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Landslide analysis with incomplete data: developing a framework for critical parameter estimation
Guido, Lauren E.
Guido, Lauren E.
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2023
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Abstract
Landslides are one of the most common geohazards, posing significant risks for infrastructure, recreation, and human life. Slope stability analyses rely on detailed data, accurate materials testing, and careful model parameter selection. However, required information is not always readily available, and estimations must be made, introducing uncertainty and error to the final slope stability analysis results. The most critical slope stability parameters that are often missing or incompletely constrained include slope topography, depth to water table, depth to failure plane, and material strength parameters. The goal of this research is to develop a standard engineering design approach for the estimation of these parameters when they cannot be directly measured, or when the accuracy of the measured values is in question. It could be argued that even sites that have ample, accurate data would require the estimation of at least one of these critical parameters. Though estimation of these values is common practice, there is limited guidance for this important step in the analysis. This research also quantifies and highlights the need for uncertainty communication and accurate representation of probabilistic approaches in slope stability analysis results, particularly where data is sparse. Guidance is provided for the estimation of the following: original and/or post failure slope topography via traditional methods as well as the use of open-source digital elevation models, water table depth across variable hydrologic settings, and depth to failure plane and slope material properties based on predicted slide type (predominately translational or rotational). Workflows are proposed for the systematic estimation of critical parameters based primarily on slide type and scale. The efficacy of the proposed estimation techniques, uncertainty quantification, and final parameter estimation protocol for data-sparse landslide analysis is demonstrated via application at three landslides of variable sizes in Colorado, USA.
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