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53 Equipment calibration and inspections procedures, in conjunction with control site testing to establish Equipment and/or manual process verification pro- acceptable ranges for various data collection techniques. cedures (e.g., testing of known control section) before 9. Software Checks: Many agencies and all service starting production testing, providers use software routines that check the data for Production quality verification procedures (e.g., test- inconsistencies for both quality control and quality ing of known or blind control sections during produc- assurance. Although there is some variation in verifi- tion testing), and cation methods, most software can perform checks for Checks for data reasonableness and completeness. detecting missing segments, corrupted records, and 5. Quality Acceptance includes the activities that gov- ratings that are out of expected ranges. Some packages ern the acceptance of the pavement condition data and can also provide statistical analysis to check for data ensure that the final product is in compliance with the inconsistencies, compare condition time-series, and/or specifications. It applies to the pavement condition graphically display the results using geographic infor- data collected by the agency and by service providers. mation systems. Approximate half of the state and provincial highway 10. Data Collection Contracting: Agencies are increas- agencies have a formal quality acceptance plan. In the ingly considering the outsourcing of data collection and case of data collection contracts, quality acceptance is processing. However, although most agencies have often also linked to payments. evaluated this possibility, most of the pavement data are still collected using in-house resources. Typical quality acceptance activities include: Pavement distress and smoothness data are the data types that are most frequently outsourced (by Establishing acceptance criteria (data accuracy and about one-third of the respondents, 43% and 38%, precision and reliability); respectively). The main factor considered for making the decision Verification of the equipment/analysis criteria before to outsource the pavement data collection is cost- data collection; effectiveness, followed by limitations of the in-house Testing of known or blind (preferred) control or data collection capabilities and amount of data that verification sites before and during data collection; is to be collected. Software data check for reasonableness, complete- The main criterion used for service provider selec- ness, and consistency; and tion is past performance/technical ability, followed Time-series comparisons. by best value and low bid. 6. Independent Assurance: Quality engineering prac- Several of the data collection contracts include tices typically recommend the inclusion of at least some clauses that link payment to the quality of the data degree of external audit in the quality management plan. collected. The purpose of the independent assurance testing is to More than two-thirds of the agencies that have out- validate the data for the user agency. However, only 4% sourced at least part of the data collection indicated of the agencies surveyed use independent verification that data collection outsourcing was a positive step. for quality control and 12% for quality acceptance. 11. Changing Requirements/Technologies: The adoption 7. Equipment/Method Calibration, Certification, and of automated (and semi-automated) data collection Verification: The verification that the equipment is technologies has created challenges for the roadway functioning according to expectations and that the agencies that verify that the new equipment results are collection and analysis methods are being followed is consistent with the historical practices. Furthermore, key for ensuring the quality of the collected data. This is institutional changes, such as the reassessment of the typically done before the initiation of the data col- highway pavement management system or the adop- lection activities and periodically after that. Equip- tion of mechanisticempirical pavement analysis and ment or process verification and validation is typically design methodologies are also influencing the pave- assessed by determining their accuracy, repeatability, ment condition data detail and quality requirements. and reproducibility. 8. Control and Verification Sites: A common proce- ISSUES IDENTIFIED dure to verify the quality of the pavement data collec- tion during production is the use of a sample of control Some of the issues identified on the pavement management or verification roadway sections that are resurveyed or collection quality management practices include the following: reanalyzed by an independent evaluator and the results compared with the production ratings. The locations There is lack of uniformity on the type of data collected of these segments can be known or "blind" for data by the various state and provincial departments of trans- collection teams. The reference measurements on these portation and the approaches followed to manage the sections are determined using the best available prac- quality of the data collection process. tical technique for that particular pavement condi- Although there appears to be common agreement that data tion indicator. Statistical methods are typically used quality is important for effective pavement management,