Soil moisture is a key variable in off-road mobility. Mobility analysis was conducted based on three soil moisture sources: WindSat (a satellite), LIS (a computer model), and in situ ground sensors (assumed to represent ground truth). Mobility of six vehicles, each with different ranges of sensitivity to soil moisture, was examined in three test sites. Two methods were used: a simplified method based on time series and a fulleatured terrain method. The results demonstrated that the effect of the soil moisture error on mobility predictions is complex and may produce very significant errors in mobility analysis for certain combinations of vehicles, seasons, and climates. Soil moisture biases vary in both direction and magnitude with season and location. Furthermore, vehicles are sensitive to different ranges of soil moistures. In the wet season, differences in soil strength resulted in more significant differences in mobility predictions than in the dry season.

Existing minefields left over from previous conflicts pose a grave threat to humanitarian relief operations, domestic everyday life, and future military operations. The remaining minefields in Afghanistan, from the decade long war with the Soviet Union, are just one example of this global problem. The purpose of this research is to develop a methodology that will predict areas where minefields are the most likely to exist through use of a GIS model. The concept is to combine geospatial data layers to produce a scored raster output of the most likely regions where minefields may exist. It is a "site suitability analysis" for minefield existence. The GIS model uses elevation and slope data, observer and defensive position locations, hydrographic features, transportation features, and trafficability estimates to form a minefield prediction surface. Through use of the NATO Reference Mobility Model (NRMMII) and the Digital Topographic Support System (DTSS), trafficability estimates are generated for specific vehicles under specific terrain and weather conditions in specific areas of interest. The model could be used to create prioritized maps for minefield detection sensors, demining teams, or for avoidance. These maps could define the "high payoff" search areas for remote sensors, such as ASTAMIDS, and positively identify minefields. These maps could also be used by humanitarian relief agencies for consideration when planning movement into areas that may contain minefields. The analysis includes a model calibration and sensitivity analysis procedure and compares the model output to known training minefield locations taken from two US Army training centers. The resultant Minefield Likelihood Surface has a 91% accuracy rate when compared to known training minefield data. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / A procedure for calibrating a discrete element (DE) computational soil model for various moisture contents using a conventional Asperity-Spring friction modeling technique is presented in this thesis. The procedure is based on the outcomes of two physical soil experiments: (1) Compression and (2) unconfined shear strength at various levels of normal stress and normal pre-stress. The Compression test is used to calibrate the DE soil plastic strain and elastic strain as a function of Compressive stress. To calibrate the DE inter-particle friction coefficient and adhesion stress as a function of soil plastic strain, the unconfined shear test is used. This thesis describes the experimental test devices and test procedures used to perform the physical terramechanical experiments. The calibration procedure for the DE soil model is demonstrated in this thesis using two types of soil: sand-silt (2NS Sand) and silt-clay(Fine Grain Soil) over 5 different moisture contents: 0%, 4%, 8%, 12%, and 16%. The DE based models response are then validated by comparing them to experimental pressure-sinkage results for circular disks and cones for those two types of soil over 5 different moisture contents. The Mean Absolute Percentage Error (MAPE) during the compression calibration was 26.9% whereas during the unconfined shear calibration, the MAPE was calculated to be 11.38%. Hence, the overall MAPE was calculated to be 19.34% for the entire calibration phase.

<p>A procedure for calibrating a discrete element (DE) computational soil model for various moisture contents using a conventional Asperity-Spring friction modeling technique is presented in this thesis. The procedure is based on the outcomes of two physical soil experiments:</p> <p>(1) Compression and (2) unconfined shear strength at various levels of normal stress and normal pre-stress. The Compression test is used to calibrate the DE soil plastic strain and elastic strain as a function of Compressive stress. To calibrate the DE inter-particle friction coefficient and adhesion stress as a function of soil plastic strain, the unconfined shear test is used. This thesis describes the experimental test devices and test procedures used to perform the physical terramechanical experiments. The calibration procedure for the DE soil model is demonstrated in this thesis using two types of soil: sand-silt (2NS Sand) and silt-clay(Fine Grain Soil) over 5 different moisture contents: 0%, 4%, 8%, 12%, and 16%. The DE based models response are then validated by comparing them to experimental pressure-sinkage results for circular disks and cones for those two types of soil over 5 different moisture contents. The Mean Absolute  Percentage Error (MAPE) during the compression calibration was 26.9% whereas during the unconfined shear calibration, the MAPE was calculated to be 11.38%. Hence, the overall MAPE was calculated to be 19.34% for the entire calibration phase.</p>