Proposed Enhancements to Pavement ME Design: Improved Consideration of the Influence of Subgrade and Unbound Layers on Pavement Performance (2019) / Chapter Skim
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Pages 126-130
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126 CHAPTER 6. SUMMARY CONCLUSIONS This project proposed several enhancements to the Pavement ME Design with the purpose of increasing the sensitivity of pavement performance to base layers and subgrade.
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127 Compared to the existing prediction models, such as the Zapata and Perera models, the developed ANN models have the highest accuracy (e.g., smallest RMSE and highest R2 values) to predict the SWCC fitting parameters in the Fredlund-Xing equation.
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128 Modified k-values were compared with the BBF k-values. Significant changes in the k-values were observed due to the base modulus and interface bonding corrections.
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129 Prediction of Pavement Performance and Sensitivity Analysis Based on the sensitivity analysis performed on the pavement models and the comparison of models proposed by researchers and the Pavement ME Design Guide, several findings are presented as follows: As an unbound granular material, the nonlinearity and the anisotropy of the pavement materials should be considered in the pavement design and analysis. With such properties, the modulus of the base and subgrade are sensitive to the moisture content, the loading level, and the pavement structure, which affect the pavement responses and performance.
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130 corresponding modified k-values from ANN model showed much higher sensitivity on calculated stress and deflections compared to the results from Pavement ME Design models. FUTURE WORK AND RECOMMENDATIONS There are several items of future work that emerge from the work that has been accomplished in this project including the following: Replace the models that are currently in the Pavement ME Design software by those that have been developed in this project.
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