The asphalt industry has a well-established history of using new technologies and innovative materials in flexible pavement cross-sections. Examples include warm mix asphalt (WMA), recycled asphalt shingles (RAS), reclaimed asphalt pavement (RAP), recycled tire rubber (RTR) and cold recycled mixtures. These, and other technologies currently in development, offer significant economic, environmental, and engineering advantages over conventional materials if used properly within a structural design framework.
The primary structural design approaches in the U.S. are empirical and mechanistic-empirical. At last count (Pierce and McGovern, 2014), the empirical approach is in use by 28 state DOTs with the majority of those using the 1993 AASHTO Design Guide (AASHTO, 1993). The remaining states have implemented or are transitioning to the new M-E approach adopted by AASHTO (2008) or have their own state-specific empirical or M-E method. A major challenge every agency faces when making the transition to M-E design is local calibration of the empirical transfer functions that predict pavement performance. Calibration is typically accomplished with a combination of laboratory testing and field-data collection from existing pavements which can take years to complete at relatively great cost. This assumes that existing pavement sections that vary in age and conditions are available from which to extract performance data. Calibration for new pavement materials or technologies is further complicated by the fact that field and lab data may be sparse or non-existent. This fact greatly limits the deployment of the technology or new material until it is fully tested, and transfer functions are fully calibrated.
The pace at which new pavement materials and technologies are developed requires more rapid methods for calibrating transfer functions. To this end, calibration should take no longer than one year to be efficient and effectively support technology deployment. This research project aims to identify and develop methodologies to rapidly calibrate M-E transfer functions for new materials and technologies. Since construction of full-scale trial pavement sections, as a matter of routine practice, would be too time consuming, it is anticipated that calibration would rely on extensive laboratory testing. Further, it is expected that the transfer functions calibrated with the new methods could be used in the AASHTO M-E framework.
AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) and accompanying
software (AASHTOWare© Pavement ME Design) represents a major improvement in
structural pavement design based on decades of research and development. While the MEPDG has significant advantages
over its predecessor (1993 AASHTO Design Guide) it is still limited by the
empirical transfer functions which require verification, calibration and
validation. The initial national
calibration performed on Long-Term Pavement Performance (LTPP) sections
represents relatively old sections that did not include more modern materials
and technologies. Therefore, local
calibration is recommended by AASHTO, even for conventional materials, and
ultimately required for any new materials that have yet to be evaluated.
typically involves laboratory testing and field study. Under NCHRP 1-37A and 1-40 projects the MEPDG
was “globally” calibrated across LTPP sites in North America. As documented in the AASHTO Guide for Local
Calibration of the MEPDG (AASHTO, 2010), the procedure followed an 11-step
process that is effective, provided sufficient field-sections are
available. The problem, of course, with
new materials and technologies is that there is insufficient (if any) field
data available which forces more reliance on laboratory-derived data.
of transfer functions has historically focused on bottom-up fatigue cracking
through the bending beam fatigue test.
This approach has been used extensively since the 1960s (e.g., Monismith,
1969), but it has been well-recognized that lab-to-field shift factors are
required to make accurate cracking performance predictions.
efforts have examined other forms of testing to predict top-down and reflective
cracking such as Energy Ratio (Roque et al., 2004), Texas Overlay Test (Zhou
and Scullion, 2005), Semi-Circular Bend Test (Cooper III et al., 2016) and the
Illinois Flexibility Index Test (Ozer et al., 2016), to name a few. Some of these approaches were developed
primarily as a screening tool for use in mix design. However, there is potential for using them as
a predictor of performance for structural design purposes and could be
considered in this study.
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