Merging experimental design and structural identification around the concept of modified Constitutive Relation Error in low-frequency dynamics for enhanced structural monitoring

Diaz, Matthieu; Charbonnel, Pierre-Étienne; Chamoin, Ludovic

doi:10.1016/j.ymssp.2023.110371

Cited by 2 publications

(2 citation statements)

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“…As one could have expected, the more sensors scattered, the more accurate identification These results also highlight that optimal sensor placement strategies should be applied in order to get the most relevant information from a restricted amount of data. We refer to our recent work [61] for more details on that feature.…”

Section: Influence Of Sensor Density On Damage Detection Performancementioning

confidence: 99%

“…As it has been evidenced that sensor placements are critical in the accuracy of identification results, dedicated studies dealing with optimal sensor placement [61] and the influence of uncertain sensor locations are currently being conducted. Other current investigations of the authors deal with mCRE-based model updating using full-field (digital image correlation) or locally rich (optic fibers) measurements.…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

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

Diaz,

Charbonnel,

Chamoin

2023

Comput Mech

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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.

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Section: Influence Of Sensor Density On Damage Detection Performancementioning

confidence: 99%

Section: Conclusion and Prospectsmentioning

confidence: 99%