University of Illinois Urbana-Champaign

Optimal resource allocation to maximize model accuracy

Dankert, Guillermo Leo

Content Files
DANKERT-THESIS-2017.pdf

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https://hdl.handle.net/2142/97467

Description

Title
Optimal resource allocation to maximize model accuracy
Author(s)
Dankert, Guillermo Leo
Issue Date
2017-04-25
Director of Research (if dissertation) or Advisor (if thesis)
Gardoni, Paolo
Department of Study
Civil & Environmental Eng
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2017-08-10T19:16:06Z
Keyword(s)
  • Model uncertainty
  • Structural engineering
  • Soil structure interaction
  • Multi-model uncertainty propagation
Abstract
In the modelling of civil engineering structures, the engineer should make several assumptions to obtain a reasonable prediction of the behavior of the structure. Experience in projects in the professional practice shows that model assumptions made mechanically, without getting into specific details, lead to inaccurate models and to expensive resources allocation that does not render the optimal results. An example within civil engineering is the modelling of a structure, where – typically - structural engineers model the structure above the foundation and geotechnical engineers model the foundation and the soil behavior. These two areas have different error tolerances, mainly because the mechanical uncertainty of manufactured materials as concrete and steel is smaller than the uncertainty of soil. If these differences are not addressed correctly, the optimization process to get more accurate results is done incorrectly. This kind of situation is also common to other interaction with seismic engineering or hydrological engineering, among others. To address the limitations, it is necessary to consider the problem as a system, rather than dealing with the local behavior of the different parts. This work proposes a procedure based on the importance of random and categorical variables to address the different sources of uncertainty. Considering individual component models as categorical variables, the procedure follows a multi-model uncertainty propagation approach. The developed procedure is applied to an example of a cantilever beam on clay foundation.
Graduation Semester
2017-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/97467
Copyright and License Information
Copyright 2017 Guillermo Dankert

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