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Unpublished Paper
Sensitivity Evaluation of MEPDG Performance Prediction
InTrans Project Reports
  • Charles W. Schwartz, University of Maryland - College Park
  • Rui Li, University of Maryland - College Park
  • Sunghwan Kim, Iowa State University
  • Halil Ceylan, Iowa State University
  • Kasthurirangan Gopalakrishnan, Iowa State University
Document Type
Report
Publication Date
12-1-2011
Abstract
This report documents and presents the results of a study to evaluate the sensitivity of pavement performance predicted by the Mechanistic-Empirical Pavement Design Guide to the values of the design inputs. Global sensitivity analyses were performed for five pavement types under five climate conditions and three traffic levels. Design inputs evaluated in the analyses included traffic volume, layer thicknesses, material properties (e.g., stiffness, strength, HMA and PCC mixture characteristics, subgrade type), groundwater depth, geometric parameters (e.g., lane width), and others. Detailed traffic inputs were not considered. Depending on the base case, approximately 25 to 35 design inputs were evaluated in the analyses. Correlations among design inputs (e.g., between PCC elastic modulus and modulus of rupture) were considered where appropriate. A normalized sensitivity index defined as the percentage change of predicted distress relative to its design limit caused by a given percentage change in the design input. The analyses found that, for all pavement types and distresses, the sensitivities of the design inputs for the bound surface layers were consistently the highest. Additional findings are also reported for each specific pavement type.
Copyright Owner
University of Maryland and Iowa State University
Report Number
NCHRP Project 1-47
Granting Agencies

National Cooperative Highway Research Program, Transportation Research Board of the National Academies

Language
en
File Format
application/pdf
Citation Information
Charles W. Schwartz, Rui Li, Sunghwan Kim, Halil Ceylan, et al.. "Sensitivity Evaluation of MEPDG Performance Prediction" (2011)
Available at: http://works.bepress.com/halil_ceylan/16/