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Quality Management of Pavement Condition Data Collection (2009)

Chapter: Chapter One - Introduction

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Suggested Citation:"Chapter One - Introduction." 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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Suggested Citation:"Chapter One - Introduction." 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 7
Suggested Citation:"Chapter One - Introduction." 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.
×
Page 7
Page 8
Suggested Citation:"Chapter One - Introduction." 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.
×
Page 8
Page 9
Suggested Citation:"Chapter One - Introduction." 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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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

5A large number of public highway agencies in the United States have adopted pavement management systems (PMS) to cost-effectively manage the pavements on the more than 4 million km (approximately 2.6 million miles) of paved pub- lic roads. The collection of network-level pavement condition data, especially pavement distress data, is one of the most costly parts of operating a PMS. This function is also very important because data quality has a critical effect on the busi- ness decisions supported by the PMS. If the quality of the pave- ment condition data is inadequate, the consequent decision making will be compromised. For example, the PMS may rec- ommend inappropriate treatments, or it may not program the roadway sections most in need of preservation. These “wrong” decisions undermine the effectiveness of, and confidence in, the pavement management process. According to AASHTO (1), “a properly planned and implemented data collection program will significantly increase credibility, cost-effectiveness, and overall utility of the PMS.” To effectively support the pave- ment management process, the data collection program col- lects, processes, and records data in a timely fashion, with a level of accuracy and precision adequate for the decision being supported, assuring data consistency and continuity from year to year, and using a consistent location referencing system (1). To ensure that the quality of the data collected meets the needs of the pavement management process, agencies are developing procedures and guidelines for managing the quality of pavement data collection activities. Agencies using ser- vice providers for pavement data collection have developed methods for service provider selection, monitoring during the contract period, and data acceptance. Agencies using staff resources for pavement data collection have developed similar quality management activities, which also include train- ing of their staff. Furthermore, many agencies are also coping with changing automation technologies that decrease cost but pose challenges with time-history consistency of the data being collected. Agencies must place special care to ensure that data collected at different times are consistent (e.g., the same pave- ment characteristics are measured) to obtain reliable pavement condition time-series, monitor the performance of the network, and assess the impact of the pavement management decisions. OBJECTIVE The objective of this synthesis is to document quality manage- ment practices being employed by public road and highway agencies for automated, semi-automated, and manual pave- ment condition data collection and delivery. In particular, the synthesis examines: (1) the quality management techniques used in service provider selection, monitoring, and data accep- tance by agencies that outsource the data collection; (2) the quality management techniques used for operations by in- house staff; and (3) how these practices affect the quality of the decisions made based on the data collected. METHODOLOGY This synthesis includes information from a compilation of sources, including an extensive literature review, an electronic survey of state and provincial practices and data collection contractors, and follow-up communications with a select num- ber of state agencies. The survey was conducted electronically using interactive web-based commercial software. A detailed web-based questionnaire was developed for collecting the information from the state and provincial agencies, and a link to the electronic survey was sent to the Pavement Management contacts in all states and Canadian provinces. This question- naire was dynamic and questions displayed were dependent on previous responses. On completion, the survey was auto- matically saved in a database. Fifty-five agency responses, covering 46 states and 9 Canadian provinces, were received. No local agencies were included in the survey. A shorter version of the survey was sent to private data collection service providers; six responses from service providers were received. Copies of the survey forms used are provided in Appendices A and B, and the results for the agencies and service providers are summarized in Appendix C. The analysis of the responses received is included in the relevant sections of the synthesis. SCOPE AND ORGANIZATION The study scope, which focused on network-level data, cov- ered the following elements: • Clear definition of key terms; • Importance of quality data to pavement management processes and other uses of the data; • Quality management techniques used for monitoring, and accepting pavement condition data collection activities by in-house staff and data collection service providers; • Tools available for quality control, quality acceptance, and independent assurance; CHAPTER ONE INTRODUCTION

• Effect of the size of the network being evaluated on the quality management process; • How agencies are addressing issues associated with loca- tion referencing systems; • Time-history issues associated with the introduction of new techniques and/or changes in data service provider; • Gaps in knowledge and needed improvements to current practice; and • Specific research and development needs. Although the synthesis focuses on the collection of dis- tress data at the network level, it also covers smoothness, friction, and structural capacity data collection processes and some elements of current quality management practices for project-level data collection. The synthesis contains six chapters. Chapter one provides the background for the synthesis, including the objectives and scope of work, major definitions relevant to the synthe- sis, the methodology used for collecting and analyzing the information, some general background on the types of pave- ment condition data collected, the importance of quality data, and the impact of these data on the quality of the supported pavement management decisions. It also provides a brief description of the organization of the report and how the var- ious components of the research were used to develop the report’s conclusions. Chapter two discusses in detail the type of data collected by highway agencies to determine the pavement’s structural and functional condition to support pavement management deci- sions. Specific issues covered included network- vs. project- level data collection, outsourcings of pavement condition data collection to service providers, location referencing, pavement characteristics evaluated, and network coverage. The chapter also addresses time-history issues associated with introduc- tion of new techniques and/or changes in service providers and additional challenges arising for adoption of new business processes, such as the Highway Performance Monitoring System (HPMS) reassessment and implementation of the Mechanistic-Empirical Pavement Design Guide (MEPDG). Chapter three presents the main data quality management concepts and principles and summarizes the general poli- cies and guidelines currently being followed by transportation agencies to conduct quality management activities. In partic- ular, the chapter expands on data quality management plans and the distinction between quality control, quality acceptance, and independent assurance. It also covers reference value determination, sources of variability in pavement data col- lection, and the effect of the size of the network being evalu- ated on the quality management process. Chapter four focuses on the specific quality management techniques that are being applied for pavement condition data collection. It discusses the tools and processes being followed for quality control, quality acceptance, and independent ver- 6 ification, and their effect on data rejection. Although the same tools are often used in more than one stage of the quality man- agement process, they are organized in the most common configuration. Quality control tools discussed include person- nel training and certification, equipment/method calibration, verification, and certification, data verification procedures, and software data checks. Quality acceptance tools include control and verification test sites, establishing acceptance cri- teria, sampling, database checks, and time-history compar- isons. The chapter also covers some specific issues associated with the acceptance of data collection assembled by contracted service providers. Chapter five documents the data management practices of four transportation agencies. For each agency reviewed, the chapter discusses the quality management procedures applied before collecting the data, as the data are being collected, dur- ing the post-processing of the data, and during the analysis of the data for supporting pavement management decisions and other business processes. Each case study highlights some of the distinct aspects of the agencies’ quality management practices. Finally, chapter six provides a summary of the key findings of the synthesis project, summarizes the state of the practice on quality management of pavement condition data, and highlights examples of good practices. This final chapter also identifies gaps in knowledge and needed improvements to current practice, and notes areas that have specific research and development needs. BACKGROUND The Data Warehousing Institute estimates that poor data qual- ity costs American business $600 billion annually (2). Trans- portation agencies are no exception; quality data are essential to support asset (and in particular pavement) management deci- sions at all organizational levels. However, the level of detail and “quality” of the information required is heavily depen- dent on the level of decision making being supported. This section provides a brief overview of the types of condition data collected for managing pavements, defines quality and the main quality terms used in pavement data collection, and intro- duces the main issues associated with the quality of pavement condition data. Pavement Management Pavement management is a key asset management business process that allows department of transportation (DOT) per- sonnel to make cost-effective decisions regarding the preser- vation and renewal of the pavements under their jurisdiction. Pavement management provided the framework for the devel- opment of asset management, and pavements account for a large percentage of the total assets managed by a typical DOT (3). A PMS is a set of decision-support tools (and meth-

7ods) that can assist decision makers in finding cost-effective strategies for providing, evaluating, and maintaining pave- ments in a serviceable condition (4). An effective PMS, as with any decision support tool, includes reliable and sufficient data, calibrated analysis models and procedures, and tools that help visualize and quantify the impact of the possible solu- tions considered. The 2001 AASHTO Pavement Management Guide discussed in detail the technologies and processes used for the selection, collection, reporting, management, and analy- sis of data used in pavement management at the state level (1). The data needed in a PMS include inventory information (e.g., pavement structure, geometrics, costs, and environment), road usage (traffic volume and loading), pavement condition (ride quality, surface distresses, friction, and/or structural capacity), and pavement construction, maintenance, and rehabilitation history. In particular, the quality of the pavement condition data is critical for producing informed decisions. Pavement management tools are currently used to support strategic decisions across various asset types within agency- wide asset management systems, network-level project selec- tion and resource allocation decisions, and project-level decisions. Strategic-level tools typically support trade-off analysis across asset classes and agency programs, and high- level impact analysis. Network-level analysis tools support planning and programming decisions for the entire network or system (5). Examples include tools to evaluate the condition of the pavement network and predict pavement performance over time; identify appropriate preservation and rehabilita- tion projects; evaluate the different alternatives and deter- mine the network needs; prioritize or optimize the allocation of resources to generate plans, programs, and budgets; and assess the impact of the funding decisions. Project-level analy- sis tools are then used to select the final alternatives and design the projects included in the work program. Examples include tools for pavement-type selection, life-cycle cost analysis, pavement analysis, and structural design. Pavement Management Data Collection Pavement condition data collection is one of the key com- ponents of pavement management. Several NCHRP synthe- ses have covered this topic; Table 1 summarizes the most recent ones. The type of data collected in a PMS include smoothness (ride quality), surface distresses (rutting, cracking, faulting, etc.), frictional properties of the surface (tire/pavement fric- tion or skid resistance and, more recently, macrotexture), and structural capacity (deflections). The way in which transporta- tion agencies collect, store, and analyze data has evolved along with advances in technology, such as mobile comput- ing, advanced sensors, imaging technologies, distributed data- bases, and spatial technologies. These technologies have enabled the data collection and integration procedures nec- essary to support the comprehensive analyses and evaluation processes needed for asset management (12). However, the use of the aforementioned technologies has in some cases led agencies to collect very large amounts of data and create vast databases that have not always been useful or necessary for supporting network-level decision processes. It is important that the agencies tailor the data collection practices to the use of the data and the level of decisions being supported. Because of excessive data collection requirements, PMS are sometimes seen as too data-intensive and too expen- sive to sustain. To avoid this situation, three guiding prin- ciples are recommended: (1) collect only the data needed; (2) collect data to the lowest level of detail sufficient to make appropriate decisions; and (3) collect data only when they are needed (13). To help tailor the data collection practices to the uses of the data, Paterson and Scullion (14) introduced the concept of Information Quality Levels (IQL) for road man- agement. This concept helps highway agencies structure road management information into different levels that correlate to the degree of sophistication required for decision making and, thus, the appropriate methods for collecting and pro- cessing data. Within the proposed framework, very detailed data (low-level data) can be condensed or aggregated into progressively simpler forms (higher-level data). Bennett and Paterson (15) defined five levels as presented in “A Guide to Calibration and Adoption of HDM-4” (see Figure 1). They ranged from very detailed data in an IQL-1 (research and benchmark data for other measurement methods) to a very general IQL-5 (top-level data, such as key performance mea- sures or indicators, which typically might combine key attri- butes from several pieces of information). Another relevant and current issue is that the pavement condition data collection technologies are advancing rapidly. NCHRP Synthesis 334 (6) found that essentially all North American highway agencies are collecting pavement condi- tion data through some automated means. Furthermore, the synthesis also found that 33 agencies (out of 56) use service providers (also called vendors or contractors) to collect at least some of the automated data. This creates new challenges for ensuring data consistency over time, as these automated data collection technologies may measure different pavement characteristics than those determined visually. The study also found that significant advances have been made in the area of quality assurance. In particular, the synthesis highlights some good examples from Canadian provinces, especially with sensor-related processes. Quality Management—General Terminology Quality is a desired essential or distinctive characteristic, prop- erty, or attribute of something (or its degree of excellence). To consistently achieve a quality product or service, it is nec- essary to adopt appropriate quality management practices. Although the concept of quality management is well devel- oped and has been extensively used in industrial production

8TABLE 1 RECENT NCHRP SYNTHESES RELATED TO PAVEMENT CONDITION DATA COLLECTION No. Title Year Content 334 (6) Automated Pavement Distress Collection Techniques 2004 Examines automated collection and processing of pavement condition data techniques typically used in network-level pavement management, contracting issues, quality assurance, costs and benefits of automated techniques, monitoring frequencies and sampling protocols in use, degree of adoption of national standards for data collection, and contrast between the state-of-the-art and the state-of- the-practice. 335 (3) Pavement Management Applications Using Geographic Information Systems (GIS) 2004 Examines the use of GIS and other spatial technologies in PMS, and discusses how the technologies have been combined to enhance the highway management process. It discusses data collection and integration, and location referencing systems. 291 (7) Evaluation of Pavement Friction Characteristics 2000 Discusses the methods used for evaluating wet pavement friction characteristics of new and restored pavements and reviews models used for measuring and evaluating friction and texture, causes for friction changes over time, and aggregate and mix design to provide adequate friction. 268 (8) Relationship Between Pavement Surface Texture and Highway Traffic Noise 1998 Presents a comprehensive synopsis of pavement/tire noise as it relates to roadways. Detailed information is presented on measurement techniques, reported noise emission results for pavement type and texture, effects of pavement wear, surface friction, and maintenance and safety considerations. 203 (9) Current Practices in Determining Pavement Condition 1994 Examines practices for the collection, reporting, and application of pavement condition data for their service in PMS, focusing on four primary measures of pavement condition: distress, smoothness, structural capacity, and friction evaluations. It describes the types of equipment used and how the data are used to affect decision making by transportation managers. 167 (10) Measurements, Specifications, and Achievement of Smoothness for Pavement Construction 1990 Examines the various devices and specifications that were being used to measure smoothness and ensure that newly constructed pavements will provide a smooth ride. 126 (11) Equipment for Obtaining Pavement Condition and Traffic Loading Data 1986 Identified equipment that was associated with the collection of structural capacity, surface distress, friction, smoothness, and traffic loading data. Costs, maintenance requirements, advantages and disadvantages, and new equipment developments are briefly discussed. FIGURE 1 Information quality level [after Bennett and Paterson (15)]. Network Level Data Performance Structure Condition Ride Distress Friction IQL-5 IQL-4 IQL-3 IQL-2 IQL-1 System Performance Monitoring Planning and Performance Evaluation Program Analysis or Detailed Planning Project Level or Detailed Programming Project Detail or Research HIGH LEVEL DATA LOW LEVEL DATA

9processes, it has not been systematically applied to pavement data collection. This issue is discussed in detail in the follow- ing chapters. The following definitions were obtained by adapting those found in the quality management field to the pavement data collection activities. Special attention was given to the defi- nitions provided in the Transportation Research Circular E-C037: Glossary of Highway Quality Assurance Terms (16), which defined standard terminology for highway quality with a focus on construction processes and to the terminology used in NCHRP Synthesis 334 (6). Quality: “The degree to which a set of inherent charac- teristics fulfill requirements” (17). These requirements could be features and characteristics of a product that are specified in a contract or identified and defined internally by the company or agency based on the cus- tomer expectations. The product could be a physical entity (e.g., a calculator) or a service (e.g., auto repair, or, as is the focus of this synthesis, data collection). Quality Management: The overarching system of poli- cies and procedures that govern the performance of quality control and acceptance activities; that is, the totality of the effort to ensure quality in the pavement condition data. Quality System: The organizational structure, procedures, processes, and resources needed to implement quality management to meet the quality objectives. Quality Control: Those actions and considerations nec- essary to assess and adjust production processes so as to control the level of quality being produced in the end product. It is also called process control. For pur- poses of this synthesis, quality control activities are those used to control the data collection activities, either by a data collection service provider or a road agency collecting data in-house, so that quality pavement con- dition data can be obtained. Quality Acceptance: Those planned and systematic actions necessary to verify that the data meet the quality requirements before it is accepted and used to support pavement management decisions. These actions govern the acceptance of the pavement condition data collected using either a service provider or in-house resources. Quality acceptance is often referred to as quality assur- ance in the pavement engineering and management field. Quality Assurance: The part of quality management focus- ing on increasing the ability to fulfill requirements. It includes all those planned and systematic actions nec- essary to provide confidence that a product or facility will perform satisfactorily in service. Because this term is often used in practice to refer to quality acceptance activities, to avoid confusion it is not used in the remain- ing sections of the synthesis. Independent Assurance: A management tool that requires a third party, not directly responsible for process control or acceptance, to provide an independent assessment of a product or service and/or the reliability of test results obtained from process control and acceptance testing (16). Figure 2 summarizes the terminology used in this synthe- sis, and provides examples of activities in each data quality management phase. The figure also shows examples of activ- ities typically included in these processes. These and other relevant activities will be introduced in chapter two and dis- cussed in detail in chapters three and four. FIGURE 2 Pavement condition data quality management framework. Qu ali ty M a n a ge m en t • Personnel training/certification • Equipment calibration/ certification/ inspections … • On-vehicle real-time data checks • Periodic diagnostics/data checks • Incoming data and video check … • Distress rating data checks • Final database checks • Completeness checks … • Initial Control Site Testing • Review qualifications or certifications … • Complete database checks • Control/ verification site testing • Sampling for quality acceptance … • Final database reviews • GIS-based quality checks • Time history comparisons … Independent Assurance Before Data Collection During Production (Data Collection & Processing) After Production Quality Control Quality Acceptance • Consistency checks • Sampling and re-analyzing … • Completeness checks • Time History Comparisons …

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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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