Soil liquefaction and resulting ground failure due to earthquakes presents a significant hazard to distributed infrastructure systems and structures around the world. Currently there is no consensus in liquefaction susceptibility or triggering models. The disagreements between models is a result of incomplete datasets and parameter spaces for model development. The Next Generation Liquefaction (NGL) Project was created to provide a database for advancing liquefaction research and to develop models for the prediction of liquefaction and its effects, derived in part from that database in a transparent and peer-reviewed manner, that provide end users with a consensus approach to assess liquefaction potential within a probabilistic framework. An online relational database was created for organizing and storing case histories which is available at http://nextgenerationliquefaction.org/ (https://www.doi.org/10.21222/C2J040, [1]). The NGL field case history database was recently expanded to include the results of laboratory testing programs because such results can inform aspects of liquefaction models that are poorly constrained by case histories alone. Data are organized by a schema describing tables, fields, and relationships among the tables. The types of information available in the database are test-specific and include processeddata quantities such as stress and strain rather than raw data such as load and displacement. The database is replicated in DesignSafe-CI [2] where users can write queries in Python scripts within Jupyter notebooks to interact with the data.
A probabilistic seismic hazard analysis performed for rock conditions and modified for soil conditions using deterministic site amplification factors does not account for uncertainty in site effects, which can be significant. One approach to account for such uncertainty is to compute a weighted average amplification curve using a logic tree that accounts for several possible scenarios with assigned weights corresponding to their relative likelihood or confidence. However, this approach can lead to statistical smoothing of the amplification curve and possibly to decreased computed hazard as epistemic uncertainty increases. This is against the expected trend that higher uncertainty leads to higher computed hazard, thus reducing the incentive for practitioners to characterize soil properties at a site. This study proposes a modified approach in which the epistemic uncertainty is captured in a plot of amplification factors versus period. Using a case history, the proposed method is shown to improve the issue with the weighted average method for at least two oscillator periods and to yield similar results for two other periods in which the highlighted issue is less significant.
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