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

A fragility function quantifies the probability that a structural system reaches an undesirable limit state, conditioned on the occurrence of a hazard of prescribed intensity level. Multiple sources of uncertainty are present when estimating fragility functions, e.g., record-to-record variation, uncertain material and geometric properties, model assumptions, adopted methodologies, and scarce data to characterize the hazard. Advances in the last decades have provided considerable research about parameter selection, hazard characteristics and multiple methodology for the computation of these functions. However, there is no clear path on the type of methodologies and data to ensure that accurate fragility functions can be computed in an efficient manner. Fragility functions are influenced by the selection of a methodology and the data to be analyzed. Each selection may lead to different levels of accuracy, due to either increased potential for bias or the rate of convergence of the fragility functions as more data is used. To overcome this difficulty, it is necessary to evaluate the level of agreement between different statistical models and the available data as well as to exploit the information provided by each piece of available data. By doing this, it is possible to accomplish more accurate fragility functions with less uncertainty while enabling faster and widespread analysis. In this dissertation, two methodologies are developed to address the aforementioned challenges. The first methodology provides a way to quantify uncertainty and perform statistical model selection to compute seismic fragility functions. This outcome is achieved by implementing a hierarchical Bayesian inference framework in conjunction with a sequential Monte Carlo technique. Using a finite amount of simulations, the stochastic map between the hazard level and the structural response is constructed using Bayesian inference. The Bayesian approach allows for the quantification of the epistemic uncertainty induced by the limited number of simulations. The most probable model is then selected using Bayesian model selection and validated through multiple metrics such as the Kolmogorov-Smirnov test. The subsequent methodology proposes a sequential selection strategy to choose the earthquake with characteristics that yield the largest reduction in uncertainty. Sequentially, the quantification of uncertainty is exploited to consecutively select the ground motion simulations that expedite learning and provides unbiased fragility functions with fewer simulations. Lastly, some examples of practices during the computation of fragility functions that results i n undesirable bias in the results are discussed. The methodologies are implemented on a widely studied twenty-story steel nonlinear benchmark building model and employ a set of realistic synthetic ground motions obtained from earthquake scenarios in California. Further analysis of this case study demonstrates the superior performance when using a lognormal probability distribution compared to other models considered. It is concluded by demonstrating that the methodologies developed in this dissertation can yield lower levels of uncertainty than traditional sampling techniques using the same number of simulations. The methodologies developed in this dissertation enable reliable and efficient structural assessment, by means of fragility functions, for civil infrastructure, especially for time-critical applications such as post-disaster evaluation. Additionally, this research empowers implementation by being transferable, facilitating such analysis at community level and for other critical infrastructure systems (e.g., transportation, communication, energy, water, security) and their interdependencies.