Application of Advanced Optimization Techniques to Parameter and Damage Identification Problems

Toropov, Vassili; Yoshida, Fusahito

doi:10.1007/3-211-38134-1_6

Cited by 4 publications

(3 citation statements)

References 55 publications

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“…The value of the error parameter gives a measure of discrepancy between the values of the initial functions and the simplified ones. Any conventional optimization technique (Toropov and Yoshida, 2005) can be used to solve a sub-problem in equation ( 5), because the functions involved in its formulation are simple and noiseless.…”

Section: Formulation Of the Alternative Materials Parameter Identification Problem As An Optimization Problemmentioning

confidence: 99%

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Identification of material parameters for low bond strength masonry

Sarhosis

Sheng

2014

Engineering Structures

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Section: Formulation Of the Alternative Materials Parameter Identification Problem As An Optimization Problemmentioning

confidence: 99%

“…the Genetic Algorithm (GA) in HyperStudy 10 has been adopted. According to Toropov and Yoshida (2005), GA is more likely to find a non-local solution (i.e. the global minimum) when compared to other gradient based methods such as Sequential Quadratic Method or the Adaptive Response Surface method.…”

Section: Figmentioning

confidence: 99%

Identification of material parameters for low bond strength masonry

Sarhosis

Sheng

2014

Engineering Structures

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“…Quadratic Programming) and a global search algorithm method (known as the Genetic 268 Algorithm approach) are the two representative methods that can be used for the comparison 269 of results (Toropov and Yoshida, 2005).…”

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Optimisation procedure for material parameter identification for masonry constitutive models

Sarhosis

2016

IJMRI

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7Constitutive models for masonry require a number of parameters to define material behaviour 8 with sufficient accuracy. It is common practice to determine such material parameters from 9 the results of various, relatively simple, small-scale laboratory experiments. However, the 10 effectiveness of determining material parameters that are representative of masonry from 11 small-scale experiments have found to be problematic. This paper investigates the material 12 parameter identification problem for masonry constitutive models. The methodology is based 13 on an inverse analysis containing an optimization procedure and surrogate modelling. The 14 general framework of the non-linear estimate methodology and the parameter identification 15 problems are discussed. 16 17

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