UCLA

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 incom-plete datasets and parameter spaces for model development. The Next Gen-eration Liquefaction (NGL) Project was created to provide a database for ad-vancing liquefaction research and to develop models for the prediction of liquefaction and its effects, derived in part from that database in a transpar-ent and peer-reviewed manner, that provide end users with a consensus ap-proach to assess liquefaction potential within a probabilistic framework. An online relational database was created for organizing and storing case histo-ries which is available at http://nextgenerationliquefaction.org/ (https://www.doi.org/10.21222/C2J040, [1]). The NGL field case history da-tabase was recently expanded to include the results of laboratory testing pro-grams 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 processed-data 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.