United States Department of Transportation
i
Development of a Mechanistic-Based Design Method for Geosynthetic-Reinforced Pavement on Expansive Soils and Prediction of Moisture Content Fluctuations in Subgrades
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2018-10-05
[PDF-1.80 MB]
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Edition:Final Report April 2017 – June 2018
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Abstract:A methodology has been developed to compute bending moments and shear forces, etc., in geosynthetic-reinforced pavement on expansive soils. The geosynthetic-reinforced pavement, subjected to the heave/shrinkage-induced vertical displacement caused by the volume change of expansive subgrade soils, was formulated using the Timoshenko beam theory. The virtual load method (VLM) was developed by applying a virtual load on the pavement to make the beam deflection equivalent to the heave/shrinkage-induced vertical displacement. The unknown virtual load was expressed as a Fourier series, and the Fourier constants were determined by employing the inverse theory for the identification of material parameters. As a case study, the virtual load method was applied to investigate the effect of geosynthetics on there search road FM 2 near the city of College Station in Texas, USA. The geosynthetics in the pavement functioned as a reinforcement to reduce the pavement damage caused by the seasonal swell and shrinkage of the expansive subgrade soils. This research may help develop a proper and implementable design and construction method to mitigate the harmful effect of expansive clays on highway pavements. Additionally, numerical prediction was performed for the moisture content fluctuation in expansive subgrade clay due to climate changes. It was completed using commercial software VADOSE/W in analyzing the changes in moisture content over a one-year period. Moisture content fluctuations from the numerical analyses were validated by the long-term moisture content measurements below the pavement of country road FM 2. The research achievements have shown that multiple weather conditions can be considered and integrated while predicting the moisture content fluctuations, which could lead to more accurate heave/shrinkage predictions.
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