A probabilistic approach for evaluating earthquake-induced landslides
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Earthquake-induced sliding displacements are commonly used to assess the seismic performance of slopes. These displacements represent the cumulative, downslope movement of a sliding block due to earthquake shaking. While the sliding block model is a simplified representation of the field conditions, the displacements predicted from this model have been shown to be a useful index of seismic performance of slopes. Current evaluation procedures that use sliding block displacements to evaluate the potential for slope instability typically are based on a deterministic approach or a pseudo-probabilistic approach, in which the variabilities in the expected ground motion and predicted displacement are either ignored or not treated rigorously. Thus, there is no concept of the actual hazard (i.e., the annual probability of exceedance) associated with the computed displacement. This dissertation focuses on quantifying the risk for earthquake-induced landslides. The basic approach involves a probabilistic framework for computing the annual rate of exceedance of different levels of sliding displacement for a slope such that a hazard curve for sliding displacement can be developed. The framework incorporates the uncertainties in the prediction of earthquake ground shaking, in the prediction of sliding displacement, and in the assessment of soil properties. The framework considers two procedures that will yield a displacement hazard curve: the scalar hazard approach that utilizes a single ground motion parameter and its associated hazard curve to compute permanent sliding displacements; and a vector hazard approach that predicts displacements based on two (or more) ground motion parameters and the correlation between these parameters. Current predictive models for sliding displacement provide the expected level of displacement as a function of the characteristics of the slope (e.g., geometry, strength, yield acceleration) and the characteristics of earthquake shaking (e.g., peak ground acceleration, peak ground velocity). However, current models contain significant aleatory variability such that the range of predicted displacements is large. To reduce the variability in the sliding displacement prediction and to provide models appropriate for the presented probabilistic framework, sliding displacement predictive equations are developed that utilize single and multiple ground motion parameters. The developed framework is implemented to the Mint Canyon 7.5-minute quadrangle in California to generate a map of earthquake-induced landslide hazard. Application of the probabilistic procedure to a 7-1/2 minute quadrangle of California is an important exercise to identify potential difficulties in California Geological Survey’s (CGS) current application for hazard mapping, and for the eventual adoption by CGS and USGS.