INTEGRATING INSTRUMENTATION DATA IN PROBABILISTIC PERFORMANCE PREDICTION OF FLEXIBLE PAVEMENTS

Open Access
Author:
Yin, Hao
Graduate Program:
Civil Engineering
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
September 10, 2007
Committee Members:
  • Shelley Marie Stoffels, Committee Chair
  • Mansour Solaimanian, Committee Member
  • Ghassan Chehab, Committee Member
  • Charles Edward Antle, Committee Member
Keywords:
  • MEPDG
  • Monte Carlo simulation
  • flexible pavement
  • instrumentation
  • performance prediction
  • probabilistic
  • finite element analysis
  • viscoelastic
  • variability analysis
Abstract:
The goal of this research was to develop a methodology integrating instrumentation data with existing mechanistic-empirical performance models for flexible pavements. The methodology is further enhanced with probabilistic features that take into account the uncertainties associated with design parameters. Two types of pavement structures are considered: 1) full-depth structures, including subbase, base, and Superpave-designed HMA layers constructed over subgrade and 2) structural overlays including only Superpave-designed HMA layers. One pavement section per structure type was selected from the instrumented sections of a comprehensive research project called the Superpave In-Situ Stress/Strain Investigation (SISSI), sponsored by Pennsylvania Department of Transportation. The first task of this project was to simulate pavement response using 3-D viscoelastic-based finite element models. A sensitivity analysis was then conducted to identify site-specific parameters that are required by empirical performance models. The variabilities associated with these parameters were quantified and further considered in a Monte Carlo simulation-based probabilistic approach. The predicted performance measures included the overall pavement functional performance (IRI) and structural performance in terms of individual distresses over a specified analysis period. The main contribution of this research is not toward the development of new performance prediction models but, rather, the demonstration of the use of instrumentation data for performance predictions. The developed methodology is enhanced with an analytical method to predict pavement responses over time and thus will be ideally suited for situations where sophisticated instrumentation data are not available. In addition, the probabilistic nature of the developed methodology proposes a unique way of assessing the effects of variabilities of design parameters on pavement performance.