Online citations, reference lists, and bibliographies.

Predicting The Compressive Mechanical Behavior Of Bone.

T. Keller
Published 1994 · Engineering, Medicine

Cite This
Download PDF
Analyze on Scholarcy
Share
The principal objectives of this study were to determine the mathematical dependency of the compressive mechanical properties of human bone on several commonly used measures of bone composition, and to assess variations in this dependency based upon the composition range spanned by the data. Destructive mechanical tests were conducted along the superior-inferior axis of 496 cubic specimens of human trabecular and cortical bone from five male donors (ages 46-84 yr), including specimens from lumbar vertebrae and femoral metaphyses and diaphyses. There was over a 3000-fold variation in strength (S, ultimate stress) and over a 20,000-fold variation in stiffness (E, elastic modulus) over the range of apparent dry density (rho a = 0.05-1.89 g cm-3), apparent ash density (rho alpha = 0.03-1.22 g cm-3) and mineral content (alpha = 17.4-66.2%) examined. Both linear and power models produced very high correlations (R2 > 0.81) between mechanical properties and bone composition, but the linear models resulted in a much greater percent deviation (PD) of the predicted dependent variable with respect to the measured value, in comparison to power models. The best correlations were obtained using rho alpha as the only independent variable: S (MPa) = 117 rho alpha 1.93 +/- 0.04 (R2 = 0.969, PD = 29.9, E (GPa) = 10.5 rho alpha 2.57 +/-0.04 (R2 = 0.965, PD = 46.7). Power models of bone stiffness and strength, incorporating only low density data (rho alpha < 0.2 g cm-3, rho a < 0.3), were characterized by approximately squared exponents and these models underestimated the stiffness (five-fold) and overestimated the strength (two-fold) for higher density data, which were characterized by exponents greater than two. Using a subset of the data based upon an apparent dry density range of 0.22 < rho a < 1.89 g cm-3, it was possible to obtain a mathematical relationship in which bone stiffness and strength were precisely proportional to the cube and square, respectively, of the apparent dry density. These results indicate that the mathematical dependency of bone compressive mechanical properties on composition is closely dependent upon the density and mineral content range examined and, in terms of a single compositional measure, is best predicted by apparent ash density expressed as a power function.



This paper is referenced by
10.1016/j.jmbbm.2009.03.001
Mechanical evaluation by patient-specific finite element analyses demonstrates therapeutic effects for osteoporotic vertebrae.
Daisuke Tawara (2010)
10.2174/1874325001105010335
Pathophysiology and Biomechanics of the Aging Spine
Michael Papadakis (2011)
10.1016/j.clinbiomech.2016.05.003
Prediction of stemless humeral implant micromotion during upper limb activities.
Philippe Favre (2016)
10.3390/ma11020258
Hydroxyapatite Microspheres as an Additive to Enhance Radiopacity, Biocompatibility, and Osteoconductivity of Poly(methyl methacrylate) Bone Cement
in-gu kang (2018)
10.1016/S0021-9290(97)00123-1
Prediction of femoral fracture load using automated finite element modeling.
J. Keyak (1997)
10.1007/s11914-018-0449-5
On the Relation of Bone Mineral Density and the Elastic Modulus in Healthy and Pathologic Bone
Sabah Nobakhti (2018)
10.1002/cnm.3211
A novel phase field method for modeling the fracture of long bones.
Rilin Shen (2019)
10.1016/S0736-0266(00)00046-2
Effect of force direction on femoral fracture load for two types of loading conditions.
J. Keyak (2001)
The evaluation of bone strength
A. Jain (2008)
10.1016/j.jbiomech.2008.03.007
On the mechanical stability of porous coated press fit titanium implants: a finite element study of a pushout test.
B. Helgason (2008)
10.1299/KIKAIA.72.255
Investigation of Vertebral Fracture Risk Evaluation in Osteoporosis by Patient-Specific Finite Element Analysis
Daisuke Tawara (2006)
disuse : A finite element study Prediction of risk of fracture in the tibia due to altered bone mineral density distribution resulting from
Tanner (2014)
10.1007/s10237-012-0394-7
Failure modelling of trabecular bone using a non-linear combined damage and fracture voxel finite element approach
Noel M. Harrison (2013)
10.1016/J.CLINBIOMECH.2019.05.028
Finite element analyses for predicting anatomical neck fractures in the proximal humerus.
Gal Dahan (2019)
10.1115/1.1517566
Biaxial failure behavior of bovine tibial trabecular bone.
G. Niebur (2002)
10.1007/s00586-003-0651-7
Damage-based finite-element vertebroplasty simulations
V. Kosmopoulos (2003)
10.1080/1025584031000149089
Finite Element Modeling of Trabecular Bone Damage
V. Kosmopoulos (2003)
10.1007/s10439-005-8960-0
Hydraulic Strengthening Affects the Stiffness and Strength of Cortical Bone
M. Liebschner (2005)
10.1016/j.jor.2017.05.003
A numerical study on stress distribution across the ankle joint: Effects of material distribution of bone, muscle force and ligaments.
Sinu Mondal (2017)
10.1007/s00264-016-3369-y
Segmental acetabular rim defects, bone loss, oversizing, and press fit cup in total hip arthroplasty evaluated with a probabilistic finite element analysis
F. M. L. Amirouche (2016)
10.4271/2005-22-0010
Material properties of human rib cortical bone from dynamic tension coupon testing.
A. Kemper (2005)
Analysis of bone drilling characteristics for the enhancement of safety and the evaluation of bone strength
Fook Rhu Ong (1998)
10.1163/157361108785963019
Subject‐Specific Finite Element Simulation of Bone Grafting Procedure for Osteonecrosis of Femoral Head
Zhi‐Qiang Lian (2008)
10.1007/s00774-011-0308-2
Changes in proximal femur bone properties following ovariectomy and their association with resistance to fracture
H. Fonseca (2011)
Characterization of the bone-implant interface and numerical analysis of implant vibrational behavior for a mechanics based preoperative planning of total hip arthroplasty
A. Rondon (2017)
Predicting Bone Mechanical State During Recovery After Long-Duration Skeletal Unloading Using QCT and Finite Element Modeling
K. L. Chang (2013)
10.1038/nrrheum.2009.107
Hierarchical microimaging of bone structure and function
R. Müller (2009)
10.3390/ijerph15050878
Exercise Early and Often: Effects of Physical Activity and Exercise on Women’s Bone Health
K. Troy (2018)
10.1115/1.4031572
The Effect of Quantitative Computed Tomography Acquisition Protocols on Bone Mineral Density Estimation.
H. Giambini (2015)
10.1016/j.bone.2018.02.011
Pathological fracture risk assessment in patients with femoral metastases using CT-based finite element methods. A retrospective clinical study.
A. Sternheim (2018)
10.1117/12.595799
Novel techniques for high-resolution functional imaging of trabecular bone
P. Thurner (2005)
10.1016/j.medengphy.2016.06.011
Quantifying trabecular bone material anisotropy and orientation using low resolution clinical CT images: A feasibility study.
S. Nazemi (2016)
See more
Semantic Scholar Logo Some data provided by SemanticScholar