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Minnesota counties and the Minnesota Department of Transportation (MnDOT) use the Dynamic Cone Penetrometer (DCP) and the Lightweight Deflectometer (LWD) for in situ evaluation of stiffness and strength of soil and aggregate bases. The in situ test of choice (DCP or LWD) varies somewhat by county and region, depending partly on the local soil conditions and partly on historical preferences. The LWD is considered a measure of modulus while the DCP is considered a measure of shear strength. Recent field and laboratory tests have provided calibration for these tests for several specific granular samples. However, the results are likely less reliable for a broader range of potential granular materials used for granular bases. The objective of this research is to build on a mechanistic model developed for dry aggregate bases under LRRB INV 850 to increase its applicability to more materials and tests used in Minnesota. There were three primary thrusts to these new additions: (1) A model for the LWD test has been added so that computational predictions for DCP tests could be compared with those from LWD tests; (2) Particle-scale models for moisture and fine particle content have been included for the user to input these among the other existing material input parameters, and (3) Analogous algorithms have been developed for the DCP and LWD tests to be used with PFC3D, a commercial code maintained by Itasca Consulting Group.

Several tests are used for characterizing unbound granular materials for pavement applications. The California Bearing Ratio (CBR), resilient modulus (MR), Dynamic Cone Penetrometer (DCP) tests are three of the most common tests used for this purpose. The objective of this research is twofold. The first is to develop numerical models for these three tests. The second is to investigate relationship between basic material properties, boundary conditions, and test results, ultimately, to develop a physics-based correlation between these tests. A 3-D discrete element method (DEM) based model is adapted to simulate these tests. Good agreement is observed between the results of the simulations and sample numerical and experimental studies on granular materials. The DEM code is used to determine effects of aggregate shape, coefficient of friction, gradation, stiffness and other details on test results. The model is also used to investigate statistics of inter-particle interaction between the granular particles.

Compaction of asphalt mixtures represents a critical step in the construction process that significantly affects the performance and durability of asphalt pavements. In this research effort; the compaction process of asphalt mixtures was investigated using a combined experimental and computational approach. The primary goal was to understand the main factors responsible for achieving good density and was triggered by the success of a recently proposed Superpave 5 mix design method. First; a two-scale discrete element method (DEM) model was developed to simulate the compaction process of asphalt mixtures. The computational model was anchored by a fluid dynamics-discrete element model; which is capable of capturing the motion of aggregates in the viscous binder. The model was then calibrated and validated by a series of experiments; which included rheological tests of the binder and a compaction test of the mixture. It was concluded that the compaction process was significantly influenced by the rheological properties of the fine aggregate matrix and by the sphericity of the coarse aggregates. Finally; the mechanical properties of two high-density mixtures were determined and compared with mechanical properties of mixtures used for MnROAD 2017 National road Research Alliance (NRRA) test sections. It was found that the properties of high-density mixtures as a group were not significantly different compared to the properties of conventional mixtures.