Masters Theses

Orcid ID

https://orcid.org/0000-0001-8986-7839

Date of Award

8-2024

Degree Type

Thesis

Degree Name

Master of Science

Major

Civil Engineering

Major Professor

Timothy J. Truster

Committee Members

Dayakar Penumadu, Nicholas Wierschem

Abstract

Predicting mechanical responses under various load conditions is of significant interest in the field of orthopedic research. Despite an abundance of research on finite element (FE) modeling for human bones, studies specifically focusing on the tibia remain notably limited. Given that mechanical properties and structural form of goat tibiae closely mimic those of human tibiae at the region of interest (ROI), they can serve as excellent models for comparative orthopedic research. While existing literature on ovine bone research offers rich in-vivo models, it lacks a validated FE model of the tibia subjected to thorough spatial error assessment.

This thesis presents a novel open-source-based FE model of a goat tibia incorporating phase field fracture representation. The model was validated using Digital Image Correlation (DIC)-measured strain under compression, offering a valuable tool for bone biomechanics research. A model of bone geometry was constructed from a 3D quantitative computed tomography (QCT) scan of the goat tibia. Density was calculated from Hounsfield values and spatially distributed within the FE mesh. To validate this FE model, we conducted a uniaxial compression test by applying the load along the shaft axis. A DIC system provided high-resolution strain measurements across the surface of the tibia, with results found to align well with FE simulation outcomes - thus validating our elastic model.

To quantitatively predict three-dimensional fractures, we used a high-performance computing (HPC) environment to couple our elastic model with a phase field model – resulting in fracture initiation and evolution predictions that closely mirror experimental observations. This high-fidelity QCT-based approach offers a framework for personalized modeling of human tibiae enabling patient-specific analysis relating to fracture risk, implant effectiveness, and optimal treatment strategies.

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