Automating Mechanistic-Empirical Pavement Design Calibration Studies

Accelerated pavement testing (APT) applies intensive traffic loading for the purpose of observing long-term performance of pavements in a short time. The testing is beneficial for researchers as a timely and valuable data resource. As Mechanistic-Empirical (M-E) pavement design approaches emerge as a favorable alternative to traditional empirical approaches, there is a need for conducting calibration of the performance models in the M-E design framework. Data from APT testing may be considered advantageous for calibration due to research-grade accuracy conducted over shorter time intervals, but even so, M-E calibration is still time-consuming because many combinations of the respective calibration coefficients need to be evaluated against observed performance from pavement sections. The current version of the AASHTOWare™ Pavement ME Design software, though serving as a powerful pavement design and analysis tool, is not necessarily conducive to calibration studies as many interactions are needed by the user to populate the software with trial calibration coefficients and extract performance predictions after simulations have been completed. This study proposes using automation to facilitate calibration studies whereby the repetitive manual operations required by the software are recorded and built into a macro that greatly reduces the required human interactions. The software automation is demonstrated through a calibration study conducted at the National Center for Asphalt Technology (NCAT) Test Track that examined fatigue cracking calibration. The efficiency of the automated approach is compared against conducting calibration requiring human interaction with the software.