Probabilistic assessments of the seismic stability of slopes : improvements to site-specific and regional analyses

Earthquake-induced landslides are a significant seismic hazard that can generate large economic losses. Predicting earthquake-induced landslides often involves an assessment of the expected sliding displacement induced by the ground shaking. A deterministic approach is commonly used for this purpose. This approach predicts sliding displacements using the expected ground shaking and the best-estimate slope properties (i.e., soil shear strengths, ground water conditions and thicknesses of sliding blocks), and does not consider the aleatory variability in predictions of ground shaking or sliding displacements or the epistemic uncertainties in the slope properties. In this dissertation, a probabilistic framework for predicting the sliding displacement of flexible sliding masses during earthquakes is developed. This framework computes a displacement hazard curve using: (1) a ground motion hazard curve from a probabilistic seismic hazard analysis, (2) a model for predicting the dynamic response of the sliding mass, (3) a model for predicting the sliding response of the sliding mass, and (4) a logic tree that incorporates the uncertainties in the various input parameters. The developed probabilistic framework for flexible sliding masses is applied to a slope at a site in California. The results of this analysis show that the displacements predicted by the probabilistic approach are larger than the deterministic approach due to the influence of the uncertainties in the slope properties. Reducing these uncertainties can reduce the predicted displacements. Regional maps of seismic landslide potential are used in land-use planning and to identify zones that require detailed, site-specific studies. Current seismic landslide hazard mapping efforts typically utilize deterministic approaches to estimate rigid sliding block displacements and identify potential slope failures. A probabilistic framework that uses displacement hazard curves and logic-tree analysis is developed for regional seismic landslide mapping efforts. A computationally efficient approach is developed that allows the logic-tree approach to be applied for regional analysis. Anchorage, Alaska is used as a study area to apply the developed approach. With aleatory variability and epistemic uncertainties considered, the probabilistic map shows that the area of high/very high hazard of seismic landslides increases by a factor of 3 compared with a deterministic map.