In this paper, a novel multiscale hierarchical model based on finite element analysis and neural network computation was developed to link mesoscopic and macroscopic scales to simulate the bone remodeling process. The finite element calculation is performed at the macroscopic level, and trained neural networks are employed as numerical devices for substituting the finite element computation needed for the mesoscale prediction. Based on a set of mesoscale simulations of representative volume elements of bones taken from different bone sites, a neural network is trained to approximate the responses at the meso level and transferred at the macro level.

1.
Rho
,
J. Y.
,
Kuhn-Spearing
,
L.
, and
Zioupos
,
P.
, 1998, “
Mechanical Properties and the Hierarchical Structure of Bone
,”
Med. Eng. Phys.
1350-4533,
20
, pp.
92
102
.
2.
Carter
,
D. R.
, and
Beaupré
,
G. S.
, 2001,
Skeletal Function and Form: Mechanobiology of Skeletal Development, Aging and Regeneration
, 1st ed.,
Cambridge University Press
,
Cambridge, UK
.
3.
Weiner
,
S.
, and
Traub
,
W.
, 1992, “
Bone Structure: From Ångstroms to Microns
,”
FASEB J.
0892-6638,
6
, pp.
879
885
.
4.
Fazzalari
,
N. L.
,
Kuliwaba
,
J. S.
, and
Forwood
,
M. R.
, 2002, “
Cancellous Bone Microdamage in the Proximal Femur: Influence of Age and Osteoarthritis on Damage Morphology and Regional Distribution
,”
Bone
31
(
6
), pp.
697
702
.
5.
Yoo
,
A.
, and
Jasiuk
,
I.
, 2006, “
Couple-Stress Moduli of a Trabecular Bone Idealized as a 3D Periodic Cellular Network
,”
J. Biomech.
0021-9290,
39
, pp.
2241
2252
.
6.
Viceconti
,
M.
,
Taddei
,
F.
,
Jan
,
S. V. S.
,
Leardini
,
A.
,
Cristofolini
,
A.
,
Stea
,
S.
,
Baruffaldi
,
F.
, and
Baleani
,
M.
, 2008, “
Multiscale Modelling of the Skeleton for the Prediction of the Risk of Fracture
,”
Clin. Biomech. (Bristol, Avon)
0268-0033,
23
, pp.
845
852
.
7.
Unger
,
J. F.
, and
Konke
,
C.
, 2008,
Coupling of Scales in Multiscale Simulation Using Neural Networks
,
Elsevier
,
New York
.
8.
Hambli
,
R.
, 2009, “
Statistical Damage Analysis of Extrusion Processes Using Finite Element Method and Neural Networks Simulation
,”
Finite Elem. Anal. Design
0168-874X,
45
(
10
), pp.
640
649
.
9.
Rafiq
,
M. Y.
,
Bugmann
,
G.
, and
Easterbrook
,
D. J.
, 2001, “
Neural Network Design for Engineering Applications
,”
Comput. Struct.
0045-7949,
79
(
17
), pp.
1541
1552
.
10.
Hambli
,
R.
,
Chamekh
,
A.
, and
Bel Hadj Salah
,
H.
, 2006, “
Real-Time Deformation of Structure Using Finite Element and Neural Networks in Virtual Reality Applications
,”
Finite Elem. Anal. Design
0168-874X,
42
(
11
), pp.
985
991
.
11.
Chaboche
,
J. L.
, 1981, “
Continuum Damage Mechanics—A Tool to Describe Phenomena Before Crack Initiation
,”
Nucl. Eng. Des.
0029-5493,
64
, pp.
233
247
.
12.
McNamara
,
L. M.
, and
Prendergast
,
J. P.
, 2007, “
Bone Remodeling Algorithms Incorporating Both Stain and Microdamage Stimuli
,”
J. Biomech.
0021-9290,
40
(
6
), pp.
1381
1391
.
13.
Hambli
,
R.
,
Soulat
,
D.
,
Gasser
,
A.
, and
Benhamou
,
C. L.
, 2009, “
Strain-Damage Coupled Algorithm for Cancellous Bone Mechano-Regulation With Spatial Function Influence
,”
Comput. Methods Appl. Mech. Eng.
0045-7825,
198
(
33–36
), pp.
2673
2682
.
14.
Cowin
,
S. C.
, 2002, “
Mechanosensation and Fluid Transport in Living Bone
,”
J. Musculoskeletal and Neuronal Interact.
1108-7161,
2
(
3
), pp.
256
260
.
You do not currently have access to this content.