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Benoît Blaysat

Researcher at University of Auvergne

Publications -  70
Citations -  1069

Benoît Blaysat is an academic researcher from University of Auvergne. The author has contributed to research in topics: Digital image correlation & Computer science. The author has an hindex of 14, co-authored 54 publications receiving 713 citations. Previous affiliations of Benoît Blaysat include UniverSud Paris & King Abdullah University of Science and Technology.

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The Grid Method for In‐plane Displacement and Strain Measurement: A Review and Analysis

TL;DR: The grid method is a technique suitable for the measurement of in-plane displacement and strain components on specimens undergoing a small deformation as discussed by the authors, which relies on a regular marking of the surfaces under investigation.
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Towards standardized mechanical characterization of microbial biofilms: analysis and critical review

TL;DR: This work proposes guidelines for characterizing biofilms according to microbiological objectives that will help the reader choose an appropriate test and a relevant identification method for measuring any given mechanical parameter.
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Interface debonding characterization by image correlation integrated with Double Cantilever Beam kinematics

TL;DR: In this paper, a closed-form theoretical model is developed to reconstruct a mechanically admissible displacement field representing the deformation of the adhering layers during debonding in the DCB fracture test.
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A Critical Comparison of Some Metrological Parameters Characterizing Local Digital Image Correlation and Grid Method

TL;DR: The main metrological performance of two full-field measurement techniques, namely local digital image correlation (DIC) and grid method (GM), are compared and it is shown that the product between the displacement resolution and the spatial resolution can be considered as a metric to perform this comparison.
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On image gradients in digital image correlation

TL;DR: In this paper, the impact of different image gradients on the accuracy, efficiency, and initial guess robustness is discussed on the basis of a number of academic examples and representative test cases, and the main conclusion is that for most cases, the image gradient most common in literature is recommended, except for cases with large rotations; initial guess instabilities; and costly iterations due to other reasons (e.g., integrated DIC).