National Academies Press: OpenBook

Quality Management of Pavement Condition Data Collection (2009)

Chapter: Chapter Two - Pavement Condition Data Collection Overview

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Suggested Citation:"Chapter Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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 Two - Pavement Condition Data Collection Overview." 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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10 This chapter focuses on the types of data collected by highway agencies to determine the pavement structural and functional conditions and support pavement management decisions, how they are collected, and why they are important for pavement management. It combines information from the literature reviewed with results from the survey of state and provincial agencies. NETWORK- VERSUS PROJECT-LEVEL DATA COLLECTION Data collection for network-level decision making is gener- ally different from data collection for project-level decision making in purpose, methods, and the actual data collected. Therefore, the quality requirements for the pavement condition data needed are also different. Network-level data collection involves collection of large quantities of pavement condition data, which is often converted to individual condition indices or aggregated into composite condition indices. Owing to the large quantity of required data, collection methods typically involve windshield surveys and automated methods, as these techniques can generally be performed at highway speeds without affecting traffic or posing a hazard to data collection teams. This information is then used to assess the overall condition of the network, determine maintenance and reha- bilitation strategies, and develop work programs and budgets for the entire network. This level of information is most appro- priate for showing decision makers the highest priority pave- ment segments and for making multi-year projections with respect to the overall network condition. Figure 3 summarizes the percentage of states and Cana- dian provinces that collect each type of pavement condition data at the network and project level; the value indicated above each bar indicates the percentage of agencies collect- ing the pavement indicators. These results are consistent with the findings reported by McQueen and Timm (18). Network- level surface distress and smoothness data are collected by almost all agencies. Only one agency (2%) reported that it is not collecting pavement distress data, and three (5%) reported that they are not collecting smoothness data at the network level. Most agencies define pavement distresses and severities, using approaches similar to the one used in the Long-Term Pavement Performance (LTPP) Distress Identification Man- ual for the Long-Term Pavement Performance Program (19). Smoothness data are typically reported using the International Roughness Index (IRI), which is computed as a linear accu- mulation of the simulated suspension motion normalized by the length of the profile, and is expressed in inches per mile or meters per kilometer (20). In addition to the individual condition indicators, a large percentage of the respondents (82%) use an overall pavement condition index, in addition to smoothness and individual distresses. Typically, struc- tural capacity and frictional properties are collected at the project level. At the project level, more specific data are typically col- lected in terms of individual distress identification and severity. Friction and structural capacity measurements are more preva- lent at this level of data collection as more specific informa- tion is needed to determine specific preservation methods and budgeting requirements for individual pavement projects. This level of information is appropriate for use in technical decisions, such as preservation treatment selection decision trees, design of the selected treatment, and project-level cost estimates. Data collection methods at the project level often include a higher prevalence of walking surveys, in addition to the other methods used for collecting network-level data. Structural capacity evaluation is performed mostly at the project level to support the “design” of the maintenance or rehabilitation projects that have been recommended through network-level analysis. Cost and traffic disruption are the primary reasons cited for agencies not performing structural evaluations at the network level. Friction measurements are also used mostly at the project level. Approximately half of the agencies (49%) indicted that the data collected are being used to control pavement warranties, performance-based contracts, and/or other types of public– private partnerships. This type of contractual obligation creates additional demands in terms of the quality of the data. IN-HOUSE VERSUS SERVICE PROVIDER COLLECTED DATA Over the last decade, there has been an increase in the use of data collection service providers for collecting both network- and project-level pavement condition data. This trend has been fueled by a combination of three factors: (1) an increased demand for timely quality data to support pavement manage- CHAPTER TWO PAVEMENT CONDITION DATA COLLECTION OVERVIEW

11 ment decisions, (2) reductions in the public sector staff, and (3) availability of more sophisticated equipment that can collect large quantities of data quickly and efficiently but are often expensive and complex to operate. For these reasons, agencies are increasingly considering the outsourcing of data collection and processing to the private sector. However, although most agencies (81%) have evaluated this possibility, most agencies still collect most of their data using in-house staff. Figure 4 summarizes the percentage of agencies using the various collection modes for each particular pavement condition indicator; it is noted that not all agencies responded to this question. Forty-eight percent of the respondents to the survey (27 agencies) are currently contracting at least some of their pavement data collection activities. Pavement distress and smoothness data are the data types that are most frequently outsourced (by about one-third of the respondents), although most data collected in those categories are still collected in- house. These results are consistent with the trend recently reported by McGhee (6), which indicated that the most com- monly contracted data collection services included sensor- measured data condition items (smoothness, rut depth, and joint faulting). In the cases in which the smoothness and/or distress data are collected by a service provider, the service is usually outsourced to a single service provider. Structural capacity data are collected primarily by in-house staff; however, for agencies that have outsourced structural capacity data collection, the use of multiple service providers is common. Friction data collection showed the lowest rate of outsourcing; only one agency currently contracts these services with a commercial service provider. The survey also showed that the outsourcing practices are not different for the various types of roads. The percentage of the agencies that have out- sourced at least part of the data collection for each of the four 98.2% 94.6% 16.1% 33.9% 58.9% 66.1% 71.4% 55.4% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Surface Distress Smoothness Structural Capacity Surface Friction Properties Pe rc en ta ge o f A ge n ci es Network Level Project Level Question: What pavement condition data does your agency collect? FIGURE 3 Types of pavement condition data collected. 50% 54% 57% 63% 36% 38% 4% 2%2% 5% 7% 0%0% 0% 21% 21% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Surface Distress Smoothness Structural Capacity Surface Friction Properties Pe rc en ta ge o f A ge n ci es In House Outsourced to Single Contractor Outsourced to Multiple Contractors Not Collected Question: How does your agency currently collect pavement condition data? FIGURE 4 In-house versus contracted pavement condition data collection.

pavement condition indicators by administrative classification are presented in Figure 5. The transition from in-house data collection to the use of data collection service providers has brought new attention to the way the quality of these data is managed. When the agency uses a service provider, the data quality control and acceptance functions are clearly separated because they are conducted by different entities. The quality control is con- ducted by the service provider and the quality acceptance by the owner agency. Because service providers may use different equipment and methodologies than those traditionally used by the agency, quality checks to ensure consistency through- out the network and over time become a critical component 12 of the quality management process. The distinction between quality control and acceptance is not as clear when the data are collected in-house because both activities are conducted by the highway agency. Data Collection Outsourcing Rationale The factors considered by the agencies that responded to the survey for making the decision of whether or not to pri- vatize the pavement condition data collection services are summarized in Figure 6. The main factor cited was cost- effectiveness. Limitations of the in-house data collection capa- bilities and the amount of data that has to be collected were also frequently cited. 38% 36% 7% 2% 38% 39% 9% 2% 30% 34% 5% 2% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Surface Distress Smoothness Structural Capacity Surface Friction Properties Pe rc en ta ge o f A ge n ci es Highway (Interstate) Arterial (Primary) Collector Local (Secondary) Question: Please select the type of data that is being collected by contractor(s) for the different types of roadways. FIGURE 5 Types of data that are being collected by service providers by type of roadway. 4% 20% 29% 32% 43% 57% 70% N/A Other Availability of Qualified Contractor Experiences of other Agencies Scope of Data Collection Requirements Capability of In-House Data Collection Cost-Effectiveness 0% 20% 60%40% 80% 100% Question: What criteria did your agency use to determine whether or not to privatize pavement condition data collection? FIGURE 6 Criteria considered to outsource pavement condition data collection.

13 Several agencies also mentioned quality and timeliness of the data as important factors. However, whereas some agencies gave this reason in support of outsourcing the data collection, others used it to justify their decisions to continue collecting data with in-house resources. This disagreement appears to indicate that there are different degrees of satisfaction with the quality of the contracted services. Service Provider Selection The outsourcing of the data collection services typically begins with the issue by the owner agency of a request for proposals (RFP) or terms of reference document. This document outlines the services that are being requested, minimum quality require- ments for these services, required service provider qualifica- tions, and selection criteria. The main criteria used for service provider selection include past performance/technical ability (39%), best value (31%), and low bid (12%). The process often requires a pre-qualification of the poten- tial service providers before the economical offers are con- sidered. For example, some states require service providers to evaluate some control section and meet specific accuracy requirements. The New Mexico DOT has taken a unique approach; the agency has contracted the distress data collection through a professional service agreement with a group of universities within the state. Contract Characteristics The contracts are typically let based on a cost per mile (58%), with some having a lump-sum fixed price (31%) and a few agencies citing other contracting modes. One agency reported using a cost per kilometer for network-level evaluations, and a fee for service for project-level surveys. Although no agency reported using performance-based contracts, McGhee (6) found that in 2003 most data collection service contracts included a quality assurance provision, approximately half had price adjustment clauses, and a smaller fraction of the con- tracts included warranty provisions. The survey conducted for this synthesis revealed that sev- eral of the data collection contracts (39%) included clauses that link payment to the quality of the data collected; 32% of the contracts do not include such clauses and 29% of the respon- dents were not sure about the terms in the contract. The length of the contracting period is highly variable (see Figure 7), rang- ing from one year to more than three years. Longer contracting periods might lead to more consistency in the data, because the possible change in service providers during the successive bidding may introduce another source of variability. Although McGhee (6) found in 2003 that agencies were contracting only a particular data collection activity (e.g., network-level smoothness measurement), the information reviewed for the preparation of the synthesis appears to indi- cate that more agencies request that the service providers collect multiple pieces of information. For example, the latest Louisiana Department of Transportation and Development (LADOTD) RFP (21) included the following services: pre- liminary activities (including training of raters and work- station delivery); collection of global positioning system (GPS)-referenced, clear digital pavement (grayscale) and right-of-way (color) images and profile data for each district; distress quantification for all roads tested; and final docu- mentation of the project. The LADOTD service provider selection criteria included the following factors: firm experience on similar projects (16% of the weight), personnel experience as related to the project (16%), the consultant’s understanding of the project requirements as evidenced in the proposed work plan (16%), field trials (16%), and price (36%). The RFP requires that the consultant deliver on a weekly basis the following data: collected right-of-way images, raw data from the consultant’s Data Collection Vehicle’s electronic sensors (rutting, IRI, faulting, and GPS data), equipment calibrations test results (i.e., distress manifestation index, rut measurement device, video foot print, etc.), and electronic sensor verification results. The acceptance plan called for LADOTD personnel to eval- uate the pavement images and condition data summary to look for discrepancies and the right-of-way images for quality assurance. Other examples are presented in the case studies reviewed in chapter five. ISSUES ASSOCIATED WITH LOCATION REFERENCING Location referencing is an important part of pavement man- agement because it allows agencies to manage data spatially and with respect to time. This is important because meaning- ful analysis generally requires multi-year condition data of 1 year 11.5% 2 years 34.6% 3 years 23.1% > 3 years 30.8% Question: How long is the contracting period? FIGURE 7 Length of the contract period for outsourced pavement data collection services.

the same pavement segments to determine pavement deteri- oration trends and provide optimum preservation strategies. In addition, accurate referencing also allows overlaying con- dition indicators and other relevant parameters to identify sections in need of work, select appropriate interventions for those sections, and design the specific treatments. Therefore, the quality of the location referencing data is paramount for efficient pavement management. Quality management prac- tices include checks for the location data. Location referencing problems may affect the pavement condition data quality and the decisions supported by these data. For example, poor loca- tion data may make it difficult to overlap different pavement indicators (e.g., roughness and cracking), develop time-series for performance prediction, link condition with traffic, etc. A location referencing method refers to a technique used in the field or in the office to identify the specific location of an asset. Commonly used location referencing methods can be grouped in linear and geodetic (or spatial) reference methods. A location referencing system constitutes a set of procedures for determining and retaining a record of specific points in a transportation network. This system includes one or more location referencing method, as well as procedures for stor- ing, maintaining, and retrieving information about points and segments on the network (22). State-of-the-art referencing systems can handle more than one referencing method and datum (22, 23). Effective location referencing systems are comprehensive and can be used within and among agencies. This means that objects in the referencing system must be represented as they are in the real world. For example, roads and highway segments can be represented as one- or two-dimensional objects; that is, lines or polygons, and interchanges may be represented in three dimensions. Additionally, because an object’s characteristics may change with time, it is necessary to include a standard temporal reference, such as a date of inspection (24). 14 Linear Referencing The prevalent location referencing used in highway applica- tions is linear referencing. Linear referencing methods consist of procedures for specifying a location as a distance, or offset, along a linear feature (highway network), from a point with known location (25). Common linear location referencing methods include route/milepost, link-node, reference point/ offset (using a distance measurement instrument or distress manifestation index), and street address. Spatial Referencing The use of spatial location referencing based on GPS is becom- ing more prevalent as the technology becomes more afford- able and accurate. The use of GPS to mark the location of distressed areas prevents some of the errors encountered by using milepost methods. Because the location is known in terms of coordinates, the relocation of a milepost or road realignment will not affect the true location of the distressed area. This mitigates the problem of losing historical data when a new segmenting system is implemented and aids with inter- agency data sharing because coordinates can be converted for use in other referencing schemes. The use of GPS also provides for easier data integration, allowing for the possibility of a more comprehensive and universal location referencing system. The use of spatial/geodetic location referencing facilitates inter- agency standardization (26). Current Practice Figure 8 presents the location referencing methods used to support the pavement data collection activities by the agencies that responded to the survey. It is noted that some agencies use more than one method. Most agencies (86%) use mileposts and milepoints. This is a classic example of a linear reference 85.7% 26.8% 46.4% 7.1% 14.3% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Milepoints and Mileposts Link-Node Global Positioning System (GPS) National Differential GPS (NDGPS) Other Pe rc en ta ge o f A ge n ci es Question: What type of location referencing is used to support the pavement data collection activities? FIGURE 8 Types of location referencing used.

15 method, which may work well for use within an agency or department, but may not be suitable for sharing the data with other agencies or departments (which may use differ- ent referencing methods). The main advantage of this type of referencing is that it facilitates section identification and is familiar to most users and operators. A disadvantage is that markers may move (e.g., as a result of realignments), poten- tially changing the size and location of individual pavement segments. These changes may cause inconsistencies from year to year. Whereas many location referencing methods can be used successfully for pavement condition data collection, it is important that they are implemented using smart business practices to ensure the quality of the collected data. More than one-third of the agencies surveyed (38%) reported problems with location referencing, many of which involved ensuring system consistency between departments within the same agency. Other problems listed included those associated with conversion from linear referencing systems to other systems (e.g., spatial coordinates), time-history updates, and inter- departmental standardization. PAVEMENT CONDITION INDICATORS Pavement Distresses The types and number of distresses surveyed varies signifi- cantly from agency to agency. This variation is the result of historical practices, use of different materials and pavement designs, and variations in the environmental conditions. Although there have been efforts to standardize the defini- tions and measuring procedures for the various distresses by ASTM International and AASHTO, the use of national (or international) standards for distress data collection is still not a common practice. Recent steps include the publication of the ASTM Standard E1778, Standard Terminology Related to Pavement Distress. The LTPP Distress Identification Man- ual (19) is widely recognized for providing a good reference for project- and research-level distress data collection. The distresses collected by the various agencies responding to the survey are summarized in Figure 9. Rutting was the only universally collected distress closely followed by transverse cracking and fatigue cracking. Most agencies also collect data on longitudinal cracking and some collect bleeding and flush- ing. In general, the asphalt pavement distresses most frequently collected (rutting, fatigue, and transverse cracking) are con- sistent with those used in the hot-mix asphalt mix design and structural design of pavements [e.g., in NCHRP 1-37A MEPDG (27)]. After the various types of cracking, the most commonly measured portland cement concrete (PCC) pavement distresses collected are faulting and spalling. This selection reflects the typical concern with the condition of concrete pavement at the joints. Smoothness Pavement smoothness is typically considered the pavement condition indicator that best reflects the public’s perception of the overall condition of a pavement section. It affects ride quality, operation cost (e.g., fuel consumption, tire wear, and vehicle durability), and vehicle dynamics. Smoothness is computed by measuring the vertical deviations of the road surface along a longitudinal line of travel in the wheel path, which is known as the “profile.” The profile is typically deter- mined using laser-based measuring systems (high-speed or light-weight profilers). These profilers measure the pavement profile directly using lasers to record the distance from the vehicle to the pavement 21% 27% 30% 32% 36% 46% 54% 54% 64% 64% 77% 88% 89% 93% 100% Pumping Durability Cracking Shattered Slab Punch-outs Other Edge Cracking Bleeding/Flushing Spalling Faulting Raveling Map/Block Cracking Longitudinal Cracking Fatigue Cracking Transverse Cracking Rutting 0% 20% 40% 60% 80% 100% Question: What pavement distress data does your agency collect? FIGURE 9 Types of distress data collected.

and accelerometers to record the vertical movement of the vehicle. The profile is used in a simulation model to compute the IRI (ASTM E1926, Computing International Roughness Index from Longitudinal Profile Measurements). The IRI is a summary measurement of the profile elevation changes of a roadway that represent the accumulated vertical movement of a “standard” vehicle traveling on the measuring profile (28). Although the IRI is fast becoming the standard to directly measure ride quality, there is a lack of standardization among transportation agencies in collecting the data, as is discussed in chapter three. Surface Friction Properties Transportation agencies monitor pavement friction because it affects wet-pavement friction and wet-pavement crashes; inadequate friction often leads to higher rates of crashes (29). Thus, friction measurements are typically conducted as part of the state’s Wet-Accident Reduction Programs on areas with high numbers of crashes (30). The friction properties developed at the tire–pavement interface can be measured through contact testing, non-contact testing, or a combina- tion of both. State DOTs typically collect friction using the locked-wheel device, a contact method. Noncontact testing (e.g., using profilers) are starting to be used to determine the pavement macrotexture. The macrotexture measurements are used to determine the change of friction with speed; pavement with high macrotexture presents less reduction of friction with speed and is less probable to contribute to hydroplaning. The International Friction Index (IFI) uses macrotexture properties in conjunction with friction testing to normalize measurement made by different types of equipment (ASTM E1960-98, Standard Practice for Calculating Friction Index of a Pavement Surface). The index is composed of two num- bers, the friction value at 60 km/h (F60) and the change of friction with speed (sp). Structural Evaluation The structural capacity of a pavement segment is typically obtained by using nondestructive techniques, such as Falling Weight Deflectometer (FWD) and/or destructive testing (i.e., coring and testing of the extracted materials) (31). FWD testing is done by dropping a weight on the pavement and measuring the deflection response at different distances from the point of load application. If the layer thicknesses are known, this information can be used to calculate the pavement Struc- tural Number and modulus of the different layers (31). These properties can then be used to determine the remaining pave- ment structural capacity and service life. Agencies that have started to collect structural capacity data at the network level generally agree that collecting data with a lower sampling rate than the one required at the project level is cost-effective and provides useful information (32, 33). Studies in Kansas and Indiana have shown that performing 16 FWD tests at three sites per mile can provide statistically reli- able results (31). Deflection measuring devices that collect deflections at traffic speed appear to be more appropriate for network-level use. For example, the Rolling Weight Deflectometer (34) and the Danish Traffic Speed Deflectometer (35) provide more spatial coverage by measuring deflection at short intervals and averaging results over a longer length to reduce scatter. These technologies bring new opportunities for network-level pavement management; however, they also add additional issues in terms of data quality management. Accurate and repeatable measurements are still difficult to obtain and these technologies are not widely available. Because these devices are not currently being used for production surveys at the state and provincial level, these issues are not included in this synthesis. TIME-HISTORY DATA COLLECTION ISSUES One of the major challenges of successfully implementing and maintaining a PMS is ensuring consistency with legacy data when new techniques and technologies are implemented. Compatibility of the pavement condition data collection over time is very important for supporting effective pavement man- agement. Quality time-series of pavement condition data are needed to develop reliable deterioration models, measure the impact of maintenance and rehabilitation treatments, develop multi-year work plans, and optimize the allocation of resources. Therefore, it is important that the new and legacy data are compatible or can be made compatible through an appro- priate conversion. This applies to the actual data attributes (e.g., type of crack and length) and to the location referencing. The use of appropriate metadata (i.e., data about the data) can facilitate the transition. The issue of ensuring consistency over time is particularly important at the onset of adopting auto- mated technologies. This typically creates significant chal- lenges in terms of ensuring that the criteria and metadata are properly referenced. Pavement Condition Data Consistency The first concern with the adoption of a new data collection technology or with the contracting of a service provider is the verification that the pavement characteristics measured are at least as accurate as the existing data and with agency protocols and requirements. Furthermore, it is also important that the new data can be processed to provide pavement condition indicators that are consistent with the agency’s historical data to allow time-history analyses. For example, it is important that automated crack detection systems provide the same rat- ings as the agency’s visual method. Verification tests could be included in the quality management programs to verify this agreement. Several DOTs have used a pre-qualification process, in which they ask potential service providers to con- duct measurements on several control sections for which the

17 agency has conducted reference measurements. Another exam- ple is the certification process that has been proposed for pro- filers (36). Verification of the consistency of the data is also important when changing service providers or when the ser- vice providers (or the agency itself) use more than one pave- ment data collection piece of equipment or technology. Location Referencing Consistency The second key issue with the implementation of a new sys- tem or data collection approach is the adoption of a common location referencing method (37), or that appropriate and accu- rate conversion procedures are provided. Non-standardized location reference methods can pose significant obstacles when new methods of collecting data or new methods of using data are introduced. A universal location referencing method based on the spatial and temporal characteristics of the data collected can reduce problems with year-to-year variations and time- history updates. Agency enforcement of data referencing can prevent many time-history update problems (37). Generally speaking, spatial and temporal referencing of raw data is one of the most effective methods of ensuring historical continuity and preventing the loss of historical data. NETWORK COVERAGE AND SAMPLING Another important issue that affects the quality of the pavement condition data is the network spatial and temporal coverage. Network coverage and sample size are generally controlled by the type of data desired and their intended use. Pavement condition data quantity expectations generally vary according to: (1) the type of information required by the agency (and its intended use), (2) how often a particular piece of data is used, (3) the expense and/or difficulty in obtaining that data, and (4) changing federal requirements. The perceived rate at which the pavement condition changes and the volume of data nec- essary to provide useful information influences how often, if at all, different types of pavement data are collected. Thus, all these factors influence the frequency of evaluations and sampling procedures. Automated condition data collection is generally considered ideal for collecting network-level data because it allows for the efficient collection of large quantities of data, and with the proper calibration and quality manage- ment, data consistency can be assured (38). Temporal Coverage According to the survey, most agencies collect smoothness data for their highways at least once every three years, with many collecting data every year. Given that the HPMS program managed by the FHWA formerly required the submission of smoothness data on a sample of the network biennially (39), it is not surprising that smoothness data are collected frequently. The reassessment of the HPMS now requires annual smooth- ness submission for the National Highway System. Most agencies also collect surface distress data at least once every three years, with many collecting such data every year. Even agencies that still use windshield surveys reported data collection frequencies of three years or less, resulting in a high degree of temporal network coverage. Friction data are generally collected once every two to three years, with a low percentage of agencies collecting data every year. For network-level friction data collection, a road- way is typically divided into segments, usually 0.5 to 1.0 mile in length, and a friction value is measured over the segment. For example, Indiana collects annual friction testing on Interstate highways and once every three years on other roadways (29). Structural capacity data are collected with the lowest fre- quency. Network-level structural condition data for an Inter- state highway can be assessed by taking as few as three FWD readings per mile, once every five years, resulting in 20% net- work coverage per year. Studies in Indiana suggest that these measurements, along with ground penetrating radar (GPR) evaluation, can provide reliable information with respect to the remaining structural capacity of a pavement system (40). A significant number of respondents were unfamiliar with their agency’s structural capacity data collection practices, suggest- ing that the collection of this pavement condition indicator is conducted by an office other than the one in charge of pave- ment management. Lanes Evaluated Another important issue related to data quality is the number of lanes evaluated. Most agencies (73%) reported collecting data for one lane only along multi-lane roads, whereas only a few reported collecting data along multiple lanes of the same roadway. Studies in Indiana have shown that in terms of pave- ment smoothness, the difference between the driving lanes and passing lanes is statistically insignificant (29). However, this type of agreement would not be expected in cases where separate lanes may receive different preservation treatments. When only one lane of a multi-lane road is being evaluated, care is to be taken so that the same lanes are consistently eval- uated to be able to establish historical trends for developing performance models. Many agencies have recognized this and have standardized which lanes are used for collecting data; for example, many agencies collect data on the primary direction on two-lane roads and on the outside lane in both directions on four-lane roads. NEW DEMANDS IMPOSED BY CHANGING BUSINESS PRACTICES Changes in business practices, such as the HPMS reassessment and the adoption by AASHTO of the MEPDG, are expected to affect quality management practices. The type of data collected and their degree of detail will likely change, influencing the quality management practices used for their collection.

Impact of the Highway Pavement Management System Reassessment The HPMS is a key national transportation data program that provides highway inventory, condition, performance, and operating characteristics data to national, state, and regional customers. The system is used at the national level for appor- tionment, performance measures, highway statistics, and con- ditions reporting. HPMS stores data on pavement condition and other items such as road classification and travel by vehi- cle type. Because all state DOTs are required to report their data, HPMS data requirements have a significant impact on their data collection practices. The HPMS has historically only required ride quality data on a biennial cycle to characterize the condition of the pave- ments. Pavement metadata to describe the processes used for collecting and reporting some of the pavement data items have been optional. States report the average of both right- and left- wheel path quarter-car IRI as a Mean Roughness Index. How- ever, the system has recently been evaluated to respond to current and future business needs, capitalize on changing tech- nology, and address resource constraints and institutional changes. The reassessment has resulted in new procedures that increase annually the frequency of IRI data collection through- out the National Highway System and requires the collection of additional pavement condition and structure data. These new requirements would be expected to change the quality management practices states follow to collect the required data. The specific additional pavement condition data items required include rutting and faulting (collected using a profiler at same time as IRI), IRI year, fatigue, and transverse cracking (regardless of severity). Other pavement-related data include last construction or reconstruction year, last overlay data and thickness, layer thicknesses, base type, and subgrade soil type. In addition, the reassessment has made metadata required, reduced the IRI metadata, and added additional metadata for rutting, faulting, and cracking. The required pavement meta- datahasbeenpublishedinthe HPMS Reassessment 2010+ (41). Impact of the Mechanistic–Empirical Pavement Design Guide Implementation Many highway agencies are adopting mechanistic–empirical procedures, because this approach in general provides an improved methodology for pavement analysis and design with respect to the traditional purely empirical approaches. In particular, AASHTO has recently adopted the MEPDG pro- posed by NCHRP Topic 1-37A (27). Mechanistic–empirical procedures use pavement models based on the mechanics of materials to predict pavement responses (deflections, strains, and stresses) and empirically based transfer functions to estimate distress initiation and development based on these responses. The distresses predicted are then used to estimate the evolution of ride quality, in terms of IRI. Implementation of these procedures is expected to improve the efficiency of 18 pavement designs, provide better capability for the prediction of pavement lifetime preservation needs, and help assess the effect of new materials and pavement technologies and sys- tems. In the long term these changes are expected to result in longer-lasting pavements that provide smoother, safer, and more comfortable rides, thus improving the level of service provided to the roadway users (42). The implementation of mechanistic–empirical pavement analysis and design methodologies is expected to affect pavement management practices and, in particular, pavement condition data collection. The validation and calibration of the mechanistic–empirical models depends on accurate per- formance data from in-service pavement sections. Although information currently available in some PMS can be used to develop initial calibration factors, an accurate long-term cal- ibration will require significant changes in the information that is stored in the PMS databases. The level of detail and quality of the information currently included in most PMS does not appear to be enough to support the validation and calibration of mechanistic–empirical pavement analysis and performance prediction models. A recent study conducted to determine the best way to calibrate the proposed MEPDG pavement condition prediction models using PMS data identified several limitations (43). For example, in most cases the materials, construction, and maintenance data available within a DOT are not currently tied to pavement management activities. This makes it difficult to link performance data to materials and construction data. Furthermore, not all relevant data are recorded (e.g., in-place thickness is often missing) and the network-level pavement deterioration and performance data often do not have the required level of detail. Therefore, the use of mechanistic–empirical performance models, especially at the network level, will likely require the collection of signif- icantly more data, such as material characterization test results, environmental conditions, as-constructed layer thicknesses and properties, and more detailed condition evaluations. SUMMARY This chapter covered the types of data collected by highway agencies to determine the pavement structural and functional conditions and support pavement management decisions, and how they are collected. The review of practice indicates that the type and quality of data required for network- and project- level decision making is generally different. Although smooth- ness and distress are collected at the network level by most DOTs, deflections and friction are collected mostly at the project level. Factors that impact the quality management procedures, and that require additional attention include the following: • Pavement data collection outsourcing—brings new challenges to ensure that the data collected are consistent with the agency protocols and requirements. This is par-

19 ticularly true for those agencies switching from in-house manual to contracted automated or semi-automated data collection. • Quality of the location referencing data is paramount for efficient pavement management, allowing, for exam- ple, the collection of time-series of data for developing performance curves, overlapping different pavement indicators (e.g., roughness and cracking) to determine optimum preservation treatments, and linking condition with traffic and other road characteristics. • Historical data consistency—especially when adopting new technologies or measurement techniques. • Network spatial and temporal coverage—expectations for quality and quantity of pavement condition data gen- erally vary according to the type of information required by the agency (and its intended use), how often a partic- ular piece of data is used, and the difficulty in obtaining that particular data. • New demands imposed by changing business practices— the HPMS reassessment and the adoption by AASHTO of the MEPDG for example are expected to affect quality management practices. The type of data collected and their degree of detail will change, influencing the quality management practices used for their collection.

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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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