Technical Papers
Sep 14, 2011

Unified Approach for Uncertainty Analysis Using the AASHTO Mechanistic-Empirical Pavement Design Guide

Publication: Journal of Transportation Engineering
Volume 138, Issue 5

Abstract

This paper addresses the problem of uncertainty management for the Mechanistic-Empirical Pavement Design Guide (MEPDG). Specifically, development of an all-inclusive approach to uncertainty management in which model uncertainty and input parameter variability are taken into consideration is presented in this paper. A Gaussian process surrogate model is chosen in this paper as a potential model for the MEPDG design software, and model verification metrics are included to assess the ability of the surrogate model to predict MEPDG output. The construction of a surrogate model that emulates the MEPDG will reduce the computational expense for uncertainty analysis but will introduce another source of uncertainty. Uncertainty propagation of these major sources of uncertainty is performed for a case study. The relative effects of these three major sources of uncertainty are investigated, and the influences of these sources of uncertainty are discussed utilizing the results from the uncertainty propagation. Reliability analysis incorporates the proposed methodology to investigate the effect of these sources of uncertainty for a typical flexible pavement design.

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Information

Published In

Go to Journal of Transportation Engineering
Journal of Transportation Engineering
Volume 138Issue 5May 2012
Pages: 657 - 664

History

Received: Apr 15, 2011
Accepted: Sep 12, 2011
Published online: Sep 14, 2011
Published in print: May 1, 2012

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Authors

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Jennifer Q. Retherford, S.M.ASCE [email protected]
P.E.
Graduate Student, Vanderbilt Univ., Dept. of Civil and Environmental Engineering, VU Station B 351831, Nashville, TN 37235 (corresponding author). E-mail: [email protected]
Mark McDonald, M.ASCE [email protected]
Professor, Vanderbilt Univ., Dept. of Civil and Environmental Engineering,VU Station B 351831, Nashville, TN 37235. E-mail: [email protected]

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