Quality Management of Pavement Condition Data Collection (2009) / Chapter Skim
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From page 30... ...
These are covered in detail in the following sections after a brief discussion on pavement condition data variability and quality control planning for controlling this variability. Quality Control and Variability The purpose of quality control is to quantify the variability in the process, maintain it within acceptable limits, identify the source of variability that can be controlled, and take the necessary production adjustments to minimize the "controllable" variability.
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For example, the Maryland State Highway Administration (MDSHA) is one of the agencies that reported having a formal quality control process for its in-house pavement data collection activities.
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The investigation showed that there were significant differences among raters, but no regional bias. The study also found that reducing the number of severity and density levels would help to reduce the variability in the pavement condition index.
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Distress For pavement distress, calibration is usually done by evaluating control sites where the pavement condition is closely monitored by a group of experts (70)
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Friction Properties Friction measuring equipment is also sent for calibration and/or certification at regional calibration centers. The regional calibration sites for the locked-wheel testers include the East Coast use of the Field Test and Evaluation Center for Eastern States in East Liberty, Ohio (81)
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For pavement distress data collection, the verification of the distress ratings can be done for individual distress quantities, individual distress indices, multiple distress indices, or overall condition indices. Quality management techniques that can be used to verify the data collection process include periodic retesting of "control" pavement segments, oversampling, and reanalyzing or resurveying a sample of the sections measured by an independent evaluator.
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The checks may include on-vehicle data checks, data and video checks when the data are received in the office, condition rating data checks, and/or final database checks after it has been entered into the relevant pavement/asset management databases. On-vehicle data checks are conducted in real time as the data are being collected and/or periodically (e.g., at the end of the day)
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Quality management techniques commonly used for this purpose include testing of control and verification sites, sampling and re-rating, complete database checks, GIS-based quality acceptance checks, and timehistory comparisons. Quality Acceptance Plan Figure 16 summarizes the percentage of agencies that indicated that they have a formal pavement condition data quality acceptance plan.
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. Control and Verification Site Testing Approximately three-fourths of the agencies use known or blind control sites as part of the quality acceptance procedure.
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Collected distress data are subject to a two-step data quality control and acceptance process in which the service provider first applies a series of internal quality control checks and the FHWA then conducts quality acceptance checks. The quality acceptance checks verify that the collected data are rated in accordance with the approved methodology for distress identification and distress severity ratings by manually rating selected pavement distress images provided by the service provider's automated crack detection system.
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. Use of Geographic Information Systems in Pavement Condition Data Quality Management One particular technique that is gaining acceptance is the use of GIS-based checks for quality acceptance.
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(96) presents some interesting approaches for comparing time-history pavement condition data.
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These tests validate that the data meet the established requirements before they are used to support pavement management decisions. Approximately half of the agencies that responded to this question have a formal pavement condition data quality acceptance plan.
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