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10 CHAPTER TWO PAVEMENT CONDITION DATA COLLECTION OVERVIEW This chapter focuses on the types of data collected by highway Roughness Index (IRI), which is computed as a linear accu- agencies to determine the pavement structural and functional mulation of the simulated suspension motion normalized by conditions and support pavement management decisions, how the length of the profile, and is expressed in inches per mile they are collected, and why they are important for pavement or meters per kilometer (20). In addition to the individual management. It combines information from the literature condition indicators, a large percentage of the respondents reviewed with results from the survey of state and provincial (82%) use an overall pavement condition index, in addition agencies. to smoothness and individual distresses. Typically, struc- tural capacity and frictional properties are collected at the project level. NETWORK- VERSUS PROJECT-LEVEL DATA COLLECTION At the project level, more specific data are typically col- Data collection for network-level decision making is gener- lected in terms of individual distress identification and severity. ally different from data collection for project-level decision Friction and structural capacity measurements are more preva- making in purpose, methods, and the actual data collected. lent at this level of data collection as more specific informa- Therefore, the quality requirements for the pavement condition tion is needed to determine specific preservation methods data needed are also different. Network-level data collection and budgeting requirements for individual pavement projects. involves collection of large quantities of pavement condition This level of information is appropriate for use in technical data, which is often converted to individual condition indices decisions, such as preservation treatment selection decision or aggregated into composite condition indices. Owing to the trees, design of the selected treatment, and project-level cost large quantity of required data, collection methods typically estimates. involve windshield surveys and automated methods, as these techniques can generally be performed at highway speeds Data collection methods at the project level often include without affecting traffic or posing a hazard to data collection a higher prevalence of walking surveys, in addition to the other teams. This information is then used to assess the overall methods used for collecting network-level data. Structural condition of the network, determine maintenance and reha- capacity evaluation is performed mostly at the project level bilitation strategies, and develop work programs and budgets to support the "design" of the maintenance or rehabilitation for the entire network. This level of information is most appro- projects that have been recommended through network-level priate for showing decision makers the highest priority pave- analysis. Cost and traffic disruption are the primary reasons ment segments and for making multi-year projections with cited for agencies not performing structural evaluations at the respect to the overall network condition. network level. Friction measurements are also used mostly at the project level. Figure 3 summarizes the percentage of states and Cana- dian provinces that collect each type of pavement condition Approximately half of the agencies (49%) indicted that the data at the network and project level; the value indicated data collected are being used to control pavement warranties, above each bar indicates the percentage of agencies collect- performance-based contracts, and/or other types of public ing the pavement indicators. These results are consistent with private partnerships. This type of contractual obligation creates the findings reported by McQueen and Timm (18). Network- additional demands in terms of the quality of the data. level surface distress and smoothness data are collected by almost all agencies. Only one agency (2%) reported that it is IN-HOUSE VERSUS SERVICE PROVIDER not collecting pavement distress data, and three (5%) reported COLLECTED DATA that they are not collecting smoothness data at the network level. Most agencies define pavement distresses and severities, Over the last decade, there has been an increase in the use of using approaches similar to the one used in the Long-Term data collection service providers for collecting both network- Pavement Performance (LTPP) Distress Identification Man- and project-level pavement condition data. This trend has been ual for the Long-Term Pavement Performance Program (19). fueled by a combination of three factors: (1) an increased Smoothness data are typically reported using the International demand for timely quality data to support pavement manage-
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11 Question: What pavement condition data does your agency collect? 98.2% 100% 94.6% Network Level Project Level 90% 80% 71.4% Percentage of Agencies 70% 66.1% 58.9% 60% 55.4% 50% 40% 33.9% 30% 20% 16.1% 10% 0% Surface Smoothness Structural Surface Friction Distress Capacity Properties FIGURE 3 Types of pavement condition data collected. ment decisions, (2) reductions in the public sector staff, and most data collected in those categories are still collected in- (3) availability of more sophisticated equipment that can house. These results are consistent with the trend recently collect large quantities of data quickly and efficiently but are reported by McGhee (6), which indicated that the most com- often expensive and complex to operate. For these reasons, monly contracted data collection services included sensor- agencies are increasingly considering the outsourcing of data measured data condition items (smoothness, rut depth, and collection and processing to the private sector. However, joint faulting). In the cases in which the smoothness and/or although most agencies (81%) have evaluated this possibility, distress data are collected by a service provider, the service most agencies still collect most of their data using in-house is usually outsourced to a single service provider. staff. Figure 4 summarizes the percentage of agencies using the various collection modes for each particular pavement Structural capacity data are collected primarily by in-house condition indicator; it is noted that not all agencies responded staff; however, for agencies that have outsourced structural to this question. capacity data collection, the use of multiple service providers is common. Friction data collection showed the lowest rate of Forty-eight percent of the respondents to the survey outsourcing; only one agency currently contracts these services (27 agencies) are currently contracting at least some of their with a commercial service provider. The survey also showed pavement data collection activities. Pavement distress and that the outsourcing practices are not different for the various smoothness data are the data types that are most frequently types of roads. The percentage of the agencies that have out- outsourced (by about one-third of the respondents), although sourced at least part of the data collection for each of the four Question: How does your agency currently collect pavement condition data? 100% In House 90% Outsourced to Single Contractor 80% Outsourced to Multiple Contractors Percentage of Agencies Not Collected 70% 63% 60% 57% 54% 50% 50% 40% 36% 38% 30% 21% 21% 20% 10% 5% 7% 4% 2% 0% 2% 0% 0% 0% Surface Distress Smoothness Structural Capacity Surface Friction Properties FIGURE 4 In-house versus contracted pavement condition data collection.
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12 Question: Please select the type of data that is being collected by contractor(s) for the different types of roadways. 100% 90% Highway (Interstate) Arterial (Primary) 80% Collector Local (Secondary) Percentage of Agencies 70% 60% 50% 38% 38% 39% 40% 36% 34% 30% 30% 20% 7% 9% 10% 5% 2% 2% 2% 0% Surface Smoothness Structural Surface Friction Distress Capacity Properties FIGURE 5 Types of data that are being collected by service providers by type of roadway. pavement condition indicators by administrative classification of the quality management process. The distinction between are presented in Figure 5. quality control and acceptance is not as clear when the data are collected in-house because both activities are conducted The transition from in-house data collection to the use of by the highway agency. data collection service providers has brought new attention to the way the quality of these data is managed. When the Data Collection Outsourcing Rationale agency uses a service provider, the data quality control and acceptance functions are clearly separated because they are The factors considered by the agencies that responded to conducted by different entities. The quality control is con- the survey for making the decision of whether or not to pri- ducted by the service provider and the quality acceptance by vatize the pavement condition data collection services are the owner agency. Because service providers may use different summarized in Figure 6. The main factor cited was cost- equipment and methodologies than those traditionally used effectiveness. Limitations of the in-house data collection capa- by the agency, quality checks to ensure consistency through- bilities and the amount of data that has to be collected were out the network and over time become a critical component also frequently cited. Question: What criteria did your agency use to determine whether or not to privatize pavement condition data collection? Cost-Effectiveness 70% Capability of In-House Data Collection 57% Scope of Data Collection Requirements 43% Experiences of other Agencies 32% Availability of Qualified Contractor 29% Other 20% N/A 4% 0% 20% 40% 60% 80% 100% FIGURE 6 Criteria considered to outsource pavement condition data collection.