The Next Generation Liquefaction (NGL) database contains geotechnical laboratory and site investigation information and observations of the presence or absence of liquefaction during past earthquake events. It is designed so that researchers from around the world have open access to consume, upload, and interact with the data. A primary goal of the NGL project is to promote use of the open access database to develop models for assessing liquefaction triggering and consequences. The database is broadly inclusive, incorporating as many liquefaction case histories as practical, including many recent events that have not been considered in previous liquefaction triggering models. The database can be accessed via an online graphical user interface (Figure 1, available at: https://doi.org/10.21222/C2J040) and using cloud-based resources via the DesignSafe cyberinfrastructure. In this presentation, we (i) briefly describe the structure of the database, (ii) provide an overview of what the database contains, (iii) discuss the data review process, and (iv) highlight the ways in which users and model developers can use cloud-based workflows to learn from the data.
Next Generation Liquefaction Database and Cloud Computing
Zimmaro P.;
2022-01-01
Abstract
The Next Generation Liquefaction (NGL) database contains geotechnical laboratory and site investigation information and observations of the presence or absence of liquefaction during past earthquake events. It is designed so that researchers from around the world have open access to consume, upload, and interact with the data. A primary goal of the NGL project is to promote use of the open access database to develop models for assessing liquefaction triggering and consequences. The database is broadly inclusive, incorporating as many liquefaction case histories as practical, including many recent events that have not been considered in previous liquefaction triggering models. The database can be accessed via an online graphical user interface (Figure 1, available at: https://doi.org/10.21222/C2J040) and using cloud-based resources via the DesignSafe cyberinfrastructure. In this presentation, we (i) briefly describe the structure of the database, (ii) provide an overview of what the database contains, (iii) discuss the data review process, and (iv) highlight the ways in which users and model developers can use cloud-based workflows to learn from the data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
