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Research Papers

A Computational Efficient Method to Assess the Sensitivity of Finite-Element Models: An Illustration With the Hemipelvis

[+] Author and Article Information
Dermot O'Rourke

Medical Device Research Institute,
School of Computer Science,
Engineering and Mathematics,
Flinders University,
1284 South Road,
Adelaide SA 5042, Australia
e-mail: dermot.orourke@flinders.edu.au

Saulo Martelli

Medical Device Research Institute,
School of Computer Science,
Engineering and Mathematics,
Flinders University,
1284 South Road,
Adelaide SA 5042, Australia
e-mail: saulo.martelli@flinders.edu.au

Murk Bottema

Medical Device Research Institute,
School of Computer Science,
Engineering and Mathematics,
Flinders University,
1284 South Road,
Adelaide SA 5042, Australia
e-mail: murk.bottema@flinders.edu.au

Mark Taylor

Medical Device Research Institute,
School of Computer Science,
Engineering and Mathematics,
Flinders University,
1284 South Road,
Adelaide SA 5042, Australia
e-mail: mark.taylor@flinders.edu.au

1Corresponding author.

Manuscript received February 14, 2016; final manuscript received September 6, 2016; published online November 3, 2016. Assoc. Editor: Joel D. Stitzel.

J Biomech Eng 138(12), 121008 (Nov 03, 2016) (8 pages) Paper No: BIO-16-1060; doi: 10.1115/1.4034831 History: Received February 14, 2016; Revised September 06, 2016

Assessing the sensitivity of a finite-element (FE) model to uncertainties in geometric parameters and material properties is a fundamental step in understanding the reliability of model predictions. However, the computational cost of individual simulations and the large number of required models limits comprehensive quantification of model sensitivity. To quickly assess the sensitivity of an FE model, we built linear and Kriging surrogate models of an FE model of the intact hemipelvis. The percentage of the total sum of squares (%TSS) was used to determine the most influential input parameters and their possible interactions on the median, 95th percentile and maximum equivalent strains. We assessed the surrogate models by comparing their predictions to those of a full factorial design of FE simulations. The Kriging surrogate model accurately predicted all output metrics based on a training set of 30 analyses (R2 = 0.99). There was good agreement between the Kriging surrogate model and the full factorial design in determining the most influential input parameters and interactions. For the median, 95th percentile and maximum equivalent strain, the bone geometry (60%, 52%, and 76%, respectively) was the most influential input parameter. The interactions between bone geometry and cancellous bone modulus (13%) and bone geometry and cortical bone thickness (7%) were also influential terms on the output metrics. This study demonstrates a method with a low time and computational cost to quantify the sensitivity of an FE model. It can be applied to FE models in computational orthopaedic biomechanics in order to understand the reliability of predictions.

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Figures

Grahic Jump Location
Fig. 1

The finite-element model of the intact hemipelvis, soft tissue layer, and femoral head with the boundary conditions and the acetabular region of interest. A load, representing the peak load experienced during gait, was applied to the base of the femoral head.

Grahic Jump Location
Fig. 2

An illustration of the variation in equivalent strain in the acetabular region of interest. From left to right, the models with the minimum, 25th percentile, median, 75th percentile, and maximum of the maximum equivalent strain are shown. The median, 95th percentile, and maximum equivalent strains (μstrain) are given for each of the regions of interests displayed.

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