Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Civil and Environmental Engineering

Committee Chair

John P. Zaniewski.


The Mechanistic-Empirical Pavement Design Guide (MEPDG) was the result of NCHRP project 1-37A. This is a mechanistic-empirical pavement design procedure which uses mechanistic and empirical models, nationally calibrated with information from several databases, especially the national Long-Term Pavement Performance (LTPP) study. This database includes data from pavements located throughout North America.;A large amount of inputs are required in order to perform pavement design with MEPDG. These may be classified into traffic loads, material properties and climate input parameters. The pavement distress mechanisms are too complex to be completely modeled without utilization of empirical data. So, calibration is required to improve the accuracy of the models for local conditions. The recommended approach for calibration includes review of the input data, sensitivity analysis, comparative studies, validation and calibration studies, the modification of the input defaults and calibration coefficients, and the verification of the national calibration by collecting a local validation database. The goal of this calibration is to verify that the performance models accurately predict pavement distress and ride quality. Unfortunately, the collection of the data either for calibration or individual designs requires numerous tests to characterize materials, and the field work for collecting the database to verify the models is laborious.;The main goal of this research is the determination of the most important parameters in the MEPDG. However, MEPDG is so complex and the input parameters are so numerous that the sensitivity analysis methodology must be carefully designed to identify the relative importance of each input variable. This research used space-filling computer experiments with Latin hypercube sampling, standardized regression coefficients, and Gaussian stochastic processes to categorize the relative importance of the flexible pavement performance parameters in MEPDG. The use of these statistical techniques allows analysis of the entire space of the input parameters. Additionally, this project studied the feasibility of this methodology for sensitivity analysis of the MEPDG.