Revised Beta Criteria For the CBR Airfield Pavement Design Method

Open Access
Author:
Miller, Eric Jordan
Graduate Program:
Civil Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
November 05, 2012
Committee Members:
  • Shelley Marie Stoffels, Thesis Advisor
  • Dr Prasenjit Basu, Thesis Advisor
  • Mansour Solaimanian, Thesis Advisor
Keywords:
  • CBR
  • california bearing ratio
  • airfield
  • flexible
  • pavement
  • asphalt
  • HMA
  • ACC
  • structural design
Abstract:
The empirical California Bearing Ratio (CBR) design method for flexible airfield pavements has been a mainstay for practicing engineers for decades. Its simplicity and proven performance makes it easy to use and reliable. However, design scenarios for increasingly larger aircraft have revealed a significant design shortfall at these extremely heavy and multi-wheeled loading conditions. This has driven a redevelopment of the CBR method to underpin it with a mechanistic load response of vertical stress at the subgrade surface instead of an equivalent single wheel load at the surface of the pavement. This response is correlated to an empirical failure model of pavement life developed from full-scale test data. This new design method has eliminated limitations in the original procedure that resulted in overdesigned pavements for large aircraft. However, any new method always leaves room for improvement. This thesis explored the development of the current failure model and identified an opportunity to refine it. The primary target of this modification was the concentration factor, an empirical modification to the Boussinesq stress distribution theory used in calculating the load response. The test data used to develop the current failure model was previously analyzed with a constant concentration factor for all test points. However, a more representative relationship of the concentration factor as a function of the subgrade CBR was available to better model soil behavior. After collecting additional test data beyond that used to develop the current failure model, a new failure model was developed that included the concentration factor as a function of CBR. In addition, it was shown, through comparison of nonlinear regression statistics, that a failure model developed using the concentration factor as a function of CBR provided failure model with a better fit to the test data than a model developed with a constant concentration factor.