Field calibration and validation of a pavement aging model

ABSTRACT

A pavement aging model (PAM) is established by calibrating and validating the NCHRP 09–54 kinetics model's predictions against field core measurements. Field cores, laboratory-mixed loose mixtures, and binders from a wide range of pavement sections are used, including both conventional hot mix asphalt (HMA) mixtures and modern materials (i.e. reclaimed asphalt pavement (RAP), warm mix asphalt (WMA), and polymer modified asphalt (PMA)). Field aging levels are measured at different depths from binder extracted and recovered from in-service field cores. The original component materials of these pavement sections are aged in the laboratory and used to calibrate the kinetics model. The measured field aging levels are compared against those predicted from the kinetics model to inform a depth and time dependent field calibration function for the PAM. The validation of PAM using an independent set of field sections shows that, within the limited data used in this study, the calibrated PAM may be able to predict the aging of the conventional HMA, RAP, WMA, and PMA mixtures. The PAM predictions are lastly found to outmatch the predictions by the Global Aging System (GAS) model.

KEYWORDS:

• Asphalt
• long-term aging
• model
• pavement
• kinetics
• pavement aging model

Acknowledgments

The authors would like to acknowledge the National Cooperative Highway Research Programme for providing funding for this study. The authors also sincerely thank Ern-Yeong Song, Sarah Yim, and Christina Lee for their assistance in conducting micro-extraction and recovery that resulted in the data used in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Academies of Sciences, Engineering, and Medicine; Transportation Research Board; National Cooperative Highway Research Program [Project 09-54: Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction].