitioning to automated pave- ment condition surveys. The focus of this synthesis was on the transition of SHAs in imple- menting APCSs. However, the transition efforts and challenges for local agencies and MPOs implementing the use of automated surveys can be different from those experienced by SHAs. Development of a synthesis of local agency and MPO practices for transitioning to APCS is another area for possible future research.

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Automated collection of pavement data allows agencies to collect data on pavement health, including cracking, rutting, faulting, and roughness, at highway speeds. This provides important information for better pavement decision-making.

The TRB National Cooperative Highway Research Program's NCHRP Synthesis 589: Automated Data Collection and Quality Management for Pavement Condition Reporting documents the experiences, challenges, and state-of-the-practice solutions used by state departments of transportation that are in the midst of transition or that have transitioned to automated and semiautomated processes for collecting pavement data. It also summarizes the data for state and federal reporting requirements, such as Transportation Asset Management Plans and MAP-21.

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