Prediction of Resilient Modulus From the Laboratory Testing of Sandy Soils
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2019-06-01
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Edition:Final; November 2017–June 2019
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Abstract:As Georgia Department of Transportation (GDOT) is moving toward implementing the Mechanistic–Empirical Pavement Design Guide (MEPDG) and utilizing the AASHTOWare Pavement ME Design software, there is a pressing need to develop a subgrade resilient modulus (MR) database considering Georgia-specific soil conditions in order to achieve a reliable pavement design. Developing a reliable subgrade MR database is key to accurate pavement structure designs. However, establishing a database requires dedicated time and effort for extensive laboratory testing. To meet GDOT’s immediate needs, the researchers built a subgrade MR prediction model based on available GDOT resources (i.e., local soil index properties and existing MR data, etc.). The developed model uses the optimum moisture content as the soil predictor of the resilient modulus. The strength of this variable was confirmed by a random forest model and machine learning analysis. In addition, this study laid the groundwork to develop a performance-based specification (PBS) for subgrade materials. The Indiana Department of Transportation (INDOT) was chosen as an example of a state DOT that has incorporated a PBS for embankment and subgrade construction. A brief discussion of INDOT’s process and test methods is presented with a recommendation to use INDOT as a general model for developing a GDOT PBS for embankment and subgrade construction.
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