Spelling suggestions: "subject:"seismic slope stability"" "subject:"eismic slope stability""
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Influence of ground motion selection on computed seismic sliding block displacementPeterman, Breanna Rose 11 September 2014 (has links)
Seismic slope stability is often evaluated via permanent displacement analyses, which quantify the cumulative, downslope displacement of a sliding mass subjected to earthquake loading. Seismic sliding block displacements provide a useful index as to the seismic performance of a slope. Seismic sliding block displacements can be computed for a suite of acceleration-time histories selected to fit a design event.
This thesis explores the effect of ground motion selection on computed seismic sliding block displacements through two approaches. First, rigid sliding block displacements were computed for ground motion suites developed to fit uniform hazard spectra (UHS), conditional mean spectra (CMS), and conditional probability distributions for peak ground velocity (PGV) and Arias Intensity (Ia). Evaluation of the suites in terms of their PGV and Ia distributions provided useful insight into the relative displacements computed for the suites. The PGV and Ia distributions of the suite selected to fit the UHS exceed the theoretical distributions of these ground motion parameters. In fact, the scaled Ia values of motions in the UHS suite are greater than the largest Ia values in the Next Generation Attenuation (NGA) ground motion database. As such, the displacements computed for the UHS suite exceed the displacements computed for any other suite. If only two ground motion parameters are to be considered in ground motion selection we recommend those parameters be PGA and PGV. However, it is important to consider PGA, PGV, and Ia when developing ground motion suites for permanent displacement analyses.
Next, the use of simulated ground motions for permanent displacement analyses was addressed by comparing displacements computed for simulated ground motions to displacements computed for the corresponding recorded ground motion. Simulated ground motions generated via four seismological models were considered: the deterministic Composite Source Model (CSM), the stochastic model EXSIM, the deterministic-stochastic hybrid model by Graves and Pitarka (GP), and the deterministic-stochastic hybrid model developed at San Deigo State University (SDSU). The displacements computed for the SDSU simulations were the most similar to those computed using the recorded motions, with the average displacement of the SDSU simulations exceeding that of the corresponding recorded ground motion by about 6%. Additionally, the displacements from the SDSU simulations provided the smallest variability about the displacements computed for the recorded motions. / text
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A comparison of methodologies used to predict earthquake-induced landslidesDreyfus, Daniel Kenoyer 07 July 2011 (has links)
The rigid sliding-block analysis introduced by Newmark in 1965 has become a popular method for assessing the stability of slopes during earthquakes. Estimates of sliding displacement calculated using this methodology serve as an index of seismic performance and are used for mapping seismic landslide hazard potential. The original approach of rigorously integrating ground acceleration time-histories to compute estimates of sliding displacement has been replaced by the use of simple, empirical models that predict displacement as a function of a slope's yield acceleration and one or more measures of ground shaking. To be useful the results of these models must be compared with observations of landslides from previous earthquakes.
Seven different empirical models were evaluated by comparing predicted displacements with an inventory of observed landslides from the 1994 Northridge, California earthquake. Using a comprehensive set of ground motion data and shear strength properties from the Northridge earthquake, sliding displacements were calculated within a geographic information system (GIS) and the accuracy of each model was computed. The influence of factors such as landslide size, geologic unit, slope angle, and material strength on the prediction of landslides was also evaluated. The results were used to show that the accuracy of the predictive models depends less on the model used and more on the uncertainty in the model parameters, specifically the assigned shear strength values. Because current approaches do not take into account the spatial variability of strength within individual geologic units, the accuracy of the predictive models is controlled by the distribution of slope angles within observed and predicted landslide cells. Assigning overly conservative (low) shear strength values results in a higher percentage of landslides accurately identified, but also results in a large over-estimation of the seismic landslide hazard. / text
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