Uncertainty Analysis of Structural
Seismic Response Parameters

SimCenter Series: Early Career Researcher Forum
June 28, 2017 | 12pm - 1pm PDT

Abstract

Uncertainty Analysis of Structural Seismic Response Parameters Using High-Throughput Computing

Reliability-based seismic design of structures requires an ensemble of nonlinear time history analyses (NLTHA) based on a nonlinear finite element (FE) model of the structure of interest. This ensemble of NLTHAs typically accounts for the seismic record-to-record variability, but can also consider the variability (uncertainty) of the FE model parameters. For detailed nonlinear FE structural models, a single NLTHA is computationally intensive (runtime on the scale of hours or days). To statistically quantify the variability of the structural response, Monte Carlo simulation (and its various derivatives) can be employed to set up the ensemble analysis. Monte Carlo simulation, known for being one of the most robust yet computationally expensive methods to propagate uncertainty through a numerical analysis, is made computationally feasible via parallelization on a supercomputer.

A 5-story three-dimensional steel moment building frame subject to both model parameter uncertainty and ground motion record-to-record variability is analyzed in parallel using a hybrid statistical/structural analysis software. This software was created by coupling two open-source software frameworks: Dakota, developed by Sandia National Laboratories, for uncertainty quantification and OpenSees (Open System for Earthquake Engineering Simulation) for advanced modeling and analysis of structural and geotechnical systems subjected to earthquakes. Some of the capabilities of this coupled framework will be illustrated through this application example.

If you have questions or concerns, please contact Erika Donald, erikad@berkeley.edu.

PRESENTER

Zach Caamano-Withall
Zach Caamano-Withall is a Ph.D. student in the Department of Structural Engineering at UC San Diego. Working with Prof. Joel P. Conte, his research focuses on probabilistic modeling and analysis in the context of reliability-based, performance-based seismic analysis and design of civil structures. Zach received his undergraduate degree from UC San Diego in 2015.

 


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