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Predicting unsaturated soil strength for mobility assessments


Accurate estimation of surficial soil moisture and soil strength is integral in the determination of vehicle mobility across landscapes for applications from agriculture to national defense. Especially important is the ability to estimate trafficability over large spatial extents at fine resolutions (10-30 m, or finer, grid cells). While methods exist to estimate soil strength across landscapes, these methods are empirical and rely on class average soil behavior or field measurements that are often difficult or impossible to acquire. In addition, modern terramechanics models require moisture-variable soil strength parameters (e.g., friction angle and cohesion) that cannot be easily acquired in the field. To tackle this issue, the Strength of Surficial Soils (STRESS) model was developed to estimate moisture-variable soil strength with a physics-based approach rooted in unsaturated soil mechanics. However, there has been a lack of field soil moisture and soil strength data from a spatially diverse landscape with which to evaluate the STRESS model. To test the STRESS model, a field study was conducted at the 4,000 ha Maxwell Ranch in the northern Colorado foothills. Soil moisture and soil strength were determined with HydraProbes and cone penetrometers, respectively, at 86 locations across the ranch on 10 dates from May to August 2022. The data were then used to test the STRESS model and determine if soil strength trends could be estimated from topographical and soil textural differences across the landscape. High variability was observed in soil strength measurements via field rating cone index (RCI) stemming from fine-scale terrain and soil features as well as variability in cone penetrometer use. Observed trends show lower soil strengths for greater soil moistures, steeper slopes, higher vegetation, and lower soil fines content. The STRESS model was able to estimate field RCI values with a mean relative error of 37.5%, while a pre-existing model had a mean relative error of 47.4%. The STRESS model was able to reproduce strength trends with fines content but failed to reproduce vegetation and topographical trends. Thus, the STRESS model outperforms the current RCI prediction method, but the uncertainty in the predictions remains large.


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soil strength
geotechnical engineering


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