The Supported Modeling Team (SMT) of the Next Generation Liquefaction (NGL) project is charged with developing a liquefaction triggering model that fulfills the contractual obligations of the project to its sponsors. The modeling team is comprised of five core members with several ex officio members that participate in bi-weekly meetings and supplemental research studies. The SMT is pursuing a three-phase approach to development of a triggering model with the goal of producing a consistent, objective, transparent, and well-documented framework for case history data processing, interpretation, uncertainty quantification, and regression. The first phase involves a data-driven initial interpretation of raw case history data by developing an automated workflow that (a) breaks CPT profiles down into discrete layers, (b) characterizes the pertinent properties (e.g., penetration resistance, soil type, etc.) of each layer, and (c) makes a preliminary identification of the critical layer(s) of the profile. The second phase involves detailed review of each of the case histories to confirm or modify the characterization developed in the first phase. The third phase will use the characterized case histories and the results of supplemental research studies to build a probabilistic triggering model with characterization of both aleatory variability and epistemic uncertainty.

NGL Supported Modeling Team Approach

Zimmaro P.
2022

Abstract

The Supported Modeling Team (SMT) of the Next Generation Liquefaction (NGL) project is charged with developing a liquefaction triggering model that fulfills the contractual obligations of the project to its sponsors. The modeling team is comprised of five core members with several ex officio members that participate in bi-weekly meetings and supplemental research studies. The SMT is pursuing a three-phase approach to development of a triggering model with the goal of producing a consistent, objective, transparent, and well-documented framework for case history data processing, interpretation, uncertainty quantification, and regression. The first phase involves a data-driven initial interpretation of raw case history data by developing an automated workflow that (a) breaks CPT profiles down into discrete layers, (b) characterizes the pertinent properties (e.g., penetration resistance, soil type, etc.) of each layer, and (c) makes a preliminary identification of the critical layer(s) of the profile. The second phase involves detailed review of each of the case histories to confirm or modify the characterization developed in the first phase. The third phase will use the characterized case histories and the results of supplemental research studies to build a probabilistic triggering model with characterization of both aleatory variability and epistemic uncertainty.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/335689
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