National Academies Press: OpenBook

Quality Management of Pavement Condition Data Collection (2009)

Chapter: Chapter Six - Findings and Suggestions for Future Research

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Suggested Citation:"Chapter Six - Findings and Suggestions for Future Research." National Academies of Sciences, Engineering, and Medicine. 2009. Quality Management of Pavement Condition Data Collection. Washington, DC: The National Academies Press. doi: 10.17226/14325.
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Page 52
Suggested Citation:"Chapter Six - Findings and Suggestions for Future Research." National Academies of Sciences, Engineering, and Medicine. 2009. Quality Management of Pavement Condition Data Collection. Washington, DC: The National Academies Press. doi: 10.17226/14325.
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Page 52
Page 53
Suggested Citation:"Chapter Six - Findings and Suggestions for Future Research." National Academies of Sciences, Engineering, and Medicine. 2009. Quality Management of Pavement Condition Data Collection. Washington, DC: The National Academies Press. doi: 10.17226/14325.
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Page 53
Page 54
Suggested Citation:"Chapter Six - Findings and Suggestions for Future Research." National Academies of Sciences, Engineering, and Medicine. 2009. Quality Management of Pavement Condition Data Collection. Washington, DC: The National Academies Press. doi: 10.17226/14325.
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Page 54

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51 Pavement data collection quality control is receiving increased attention, not only because data collection is one of the most costly parts of operating a pavement management system, but also because data quality has a critical effect on the business decisions supported by the system. To ensure that the data collected meets the need of the pavement management process, agencies are developing procedures and guidelines for quality management of pavement data collection activities. The syn- thesis reviewed quality management practices being employed by public road and highway agencies for automated, semi- automated, and manual pavement condition data collection and delivery using in-house staff and contracted services. The following sections summarize the main findings of the study and provide topics for future research. SUMMARY OF FINDINGS The concepts of quality, quality management, quality control, and quality acceptance have been extensively used in manu- facturing industrial processes. However, these same principles, methods, and tools have not been systematically applied to pavement data collection. This is partially because in these services the “product” is not clearly known and the reference value often is difficult to determine. The literature suggests that the most efficient way to achieve high-quality pavement condition data collection services is to adopt a comprehensive, systematic quality management approach that includes meth- ods, techniques, tools, and model problem solutions. Independent of the mechanism used to collect the data, in-house or through a service provider, a complete quality management system may include a clearly documented qual- ity management plan, detailed and timely quality control and acceptance procedures, and established guidelines to monitor the entire data collection process. Before data collection, equip- ment is properly calibrated, procedures clearly defined and documented, and personnel trained. During data collection, pavement condition data is verified by a variety of possible methods to ensure data accuracy, consistency, and complete- ness during the collection effort. After data collection is com- plete, the data may then be validated before acceptance. The main findings concerning the state of the practice and knowledge of quality management of pavement condition data are the following: 1. Data Quality Requirements: Data collection prac- tices and quality management processes may be tai- lored to the use of the data and the level of decisions being supported. The level of detail, accuracy, and coverage (and consequently “quality”) required is different for supporting network- and project-level pavement management decisions. In general, surface distress (98% of respondents) and smoothness (95%) data are collected for network-level analysis. Project- level surveys typically include more detailed distress surveys (oftentimes walking the section) and assess- ments of the structural capacity (71%) and frictional properties (55%) for specific projects. 2. Quality Management Plan: This plan documents how the agency plans, implements, and assesses the effectiveness of its pavement data collection quality control, quality acceptance, and independent verifica- tion operations. Approximately one-third of the state and provincial highway agencies (35%) already have a formal plan and an additional 27% are working on developing such a plan. Furthermore, agencies with larger networks were more likely to have a formalized quality management plan than the smaller agencies. An example of the components of a quality management plan is provided in Figure 24. 3. Quality Management Tools and Methods: The main tools/methods used for quality control and accep- tance by state and provincial highway agencies are the following: • Calibration/verification of equipment and meth- ods before the data collection (used by 94% of the agencies for quality control and by 80% for quality acceptance), • Testing of known control segments before data col- lection (94% for quality control and 73% for quality acceptance), • Testing of known control or verification segments during data collection (81% for quality control and 71% for quality acceptance), and • Software routines for checking the reasonableness (57% for quality control and 71% for quality accep- tance) and completeness (55% for quality control and 61% for quality acceptance) of the data. Other promising quality management techniques that are not yet as commonly used include: • Analysis of time-series data both at the project and network-level (used by 42% of the agencies for qual- ity control and by 50% for quality acceptance), CHAPTER SIX FINDINGS AND SUGGESTIONS FOR FUTURE RESEARCH

52 Before Data Collection Define & set up: o Scope of work o Project schedule o Project team Select control sites and ground truth determination Setup collection subsystems Control site data collection and processing Quality Acceptance Define: o Data accuracy, precision, and resolution o Rating system/ protocol o Specific requirements/ specifications Known control site testing & review Quality Control Equipment calibration & acceptance Rater Training (certification) Standardization of operation procedures Develop quality check program Equipment/method validation using control sites Quality Acceptance Pilot feedback Blind (or known) control site testing Periodic raw data review (e.g., weekly) Periodic processed data review (e.g., monthly) Quality Control Equipment inspection Real-time data checks Raw data checks (e.g., daily) Processed data checks (e.g., weekly) Control site data monitoring Rater consistency monitoring File and project tracking/ documentation During Production (Data Collection & Processing) Pilot data collection & processing Production data collection Production data processing Control site (known & blind) testing Reruns and exceptions Quality Acceptance Final data review & feedback Review for missing segments (e.g., GIS-based) Sampling and statistical comparisons Independent quality assurance Time series comparisons Quality Control Check for missing segments or data elements Final database software checks Verification of distress ratings (e.g., using time series comparisons) After Data Collection Production Data assembly Exception flags Data Delivery Final Reports FIGURE 24 Example of quality management plan components [after Rada et al. (67) and Zhang and Smadi (73)]. • Independent (quality control or acceptance) verifi- cation and validation of the pavement condition data by an independent quality auditor (4% for quality control and 12% for quality acceptance), and • Use of blind site monitoring during the production quality acceptance process (24% for quality control and 21% for quality acceptance). 4. Quality Control includes actions and considerations necessary to assess and adjust production processes to obtain the desired level of quality of pavement condi- tion data. Approximately two-thirds of state and provin- cial highway agencies have a formal data collection quality control plan or require the service provider to develop such a plan. All pavement data collection ser- vice providers indicated having a formal data collection quality control plan. Based on the examples reviewed, a comprehensive quality control plan typically includes the following elements: • Clear delineation of the responsibilities, • Documented (and available) manuals and procedures, • Training requirements for the survey personnel,

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

several agencies still do not have formal quality man- agement plans. • Several agencies are facing problems with the consistency of data after the adoption of automated or semi-automated data collection methodologies, changes in the data collection equipment (in-house or service provider), or changes in service providers. • Several agencies also reported problems with the con- sistency of their location referencing systems, especially as they migrate from linear to geodetic methods. • There appears to be a need for guidelines to help agen- cies define the level of data quality and detail needed for the various pavement management functions and decision-making levels. SUGGESTIONS FOR FUTURE RESEARCH Based on the issues identified in the previous section, the following topics can be listed as future research needs: • Identification and demonstration of “best quality man- agement practices.” 54 • Investigation of the effect of emerging pavement data collection technologies on pavement management rec- ommendations. • Development of processes and procedures for evaluating backward compatibility in data from year to year and/or developing correlations in data where variability exists. • Development of commercially available tools for facili- tating of the implementation of quality management checks at the various stages of the data collection process. • Cost-effectiveness analysis of the implementation of dif- ferent quality management tools, methods, and programs. • Development of “generic” quality management, control, and assurance plans that agencies can customize for their specific needs, and/or software for guiding the develop- ment of these plans. These could be provided in the framework of an AASHTO Standard Practice. • Investigation of the need and content for a workshop or training course and materials specifically on quality management of pavement data collection. • Investigation of the impacts of alternative delivery meth- ods including performance-based warranty contracts on pavement data quality management.

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TRB’s National Cooperative Highway Research Program (NCHRP) Synthesis 401: Quality Management of Pavement Condition Data Collection explores the quality management practices being employed by public highway agencies for automated, semi-automated, and manual pavement data collection and delivery.

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