New Methods for Predicting Critical Tensile Strains in an M-E Framework

Abstract

As mechanistic-empirical pavement design comes to the forefront of design, the accurate prediction of critical tensile strains at the bottom of the asphalt concrete (AC) becomes increasingly more important to predict fatigue cracking. Current methods of predicting tensile strains typically rely solely on dynamic modulus (|E*|) of the AC to predict strain from which pavement performance is predicted. In doing so, vehicle speed is represented by loading frequency. However, a relationship between loading frequency in the lab and vehicle speed in the field has yet to be fully validated. As a result tensile strains are inaccurately predicted, leading to erroneous performance predictions and potential for inefficient pavement designs. A potential solution is to calibrate AC modulus to field-measured strain. This investigation pairs field measured strain from instrumented test sections at the National Center for Asphalt Technology (NCAT) Test Track from both the 2006 and 2009 test cycles with the modulus required to achieve those strains using a Layered Elastic Analysis (LEA) method. These sections represent a wide variety of AC mixtures including reclaimed asphalt pavement (RAP), warm-mix asphalt (WMA), high RAP, RAP and WMA combinations, highly polymer modified asphalt, sulfur-modified warm-mix and other, more conventional, mixtures. Utilizing the method for development of a master curve outlined in AASHTO PP 61-09, a field-based master curve was developed for the calibrated modulus using vehicle speed rather than loading frequency. Based on this sigmoidal fit function, a model was then developed to predict modulus using vehicle speed, shape parameters for the |E*| master curves for the associated mixtures, as well as shape parameters for |G*| for the associated binders, gradation and volumetrics of the mixtures involved. This model enables the prediction of critical tensile strain within LEA at a given pavement temperature and vehicle speed.