Automated Data Collection and Quality Management for Pavement Condition Reporting (2022) / Chapter Skim
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Pages 5-34
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5 This chapter presents the findings of the literature review relevant to transitioning from manual to automated pavement condition surveys, agency DQMPs, and national reporting requirements for pavement condition. Assessment of Pavement Condition There are essentially two broad categories for assessing pavement condition: manual and automated surveys.
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6 Automated Data Collection and Quality Management for Pavement Condition Reporting • ASTM E1489, Standard Practice for Computing Ride Number of Roads from Longitudinal Profile Measurements Made by an Inertial Profile Measuring Device (2019) , • ASTM E1656, Standard Guide for Classification of Automated Pavement Condition Survey Equipment (2016)
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Literature Review 7 Transitioning from Manual to Automated Pavement Condition Surveys Timm and McQueen (2004) conducted an agency survey to determine the methods used by other SHAs for conducting pavement condition surveys.
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8 Automated Data Collection and Quality Management for Pavement Condition Reporting funding. The network PSC from the 1998 windshield survey and the 1999 APCS agreed reasonably well, and in particular for PSC values of less than 60 (the threshold for triggering rehabilitation)
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Literature Review 9 Vavrik et al.
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10 Automated Data Collection and Quality Management for Pavement Condition Reporting types, except for pumping, scaling, and shrinkage cracks. For CRCP, semi- and fully automated survey methods were predominantly used to assess distress type, severity, and extent.
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Literature Review 11 Measure Fully Automated Semiautomated Manual Total No. of Responses 91 IRI 0 0 19 Cross slope 9 0 0 9 Longitudinal cracking 8 7 2 17 Transverse cracking 6 6 1 13 Texture 6 0 1 7 5 tuohcnuP 8 1 14 Lane/shoulder 8 2 1 5 ffo-pord Spalling 3 4 1 8 3 gnihctaP 7 2 12 Durability 3 3 2 8 Scaling 1 1 1 3 Map cracking 1 3 0 4 Polished aggregate 0 2 1 3 Blowups 0 4 2 6 Source: Pierce and Weitzel (2019)
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12 Automated Data Collection and Quality Management for Pavement Condition Reporting National Performance Management Measures Enacted in 2018, PM2 established a performance- and outcome-based program that requires SHAs and metropolitan planning organizations (MPOs) to prepare and use federally established performance measures.
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Literature Review 13 cracking. For CRCP, the performance measures are IRI and percent cracking.
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14 Automated Data Collection and Quality Management for Pavement Condition Reporting Reporting Requirements In accordance with 23 CFR § 490.319, each SHA is required to report to FHWA, no later than April 15 of each year, the information necessary to calculate the Interstate system condition measures and, no later than June 15 of each year, the information on the non-Interstate NHS condition measures. In addition, MPOs are to report targets to their respective SHAs and to report baseline pavement condition and progress toward achieving the targets.
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Literature Review 15 percentage of pavements in good and poor condition on the Interstate system and the nonInterstate NHS. Pavement condition is quantified according to the following measures: • Average IRI: Average IRI is required for all pavement types.
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16 Automated Data Collection and Quality Management for Pavement Condition Reporting Figure 7. JPCP faulting measurements (FHWA 2018b)
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Literature Review 17 Figure 9. JPCP: percent slabs with transverse cracking (adapted from FHWA 2018b)
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18 Automated Data Collection and Quality Management for Pavement Condition Reporting Figure 12. CRCP percent cracking: patching (FHWA 2018b)
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Literature Review 19 The FHWA process for determining PSR and PCI from limited distress types is based on procedures developed by Mok and Smith (1997) and the San Francisco Bay Area Metropolitan Transportation Commission (MTC)
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20 Automated Data Collection and Quality Management for Pavement Condition Reporting In the Mok and Smith (1997) procedure, a correlation between PSR and PCI was developed on the basis of the comparison of windshield survey results to determine PSR for pavement segments with PCI results based on the MTC method (e.g., ASTM D6433 with limited distress types)
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Literature Review 21 – Fund financial statements, which include governmental funds, proprietary funds, and fiduciary funds. • Required supplementary information, which includes budgetary comparison schedules as well as any other information required in previous GASB statements.
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22 Automated Data Collection and Quality Management for Pavement Condition Reporting • Data sampling, review, and checking processes; and • Error resolution procedures and data acceptance criteria. DQMPs for the SHAs, including Puerto Rico and the District of Columbia, were reviewed, and information regarding agency practices for assessing pavement condition was summarized in relation to • Types of pavement condition data collected, • Requirements for equipment calibration and certification, • Data quality control criteria, and • Data acceptance criteria.
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Literature Review 23 DC DE FL GA HI IA ID IL IN KS LA MA MD ME MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA PR RI SC SD TN TX UT VA VT WA WI WV WY Total 50 50 33 34 9 18 7 3 33 8 26 10 17 6 35 Note: Crack. = cracking; Allig.
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24 Automated Data Collection and Quality Management for Pavement Condition Reporting State IRI Fault Percent Crack. Blowups/ Repair Corner Break Cracked Slabs Multicracked Slabs DCrack.
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Literature Review 25 and cracking. It should be noted the information contained in these tables does not necessarily represent all data quality elements required by each SHA.
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26 Automated Data Collection and Quality Management for Pavement Condition Reporting Agencya Resolution Accuracy (to reference value) Repeatability AK (2018)
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Literature Review 27 Statea Resolution Accuracy (to reference value) Repeatability AK (2018)
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28 Automated Data Collection and Quality Management for Pavement Condition Reporting Statea Resolution Accuracy (to reference value) Repeatability AK (2018)
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Literature Review 29 Statea Resolution Accuracy (to reference value) Repeatability AK (2018)
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30 Automated Data Collection and Quality Management for Pavement Condition Reporting Statea IRI Rutting AK (2018) SD < 5% 10 runs SD < 0.04 in.
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Literature Review 31 Statea Faulting Cracking AK (2018) na SD < 15% 10 runs AL (2018)
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32 Automated Data Collection and Quality Management for Pavement Condition Reporting Statea IRI Rutting AK (2018) 95% verification testing 95% verification testing AL (2018)
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Literature Review 33 Statea Faulting Cracking AK (2018) na 95% verification testing AL (2018)
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34 Automated Data Collection and Quality Management for Pavement Condition Reporting Summary of Chapter 2 Over the past several decades, SHAs have been transitioning from manual to semiautomated to fully automated pavement condition surveys. This transition was originally initiated through collection of transverse and longitudinal profiles for determining IRI, rut depth, and faulting.
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