A Mechanistic-Empirical Pavement Design Guide (MEPDG) was developed under NCHRP Project 1-37A to address the shortcomings of empirical pavement design methods. The MEPDG uses mechanistic-empirical models to analyze the impacts of traffic, climate, materials and pavement structure and to predict long term performances of pavement. The MEPDG software (AASHTOWare Pavement M-E) use a three-level hierarchical input scheme to predict pavement performance in terms of terminal International Roughness Index (IRI), Permanent Deformation , Total Cracking (Reflective and Alligator), Asphalt Concrete (AC) Thermal Fracture, AC Bottom-Up Fatigue Cracking, and AC Top-Down Fatigue Cracking. Different highway agencies are taking initiatives to adopt MEPDG based pavement design and performance prediction by calibrating the prediction models for their local conditions. However, these inputs with different levels of accuracy may have significant impact on performance prediction and thereby on accuracy of local calibration. This study focuses on the sensitivity of the input parameters of MEPDG distresses to identify the effect of the accuracy level of input parameters based on orthogonal experimental design. A local sensitivity analysis is carried out by using Ontario’s default value and historical performance record of Ontario highway system. Sensitive input parameters are evaluated through a multiple regression analysis for respective distresses. It is found that terminal IRI is sensitive to initial IRI, initial permanent deformation, and milled thickness in asphalt layer; permanent deformation is sensitive to initial permanent deformation, subgrade resilient modulus, and traffic load; top down fatigue cracking is sensitive to AC effective binder content, and AC air voids. Based on the independent influence of these sensitive inputs, the requirement of accuracy level will be identified for MEPDG design.
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