Stochastic inverse method to identify parameter random fields in a structure

Chan Kyu Choi, Hong Hee Yoo

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The parameters in a structure such as geometric and material properties are generally uncertain due to manufacturing tolerance, wear, fatigue and material irregularity. Such parameters are random fields because the uncertain properties vary along the spatial domain of a structure. Since the parameter uncertainties in a structure result in the uncertainty of the structural dynamic behavior, they need to be identified accurately for structural analysis or design. In order to identify the random fields of geometric parameters, the parameters can be measured directly using a 3-dimensional coordinate measuring machine. However, it is often very expensive to measure them directly. It is even impossible to directly measure some parameters such as density and Young’s modulus. For that case, the parameter random fields should be identified from measurable response data samples. In this paper, a stochastic inverse method to identify parameter random fields in a structure using modal data is proposed. The proposed method consists of the following three steps: (i) obtaining realizations of the parameter random field from modal data samples by solving an optimization problem, (ii) obtaining the deterministic terms in the Karhunen-Loève expansion by solving an eigenvalue problem and (iii) estimating the distributions of random variables in the Karhunen-Loève expansion using a maximum likelihood estimation method with kernel density.

Original languageEnglish
Pages (from-to)1557-1571
Number of pages15
JournalStructural and Multidisciplinary Optimization
Volume54
Issue number6
DOIs
StatePublished - 2016 Dec 1

Fingerprint

Inverse Method
Stochastic Methods
Random Field
Coordinate measuring machines
Maximum likelihood estimation
Structural dynamics
Structural design
Random variables
Structural analysis
Materials properties
Elastic moduli
Wear of materials
Fatigue of materials
Kernel Density
Coordinate Measuring Machine
Structural Design
Uncertainty
Structural Dynamics
Young's Modulus
Irregularity

Keywords

  • Karhunen-Loève expansion
  • Modal data
  • Parameter random field
  • Stochastic inverse method
  • Structure

Cite this

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abstract = "The parameters in a structure such as geometric and material properties are generally uncertain due to manufacturing tolerance, wear, fatigue and material irregularity. Such parameters are random fields because the uncertain properties vary along the spatial domain of a structure. Since the parameter uncertainties in a structure result in the uncertainty of the structural dynamic behavior, they need to be identified accurately for structural analysis or design. In order to identify the random fields of geometric parameters, the parameters can be measured directly using a 3-dimensional coordinate measuring machine. However, it is often very expensive to measure them directly. It is even impossible to directly measure some parameters such as density and Young’s modulus. For that case, the parameter random fields should be identified from measurable response data samples. In this paper, a stochastic inverse method to identify parameter random fields in a structure using modal data is proposed. The proposed method consists of the following three steps: (i) obtaining realizations of the parameter random field from modal data samples by solving an optimization problem, (ii) obtaining the deterministic terms in the Karhunen-Lo{\`e}ve expansion by solving an eigenvalue problem and (iii) estimating the distributions of random variables in the Karhunen-Lo{\`e}ve expansion using a maximum likelihood estimation method with kernel density.",
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Stochastic inverse method to identify parameter random fields in a structure. / Choi, Chan Kyu; Yoo, Hong Hee.

In: Structural and Multidisciplinary Optimization, Vol. 54, No. 6, 01.12.2016, p. 1557-1571.

Research output: Contribution to journalArticle

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