A novel identification method for obtaining bending moment–curvature relations of wood stems based on digital image correlation and SDOF analysis - Mécanique des Matériaux et des Structures, laboratoire GEOMAS Access content directly
Journal Articles European Journal of Environmental and Civil Engineering Year : 2022

A novel identification method for obtaining bending moment–curvature relations of wood stems based on digital image correlation and SDOF analysis

Abstract

Bending moment–curvature relations (M-χ) are useful in many beam modelling approaches. It allows describing in a simple way the mechanical behaviour at the cross-section scale and can be used to model continuous beams as single degree of freedom systems dedicated to perform stochastic analysis. This paper focused on tree mechanical charaterisation and presents an identification method for obtaining bending moment–curvature relationships up to failure from Digital Image Correlation (DIC) and experimental three points bending tests. Due to wood intrinsic variability, the proposed identification method has been validated from a Finite Element (FE) model which aims at simulating precisely the bending response of a cylindrical stem. The refined FE model has been developed based on the geometry and the boundary conditions of the experimentally tested stems. M-χ curves are derived from the FE model and used within a Single Degree Of Freedom (SDOF) approach. At the stem scale, the prediction of the SDOF model (Force-Displacement) is compared to the FE model and lead to a good agreement. Then the approach is used to characterise fresh European beech samples allowing deriving their mechanical properties (longitudinal Young modulus, modulus of elasticity and modulus of rupture).
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Dates and versions

hal-02094497 , version 1 (12-06-2019)

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David Bertrand, Nicolas Tardif, Franck Bourrier, Jean-Baptiste Barré, Stéphane Grange. A novel identification method for obtaining bending moment–curvature relations of wood stems based on digital image correlation and SDOF analysis. European Journal of Environmental and Civil Engineering, 2022, 25 (10), pp.1782-1795. ⟨10.1080/19648189.2019.1602567⟩. ⟨hal-02094497⟩
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