MCRE-based finite element model updating: Cast3M implementation

Oliveira, Hugo Luiz; Louf, François; Gatuingt, Fabrice

doi:10.1016/j.advengsoft.2022.103220

Cited by 2 publications

(1 citation statement)

References 33 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among other works, the mCRE was shown to be robust to highly noisy and corrupted measurements [31] and able to perform local defect detection [25,32,33], or full-field material identification from dense measurements [34,35]. It has recently been successfully applied to Earthquake Engineering problems, with an implementation in an industrial FE software [36,37]. As one can explicitly establish a link between mCRE with other deterministic and stochastic model updating functionals, it is also worth mentioning the comparative study between mCRE, Tikhonovbased and Bayesian damage detection using optical fiber strain measurements investigated in [38].…”

Section: Vibration-based Damage Detection At a Glancementioning

confidence: 99%

Fully automated model updating framework for damage detection based on the modified constitutive relation error

Diaz,

Charbonnel,

Chamoin

2023

Comput Mech

View full text Add to dashboard Cite

Digital twins efficiency lies in fast and representative solutions of inverse problems to accomodate models with physical observations. The quality of the solution of an inverse problem is conditioned by inherent features of the latter, in particular (i) the richness of available data, (ii) the a priori experimental and modeling knowledge that allows to regularize the illposedness nature of the problem, and (iii) the complexity of the space in which updated parameters are sought. We present in this contribution a fully automated physics-guided model updating framework dedicated to the correction of finite element models from low-frequency dynamics measurements. The proposed methodology is based on the minimization of a modified Constitutive Relation Error (mCRE) functional, whose basic idea is to construct mechanical fields and identify material parameters that are a trade-off between all available information (and associated confidence) but without any further assumption. The dependency into some expert-user's judgment is thus avoided. Dedicated rules are provided to automatically calibrate all mCRE internal tuning parameters as well as a strategy to optimize the space in which parameters are sought, leading to a fully autonomous algorithm. The performance and robustness of the proposed model updating methodology are illustrated using synthetic ground motion tests on a bending plate in which defects of various shapes are identified from noisy acceleration datasets, with inherent limitations due to richness of input loading, sensors sparsity and defect identifiability.

show abstract