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Improving soil property predictions for applications in tailings and terramechanics

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dc.contributor.authorBindner, Joseph R., author
dc.contributor.authorScalia, Joseph, advisor
dc.contributor.authorAtadero, Rebecca, advisor
dc.contributor.authorBareither, Christopher, committee member
dc.contributor.authorNiemann, Jeffrey, committee member
dc.contributor.authorHam, Jay, committee member
dc.date.accessioned2024-09-09T20:52:08Z
dc.date.available2026-08-16
dc.date.issued2024
dc.description.abstractSoil properties are used by engineers and scientists to better understand the state and behavior of soils. For example, soil properties can be used to estimate surficial soil strength for vehicle mobility models and can be used to better understand the engineering characteristics of mine waste (tailings) stored in tailings storage facilities. Soil and tailings properties often have high spatial variability and often require high resolution data for engineering analyses. Standard laboratory procedures are commonly used to determine soil properties but are often impractical for large spatial extents. While some existing soil data products provide estimates of surficial soil properties, the fidelity of soil data products is often poorly understood and insufficient for many applications. Additionally, some field tests used to estimate soil properties, such as the cone penetration test (CPT), rely on empirical correlations that cannot be used for some soils. There remains a need for procedures which improve the speed and accuracy of soil property estimates across large spatial extents. The objectives of this study are to (i) evaluate how surficial soil moisture and soil strength vary with soil and landscape attributes across a large spatial extent, (ii) explore the use of field-based hyperspectral sensing and machine learning for the prediction of surficial soil properties across a landscape, and (iii) assess the use of laboratory hyperspectral sensing and machine learning for the prediction of tailings properties for potential application in situ via direct push methods. Soil and landscape attributes were determined at sampling locations across a semi-arid foothills region and used to assess how soil moisture and soil strength vary with soil and landscape attributes. Then, hyperspectral data were captured at select sampling locations and used to train and assess the performance of a convolutional neural network (CNN) for the predictions of soil properties. Finally, a diverse tailings-hyperspectral dataset was prepared in the lab and used to train and assess a CNN to provide proof of concepts for prediction of material properties relevant to TSF stability analyses.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierBINDNER_colostate_0053A_18472.pdf
dc.identifier.urihttps://hdl.handle.net/10217/239250
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.rights.accessEmbargo expires: 08/16/2026.
dc.subjecthyperspectral sensing
dc.subjecttailings
dc.subjectdiffuse reflectance spectroscopy
dc.subjectterramechanics
dc.subjectmachine learning
dc.titleImproving soil property predictions for applications in tailings and terramechanics
dc.typeText
dcterms.embargo.expires2026-08-16
dcterms.embargo.terms2026-08-16
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.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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