On the identification of rigid body boundary conditions from full-field measurements

This study presents a method to identify Dirichlet boundary conditions (BCs) and constitutive parameters from heterogeneous experimental data, solving a multi-objective optimization problem. The BCs are parameterized as remote rigid body motions leading to a very low number of unknowns and allowing for faster convergence. This methods enables for accurate models of material and structural tests to be obtained. A comparison with classical methods of BC determination is introduced, demonstrating the advantages of the proposed approach. The identified models are evaluated by introducing a validation metric based on experimental data and their uncertainties. The approach is illustrated by using a multi-instrumented tensile test on a well-characterized material. Beyond this validation example, the introduced method paves the way for improved model updating of large-scale tests. • A reduced model for boundary conditions identification method is proposed. • Boundary conditions are accurately determined outside the measurement area. • A quality metric is introduced to better evaluate and compare models. • The study accounts for measurement uncertainties.

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Publication Details

Journal
International Journal of Solids and Structures
Published
2025-11-21
DOI
https://doi.org/10.1016/j.ijsolstr.2025.113768
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

On the identification of rigid body boundary conditions from full-field measurements

Pierre Baudoin, François Hild A, Antoine Vintache, Florent Mathieu et al.
International Journal of Solids and Structures
Model Reduction and Neural Networks
article

On the identification of rigid body boundary conditions from full-field measurements

Pierre Baudoin, François Hild A, Antoine Vintache, Florent Mathieu, Renaud Gras
article en

Abstract

This study presents a method to identify Dirichlet boundary conditions (BCs) and constitutive parameters from heterogeneous experimental data, solving a multi-objective optimization problem. The BCs are parameterized as remote rigid body motions leading to a very low number of unknowns and allowing for faster convergence. This methods enables for accurate models of material and structural tests to be obtained. A comparison with classical methods of BC determination is introduced, demonstrating the advantages of the proposed approach. The identified models are evaluated by introducing a validation metric based on experimental data and their uncertainties. The approach is illustrated by using a multi-instrumented tensile test on a well-characterized material. Beyond this validation example, the introduced method paves the way for improved model updating of large-scale tests. • A reduced model for boundary conditions identification method is proposed. • Boundary conditions are accurately determined outside the measurement area. • A quality metric is introduced to better evaluate and compare models. • The study accounts for measurement uncertainties.

International Journal of Solids and StructuresVol. 327
Centre National de la Recherche Scientifique (FR), Université Paris-Saclay (FR), STMicroelectronics (France) (FR), CentraleSupélec (FR), Laboratoire de Mécanique Paris-Saclay (FR)
Association Nationale de la Recherche et de la Technologie
Openalex Percentile: Top 71%
Model Reduction and Neural Networks
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On the identification of rigid body boundary conditions from full-field measurements — Pierre Baudoin, François Hild A, et al. · International Journal of Solids and Structures (2025) | TGRS Research Map | TGRS