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Improving Reduced Order Models of Soil-Structure Interaction Using an Ensemble Kalman Inversion Finite Element Model Updating Framework

Citation

Kusanovic, Danilo Smiljan (2021) Improving Reduced Order Models of Soil-Structure Interaction Using an Ensemble Kalman Inversion Finite Element Model Updating Framework. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/m2qj-s182. https://resolver.caltech.edu/CaltechTHESIS:12092020-002934412

Abstract

In civil engineering, almost all structures are somehow in contact with soil - i.e., have foundations or support elements that either rest on or are embedded in soil. Thus, their seismic response is governed by the interaction between the structure, the non-structural components, the foundation, and the surrounding soil. Predicting such interaction becomes increasingly complex when uncertainties of soil and structural material, ground motion variability, and dissipation mechanisms are considered. The accuracy of numerical models to predict the linear or nonlinear responses of structures depends not only on how well the uncertainties in the material properties and input motion are estimated, but also on how well the various sources of energy dissipation and their interaction are modeled. Therefore, high-fidelity simulation of soil-structure interaction (SSI) problems require advanced models that can capture the nonlinear behavior of soils and structures, and parallel computing capabilities to optimize the cost associated with large scale problems. In spite of this fact, SSI in practice is widely accounted for using fixed-base building and reduced-order-models (ROM) which usually trade accurate solution for fast ones. Unfortunately, if SSI effects are neglected or poorly estimated, then critical response measures of a structure can be over- or under-estimated, which in turn can lead to unsafe or overly conservative designs.

Motivated by the previous challenge, in this thesis work we present a robust and efficient framework for finite element model (FEM) updating based on ensemble-Kalman inversion (EnKI). The EnKI-FEM updating framework is used to obtain suitable parameters to inform a ROM from data generated using high-fidelity FEM simulations. Since high-fidelity SSI simulations call for accurate and computationally efficient capabilities, as a part of this work, we developed Seismo-VLAB, a simple, fast, and extendable C++ finite element software to optimize large-scale simulations of dynamic and nonlinear SSI problems. The EnKI-FEM updating framework is thus integrated in Seismo-VLAB allowing to identify any parameter of the ROM without compromising accuracy. The so-generated ROM are finally employed to propose a new dimensionless frequency mapping to estimate the soil impedance for time domain analysis and to investigate soil-structure-interaction effects at a regional-scale. The presented methodology is general enough and it can be extended to more complex structural and/or geotechnical systems, allowing to construct highly-accurate ROM in a simple manner.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Finite element method, Large-scale simulations, Soil-structure interaction, Wave propagation, Meso-scale simulation, High-Performance computing, Parallel computing, Object-oriented programming, Inverse problem, Ensemble Kalman inversion, Mathematical modeling, Reduced order model
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Civil Engineering
Minor Option:Applied And Computational Mathematics
Computer Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Asimaki, Domniki
Thesis Committee:
  • Daraio, Chiara (chair)
  • Stuart, Andrew M.
  • Andrade, Jose E.
  • Asimaki, Domniki
Defense Date:18 September 2020
Funders:
Funding AgencyGrant Number
Comisión Nacional de Investigación Científica y Tecnológica (CONICYT)UNSPECIFIED
Record Number:CaltechTHESIS:12092020-002934412
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:12092020-002934412
DOI:10.7907/m2qj-s182
Related URLs:
URLURL TypeDescription
https://dankusanovic.github.io/AuthorPersonal website where personal information can be found.
http://www.seismovlab.com/OtherWebsite where Seismo-VLAB can be downloaded and where the documention is available.
ORCID:
AuthorORCID
Kusanovic, Danilo Smiljan0000-0002-0935-2577
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14019
Collection:CaltechTHESIS
Deposited By: Danilo Kusanovic Maldonado
Deposited On:11 Dec 2020 18:07
Last Modified:18 Dec 2020 17:49

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