Model updating based on mixed-integer nonlinear programming under model-form uncertainty in finite element model

In this paper, a new finite element model updating (FEMU) method is proposed based on mixed-integer nonlinear programming (MINLP) to deal with model-form uncertainty in FE models. Depending on modelers’ preference and experience, various FE models can be constructed for a specific structure in pract...

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Bibliographic Details
Published in:Engineering with computers Vol. 37; no. 4; pp. 3699 - 3725
Main Authors: Jin, Seung-Seop, Park, Young-Soo, Kim, SungTae, Park, Young-Hwan
Format: Journal Article
Language:English
Published: London Springer London 01.10.2021
Springer Nature B.V
Subjects:
Algorithms
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Continuity (mathematics)
Control
Design optimization
Estimates
Finite element method
Integer programming
Markov analysis
Math. Applications in Chemistry
Mathematical and Computational Engineering
Mathematical models
Mixed integer
Model updating
Nonlinear programming
Optimization
Optimization algorithms
Original Article
Parameter estimation
Real variables
Systems Theory
Uncertainty
Variables
Mixed-integer nonlinear programming
Genetic algorithm
Finite element model updating
Model-form uncertainty
ISSN:0177-0667, 1435-5663
Online Access:Get full text
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Summary:In this paper, a new finite element model updating (FEMU) method is proposed based on mixed-integer nonlinear programming (MINLP) to deal with model-form uncertainty in FE models. Depending on modelers’ preference and experience, various FE models can be constructed for a specific structure in practice. However, no one can guarantee that a specific model representation is always best (Model-form uncertainty). Conventional method should perform model updating for each FE model independently and select a best one among them, so that it becomes computationally intensive with many candidate FE models. To handle model-form uncertainty, this study formulates FEMU as the MINLP problem. The proposed method assigns an integer variable for model choice, while continuous real variables are used for the updating parameters. With this formulation, the optimization algorithm can explore both model and parameter space simultaneously to deal with the model-form uncertainty in FE models. Firstly, three numerical experiments were explored to evaluate the performance of the proposed method by considering possible situations in reality as follows: (1) a true FE model exists in model space with an admissible FE model; (2) only admissible FE model exists in model space; and (3) no true and admissible FE models exist in model space. Then, the proposed method was experimentally validated through a real bridge. The results show that the proposed method can find a best FE model with optimal estimates of the updating parameters with much less computational efforts against the conventional FEMU.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-020-01030-x