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Predicting unsaturated soil strength for mobility assessments

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dc.contributor.authorBullock, Matthew D., author
dc.contributor.authorScalia, Joseph, advisor
dc.contributor.authorNiemann, Jeffrey D., advisor
dc.contributor.authorGallen, Sean, committee member
dc.date.accessioned2024-01-01T11:23:46Z
dc.date.available2024-01-01T11:23:46Z
dc.date.issued2023
dc.description.abstractAccurate 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.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierBullock_colostate_0053N_18006.pdf
dc.identifier.urihttps://hdl.handle.net/10217/237325
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectsoil strength
dc.subjecttrafficability
dc.subjectterramechanics
dc.subjectgeotechnical engineering
dc.titlePredicting unsaturated soil strength for mobility assessments
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineCivil and Environmental Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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