Projects per year
Abstract
Trabecular bone has been previously recognized as time-dependent (viscoelastic) material, but the relationships of its viscoelastic behaviour with bone volume fraction (BV/TV) have not been investigated so far. Therefore, the aim of the present study was to quantify the time-dependent viscoelastic behaviour of trabecular bone and relate it to BV/TV. Uniaxial compressive creep experiments were performed on cylindrical bovine trabecular bone samples ( n=13 ) at loads corresponding to physiological strain level of 2000 με . We assumed that the bone behaves in a linear viscoelastic manner at this low strain level and the corresponding linear viscoelastic parameters were estimated by fitting a generalized Kelvin–Voigt rheological model to the experimental creep strain response. Strong and significant power law relationships ( r2=0.73, p<0.001 ) were found between time-dependent creep compliance function and BV/TV of the bone. These BV/TV-based material properties can be used in finite element models involving trabecular bone to predict time-dependent response. For users’ convenience, the creep compliance functions were also converted to relaxation functions by using numerical interconversion methods and similar power law relationships were reported between time-dependent relaxation modulus function and BV/TV.
Original language | English |
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Pages (from-to) | 1631-1640 |
Journal | Biomechanics and Modeling in Mechanobiology |
Volume | 15 |
Issue number | 6 |
Early online date | 18 Apr 2016 |
DOIs | |
Publication status | E-pub ahead of print - 18 Apr 2016 |
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Dive into the research topics of 'Linear viscoelasticity - bone volume fraction relationships of bovine trabecular bone'. Together they form a unique fingerprint.Projects
- 1 Finished
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A novel diagnostic tool: from structural health monitoring to tissue quality prediction
1/10/13 → 31/03/17
Project: Research
Datasets
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Characterisation of time-dependent mechanical behaviour of trabecular bone samples
Pankaj, P. (Creator) & Xie, S. (Creator), Edinburgh DataShare, 18 Apr 2018
DOI: 10.7488/ds/2340
Dataset