Optimization routine for identification of model parameters in soil plasticity

Mattsson, Hans; Klisinski, Marek; Axelsson, Kennet

doi:10.1002/nag.137

Cited by 29 publications

(27 citation statements)

References 13 publications

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“…The solution of the inverse problem consists in obtaining a minimum for an objective function which is defined by taking into account the mathematical structure of the material model and the experimental data. This generally results in a non-linear programming constrained problem of the following form [13]:…”

Section: Definition Of Inverse Problem For Parameter Identificationmentioning

confidence: 99%

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Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Liu

2006

Acta Mech Sin

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Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of vibrating forces. In order to identify parameters in an efficient and robust manner, an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy. The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification, in which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.

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Section: Definition Of Inverse Problem For Parameter Identificationmentioning

confidence: 99%

“…Several such techniques have been introduced in Refs. [13][14][15][16]. Most of the literature, however, used a gradient-based optimization method and the solution often vibrates or diverges, depending upon the initial search point, since the model and the measurement errors could make the objective function complicated.…”

Section: Introductionmentioning

confidence: 99%

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Liu

2006

Acta Mech Sin

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“…(1) Zero-order method: requires evaluations of f ðxÞ only, and no information about g or H is needed [6]. (2) First-order method: requires evaluations of f ðxÞ as well as gðxÞ: If gðxÞ are simple functions, gðx n Þ ¼ 0 may be solved in closed form for x n : In case an explicit expression for gðxÞ is not available, it is often approximated using semi-analytical [4] or finite difference methods [12].…”

Section: Unconstrained Optimization Backgroundmentioning

confidence: 99%

“…Indeed, efforts have been reported in development of efficient optimization algorithms, and their applications to the areas of soil constitutive model calibration (e.g. References [1,2,[4][5][6]), and geotechnical system identification (e.g. References [3,7,[14][15][16][17]).…”

Section: Introductionmentioning

confidence: 99%

Application of unconstrained optimization and sensitivity analysis to calibration of a soil constitutive model

Yang

Elgamal

2003

Num Anal Meth Geomechanics

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SUMMARYLarge sets of soil experimental data (field and laboratory) are becoming increasingly available for calibration of soil constitutive models. A challenging task is to calibrate a potentially large number of model parameters to satisfactorily match many data sets simultaneously. This calibration effort can be facilitated by optimization techniques. The current study aims to explore systematic approaches for exercising optimization and sensitivity analysis in the area of soil constitutive modelling. Analytical, semianalytical and numerical optimization techniques are employed to calibrate a multi-surface-plasticity sand model. Calibration is based on results from a number of drained triaxial sample tests and a dynamic centrifuge liquefaction test. The analytical and semi-analytical approaches and associated sensitivity analysis are applied to calibrate the model non-linear shear stress-strain response. Thereafter, model parameters controlling shear-volume coupling effects (dilatancy) are calibrated using a solid-fluid fully coupled finite element program in conjunction with an advanced numerical optimization code. A related sensitivity study reveals the challenges often encountered in optimizing highly non-linear functions. Overall, this study demonstrates applicability and limitations of optimization techniques for constitutive model calibration.

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“…During the last decade, research has been carried out on the use of optimization methods for the identification of geotechnical parameters from both laboratory tests [1] and field tests (e.g. pressuremeter tests) [2].…”

Section: Introductionmentioning

confidence: 99%

Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization

Zhang

Gallipoli

Augarde

2009

Computers and Geotechnics

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(2009) 'Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization.', Computers and geotechnics., 36 (4). pp. 604-615. Further information on publisher's website: http://dx. The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. Please consult the full DRO policy for further details. Abstract Simulation-based optimization methods have been recently proposed for calibrating geotechnical models from laboratory and field tests. In these methods, geotechnical parameters are identified by matching model predictions to experimental data, i.e. by minimizing an objective function that measures the difference between the two. Expensive computational models, such as finite difference or finite element models, are often required to simulate laboratory or field geotechnical tests. In such cases, simulation-based optimization might prove demanding since every evaluation of the objective function requires a new model simulation until the optimum set of parameter values is achieved. This paper introduces a novel simulation-based "hybrid moving boundary particle swarm optimization" (hmPSO) algorithm that enables calibration of geotechnical models from laboratory or field data. The hmPSO has proven effective in searching for model parameter values and, unlike other optimization methods, does not require information about the gradient of the objective function. Serial and parallel implementations of hmPSO have been validated in this work against a number of benchmarks, including numerical tests, and a challenging geotechnical problem consisting of the calibration of a water infiltration model for unsaturated soils. The latter application demonstrates the potential of hmPSO for interpreting laboratory and field tests as well as a tool for general back-analysis of geotechnical case studies.

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Optimization routine for identification of model parameters in soil plasticity

Cited by 29 publications

References 13 publications

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Application of unconstrained optimization and sensitivity analysis to calibration of a soil constitutive model

Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization

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