After the implementation of the Mechanistic-Empirical Pavement Design Guide (MEPDG) in Indiana, an overall evaluation of the stiffness characteristics of local AC mixtures and the ability of level III MEPDG predictive equations to estimate dynamic modulus (E*) with local mixtures was required. Therefore, the primary objectives of this study were to identify significant differences among Indiana asphalt mixtures, to evaluate the performance of commonly used E* predictive models, and to assess the influence of level III E* input on the pavement design life of typical pavement structures. It was found that Indiana mixtures do not show extensive variability among mixtures having the same nominal maximum aggregate size. When conducting a statistical analysis to group asphalt mixtures having similar characteristics, few mixtures were left out of the groups. In general, it was observed that mixtures having Ndes equal to 75, showed the lowest E* values along the entire frequency range. The Witczak 1-37A showed the most accurate and less biased E* predictions for Indiana mixtures. It showed the highest R2, and the least deviation from the measured E* values. However, predicted E* input values produced higher levels of pavement distress compared with measured E* values, indicating general overprediction. Besides, using level III (predictive) rather than level I (measured) E* input values can influence the pavement thickness design due to the functional performance (i.e., the International Roughness Index (IRI)). When a structural performance (i.e., bottom-up cracking) was taken into consideration, no influence of the E* input type on the design AC layer thickness was observed.


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    Title :

    Evaluating Dynamic Modulus for Indiana Mechanistic-Empirical Pavement Design Guide Practice


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2019-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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