Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 18
18 Impact of the Highway Pavement Management pavement designs, provide better capability for the prediction System Reassessment of pavement lifetime preservation needs, and help assess the effect of new materials and pavement technologies and sys- The HPMS is a key national transportation data program that tems. In the long term these changes are expected to result in provides highway inventory, condition, performance, and longer-lasting pavements that provide smoother, safer, and operating characteristics data to national, state, and regional more comfortable rides, thus improving the level of service customers. The system is used at the national level for appor- provided to the roadway users (42). tionment, performance measures, highway statistics, and con- ditions reporting. HPMS stores data on pavement condition The implementation of mechanistic≠empirical pavement and other items such as road classification and travel by vehi- analysis and design methodologies is expected to affect cle type. Because all state DOTs are required to report their pavement management practices and, in particular, pavement data, HPMS data requirements have a significant impact on condition data collection. The validation and calibration of their data collection practices. the mechanistic≠empirical models depends on accurate per- formance data from in-service pavement sections. Although The HPMS has historically only required ride quality data information currently available in some PMS can be used to on a biennial cycle to characterize the condition of the pave- develop initial calibration factors, an accurate long-term cal- ments. Pavement metadata to describe the processes used for ibration will require significant changes in the information that collecting and reporting some of the pavement data items have is stored in the PMS databases. The level of detail and quality been optional. States report the average of both right- and left- wheel path quarter-car IRI as a Mean Roughness Index. How- of the information currently included in most PMS does not ever, the system has recently been evaluated to respond to appear to be enough to support the validation and calibration of current and future business needs, capitalize on changing tech- mechanistic≠empirical pavement analysis and performance nology, and address resource constraints and institutional prediction models. A recent study conducted to determine changes. The reassessment has resulted in new procedures that the best way to calibrate the proposed MEPDG pavement increase annually the frequency of IRI data collection through- condition prediction models using PMS data identified several out the National Highway System and requires the collection limitations (43). For example, in most cases the materials, of additional pavement condition and structure data. These construction, and maintenance data available within a DOT new requirements would be expected to change the quality are not currently tied to pavement management activities. management practices states follow to collect the required data. This makes it difficult to link performance data to materials and construction data. Furthermore, not all relevant data are The specific additional pavement condition data items recorded (e.g., in-place thickness is often missing) and the required include rutting and faulting (collected using a profiler network-level pavement deterioration and performance data at same time as IRI), IRI year, fatigue, and transverse cracking often do not have the required level of detail. Therefore, the (regardless of severity). Other pavement-related data include use of mechanistic≠empirical performance models, especially last construction or reconstruction year, last overlay data and at the network level, will likely require the collection of signif- thickness, layer thicknesses, base type, and subgrade soil type. icantly more data, such as material characterization test results, In addition, the reassessment has made metadata required, environmental conditions, as-constructed layer thicknesses and reduced the IRI metadata, and added additional metadata for properties, and more detailed condition evaluations. rutting, faulting, and cracking. The required pavement meta- data has been published in the HPMS Reassessment 2010+ (41). SUMMARY Impact of the Mechanistic≠Empirical This chapter covered the types of data collected by highway Pavement Design Guide Implementation agencies to determine the pavement structural and functional conditions and support pavement management decisions, and Many highway agencies are adopting mechanistic≠empirical how they are collected. The review of practice indicates that procedures, because this approach in general provides an the type and quality of data required for network- and project- improved methodology for pavement analysis and design level decision making is generally different. Although smooth- with respect to the traditional purely empirical approaches. In ness and distress are collected at the network level by most particular, AASHTO has recently adopted the MEPDG pro- DOTs, deflections and friction are collected mostly at the posed by NCHRP Topic 1-37A (27). Mechanistic≠empirical project level. procedures use pavement models based on the mechanics of materials to predict pavement responses (deflections, strains, Factors that impact the quality management procedures, and stresses) and empirically based transfer functions to and that require additional attention include the following: estimate distress initiation and development based on these responses. The distresses predicted are then used to estimate ∑ Pavement data collection outsourcing--brings new the evolution of ride quality, in terms of IRI. Implementation challenges to ensure that the data collected are consistent of these procedures is expected to improve the efficiency of with the agency protocols and requirements. This is par-
OCR for page 18
19 ticularly true for those agencies switching from in-house ∑ Network spatial and temporal coverage--expectations manual to contracted automated or semi-automated data for quality and quantity of pavement condition data gen- collection. erally vary according to the type of information required ∑ Quality of the location referencing data is paramount by the agency (and its intended use), how often a partic- for efficient pavement management, allowing, for exam- ular piece of data is used, and the difficulty in obtaining ple, the collection of time-series of data for developing that particular data. performance curves, overlapping different pavement ∑ New demands imposed by changing business practices-- indicators (e.g., roughness and cracking) to determine the HPMS reassessment and the adoption by AASHTO optimum preservation treatments, and linking condition of the MEPDG for example are expected to affect quality with traffic and other road characteristics. management practices. The type of data collected and ∑ Historical data consistency--especially when adopting their degree of detail will change, influencing the quality new technologies or measurement techniques. management practices used for their collection.