Quality Management of Pavement Condition Data Collection (2009) / Chapter Skim
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PAVEMENT CONDITION DATA COLLECTION QUALITY MANAGEMENT SYSTEM Independently of the mechanism used to collect the data, inhouse or through a service provider, a complete pavement condition data quality management system would include a clearly documented quality management plan, detailed quality acceptance procedures, and established guidelines to monitor the entire process (as summarized in Figure 2)
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In particular, adequate quality and quantity of pavement condition data are a very important component in a PMS. For example, accurate and temporally consistent data are critical to develop models to predict smoothness and crack progression (38)
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Statistical/software routines that check for inconsistencies in the data Comparison with existing time-series data Verification of the post-survey processing software/ procedures Software routines that check for missing road segments or data elements Software routines that check if the data is within the expected ranges Periodic testing of known "control" segments during production Testing of known "control" segments before data collection Calibration of equipment and/or analysis criteria before the data collection Quality Acceptance Quality Control FIGURE 11 Percentage of state and provincial agencies using each quality control and acceptance activity.
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QUALITY ACCEPTANCE Quality acceptance activities are those that govern the acceptance of the pavement condition data; this is often referred to as quality assurance in the pavement data collection terminology. However, this latter term is not used in this synthesis because quality assurance in the quality management literature encompasses all the activities focusing on increasing the ability to fulfill requirements for the product of service being produced.
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. SOURCES OF VARIABILITY IN PAVEMENT CONDITION DATA COLLECTION One of the purposes of the quality control, and to some degree quality acceptance, processes is to reduce the variability in the pavement condition measurements.
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with some type of automatic data collection equipment, including rut bars with multiple sensors or continuous measurement systems. In general, the sources of variation include the following: • Type of equipment – Type of sensors -- Common sensors include point laser, ultrasonic, and continuous scanning lasers.
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From page 26... ...
Measurements over smaller segments tend to yield more useful results because short, rough areas are detected and might be unnoticed if the segments were larger. 26 – Wheel path measured -- Although some agencies report one of the wheel paths, others compute the average of both wheel paths.
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Network size also appears to play a minor role in the condition data quality management process. Agencies with larger networks (e.g., more than 25,000 lane-miles)
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smoothness as a percent of agencies collecting the pavement condition indicator. 28 25% 0% 29% 55% 58%0% 50% 35% 36% 17%75% 50% 35% 9% 25% 0% 20% 40% 60% 80% 100% Less than 5,000 miles Between 5,000 and 10,000 miles Between 25,000 and 50,000 miles Between 10,000 and 25,000 miles More than 50,000 miles No No; but under development Yes FIGURE 12 Percentage of agencies having a quality management plan as a function of network size.
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Effective data collection quality management programs provide a comprehensive, systematic approach to data collection and processing. It is important that a complete pavement condition data quality management system include a clearly documented quality control plan, detailed quality acceptance procedures, and established guidelines to monitor the entire process, with timelines, milestones, and evaluation criteria.
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