Performance-based probabilistic assessment of liquefaction-induced building settlements
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Shallow founded buildings on liquefiable soils may suffer significant settlement during earthquake loading. The damage observed during the Canterbury earthquake sequence in New Zealand and the Maule earthquake in Chile are good examples of the amount of damage that can be caused due to liquefaction in buildings with shallow foundations. Earthquake scenarios similar to the ones in the Canterbury earthquake sequence are often used for design in the United States. Hence the proper estimation of liquefaction-induced building settlements (Ds) is of primary importance for areas in the U.S. affected by earthquakes (e.g. the Pacific Northwest, the Cascadia subduction zone, the New Madrid seismic zone, etc.). Existing procedures to estimate Ds are formulated under a deterministic or pseudo-probabilistic framework where there is not a quantification of the existing hazard for Ds, and only the hazard associated with ground motion intensity measure parameters is considered. The quantification of the Ds hazard would allow more informed decisions in engineering practice, because the hazard is directly quantified and related to the amount of Ds. In Addition, a Ds hazard quantification is fully consistent with performance-based engineering concepts because the engineering design of a geotechnical system can now be directly related to the hazard in Ds and not only to the hazard in a ground motion parameter. This study focuses on the implementation of a performance- based, hazard-consistent framework for the estimation of Ds. The following components, which are currently not available in engineering practice, for a performance-based estimation of Ds are developed in this study: (1) new conditional ground motion models and scenario based models for the cumulative absolute velocity (CAV), which is a primary intensity measure parameter to estimate Ds, (2) coefficients of correlation between CAV and spectral accelerations, which are necessary for performance-based implementations, and (3) performance-based implementation of Ds models that are currently used in deterministic or pseudo-probabilistic approaches. Finally, this study offers examples of the application of the developed procedures for the performance-based estimation of Ds in engineering practice considering 3 different sites in the United States (California, Salt Lake, Seattle).