Corpus ID: 5147672

Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and additional insight

@article{Rappel2016BayesianIF,
  title={Bayesian inference for the stochastic identification of elastoplastic material parameters: Introduction, misconceptions and additional insight},
  author={Hussein Rappel and Lars A. A. Beex and Jack S. Hale and St{\'e}phane P. A. Bordas},
  journal={ArXiv},
  year={2016},
  volume={abs/1606.02422}
}
We discuss Bayesian inference (BI) for the probabilistic identification of material parameters. This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters. For this purpose a single spring is considered, for which the stress-strain curves are artificially created. Besides offering a didactic introduction to BI, this paper proposes an approach to incorporate statistical errors both in the measured stresses, and in the measured strains. It is… Expand
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References

SHOWING 1-10 OF 54 REFERENCES
Introduction to the Bayesian Approach Applied to Elastic Constants Identification
The basic formulation of the least-squares method, based on the L2 norm of the residuals, is still widely used today for identifying elastic constants of aerospace materials from experimental data.Expand
A novel Bayesian strategy for the identification of spatially varying material properties and model validation: an application to static elastography
TLDR
A novel Bayesian, a computational strategy in the context of model-based inverse problems in elastostatics, and an expanded parametrization that enables the quantification of model discrepancies in addition to the constitutive parameters are proposed. Expand
Estimation of elastic constants of thick laminated plates within a Bayesian framework
Abstract This paper compares two estimators for the dynamic identification of elastic constants of thick laminated composite plates. The plate’s response is modeled by finite elements based onExpand
A Bayesian Approach to Identifying Structural Nonlinearity using Free-Decay Response: Application to Damage Detection in Composites
This work discusses a Bayesian approach to approximating the distribution of parameters governing nonlinear structural systems. Specifically, we use a Markov Chain Monte Carlo method for sampling theExpand
Identification of the parameters of complex constitutive models: Least squares minimization vs. Bayesian updating
TLDR
The common least-squares minimization approach is compared to the Bayesian updating procedure and several methodologies for an efficient approximation of the likelihood function are discussed in the present study. Expand
A Bayesian approach to selecting hyperelastic constitutive models of soft tissue
Abstract Hyperelastic constitutive models of soft tissue mechanical behavior are extensively used in applications like computer-aided surgery, injury modeling, etc. While numerous constitutive modelsExpand
Parameter estimation of orthotropic plates by Bayesian sensitivity analysis
A method is developed to revise the elastic properties of a thin composite plate vibration model in an iterative manner such that its modified analytical responses eventually match those obtainedExpand
Bayesian Identification of Elastic Constants in Multi-Directional Laminate from Moiré Interferometry Displacement Fields
The ply elastic constants needed for classical lamination theory analysis of multi-directional laminates may differ from those obtained from unidirectional laminates because of three dimensionalExpand
Bayesian parameter identification of hysteretic behavior of composite walls
Abstract A Bayesian probabilistic approach is applied for parameter identification of a hysteretic model using laboratory test data in this paper. A hysteretic model for multi-grid composite walls isExpand
Bayesian model selection and parameter estimation for fatigue damage progression models in composites
Abstract A Bayesian approach is presented for selecting the most probable model class among a set of damage mechanics models for fatigue damage progression in composites. Candidate models, that areExpand
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