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Sensitivity analysis for the mechanics of tendons and ligaments: Investigation on the effects of collagen structural properties via a multiscale modeling approach
Author(s) -
Hamdia Khader M.,
Marino Michele,
Zhuang Xiaoying,
Wriggers Peter,
Rabczuk Timon
Publication year - 2019
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.3209
Subject(s) - parametric statistics , sensitivity (control systems) , uncertainty quantification , biological system , multiscale modeling , statistical physics , constitutive equation , continuum mechanics , mechanics , computer science , mathematics , finite element method , structural engineering , physics , engineering , statistics , bioinformatics , biology , electronic engineering
The effects of the stochasticity of collagen‐related structural properties on the biomechanical properties of tendons and ligaments are investigated in this study. The tissue mechanics is modeled by means of a macroscale constitutive model based on a multiscale structural approach. This rationale allows to introduce model parameters directly associated with tissue structural and biochemical features, opening to physically motivated parametric studies. Variance and density‐based global sensitivity analyses are employed, together with the quantification of output uncertainty due to stochastic variations of parameters. Novel insights on tissue structure‐mechanics relationship are provided, quantifying the dependence between mechanical output quantities on specific collagen‐related structural features. Moreover, the uncertainty quantification shows that model predictions provided by the multiscale structural approach are reliable with respect to inevitable uncertainties in tissue structure. Addressing rat tail tendons, the use of average values in tissue properties returns a constitutive response that fits well‐available experimental data, and it is robust with respect to parameter stochasticity.