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|Title:||Description of anisotropic properties of trabecular bone|
Webber, C. E.
|Keywords:||Civil Engineering;Civil Engineering|
|Abstract:||<p>This thesis presents a comprehensive approach to numerical modelling of human bone. Bone has been shown, in general, to be a heterogeneous material with orthotropic symmetry. The geometric arrangement of its porous micro-structure can be detected by high resolution tomographic imaging and then characterized by a 'fabric tensor.' This tensorial measure of material fabric can be correlated with mechanical properties and subsequently employed within numerical analyses of bone. In this work, the fabric tensor is incorporated into an elastic constitutive framework and a novel failure criterion for bone is proposed, which is seen as an important contribution to the numerical analysis of bone within the finite element (FE) methodology. The identification of fabric from micro-computed tomography (micro-CT) images of representative bone samples is achieved by a unified computational framework, described by a language independent pseudo-code. As a contribution to the constitutive representation of bone material, a new measure of fabric is defined and then identified using synthetic data of simple geometric shapes and micro-CT scans of human trabecular bone. The current potential for improvement in FE modelling of the mechanical behaviour of bone is illustrated through a discussion of bone fracture. The numerical analysis is an extension of the results presented in Pietruszczak et al. (1997, 1999) wherein a high resolution geometric model with heterogeneous distribution of orientation-dependent mechanical properties was employed. The use of the material model within a FE analysis is illustrated by a FE analysis pertaining to the prediction of fracture within a femur, under the simulated conditions of a fall to the hip. In particular, the distribution of damage within a femur is assessed under two porosity distributions, simulating a healthy and an aged bone.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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