Thermal cracking is the predominant flexible pavement distress in northern climates, causing transverse cracking perpendicular to the direction of traffic. The indirect tensile (IDT) strength test is currently the most widely used method to characterize thermal cracking susceptibility and is an input to the Pavement ME Design software (formerly known as the Mechanistic- Empirical Design Guide (MEPDG). In Pavement ME Design when laboratory IDT strength testing data is not available to designers it is predicted using mixture volumetrics and performance grade (PG) of the binder. The purpose of this research is to examine the IDT strength characteristics of flexible pavement mixtures commonly used by the Michigan Department of Transportation (MDOT) and to develop improved prediction methods for IDT strength. Laboratory testing of 62 unique MDOT mixtures showed Pavement ME Design software generally over predicted IDT strength. Three models where developed to improve the accuracy of IDT strength prediction. The first model consists of local calibration of the current IDT strength Pavement ME Design predictive model for MDOT mixes. The second model consists of a new statistical model developed based on job mix information to predict low temperature IDT strength. The third model consists of an artificial neural network developed to predict low temperature strength from job mix information. All three models showed increased prediction performance when compared to Pavement ME Design IDT strength prediction. With these models a more accurate low temperature prediction of IDT strength is available to pavement designers in Michigan using readily available job mix information.
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