Application of artificial neural network for micro-crack and damage evaluation of bone

M. Sayeed Hasan, A. Faruque, David Burr

Research output: Contribution to journalArticle

Abstract

This paper presents the reasoning and adaptive learning method of artificial neural network (ANN) for micro-crack assessment and damage accumulation due to stiffness loss of dog bone. The importance of using the alternative approach of ANN is that it avoids the complexity of modeling problems, overrides the consideration of simplified assumptions and can be developed directly from experimental data using adaptive learning mechanisms. The proposed artificial neural network model provides a relationship between microdamage accumulation, stiffness loss and number of fatigue cycles (N(f)) to failure from an experimental study where stiffness loss and crack area (Cr.Ar., mm2/mm2) are evaluated. This preliminary study using ANN for microdamage evaluation shows that ANN accurately predicts the amount of damage accumulation from stiffness loss.

Original languageEnglish
Pages (from-to)382-387
Number of pages6
JournalBiomedical Sciences Instrumentation
Volume33
StatePublished - 1997

Fingerprint

Bone
Learning
Cracks
Neural networks
Bone and Bones
Neural Networks (Computer)
Stiffness
Fatigue
Dogs
Fatigue of materials

Keywords

  • Artificial neural network
  • Dog bone
  • Fatigue
  • Micro-crack
  • Microdamage
  • Stiffness loss

ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Application of artificial neural network for micro-crack and damage evaluation of bone. / Hasan, M. Sayeed; Faruque, A.; Burr, David.

In: Biomedical Sciences Instrumentation, Vol. 33, 1997, p. 382-387.

Research output: Contribution to journalArticle

Hasan, M. Sayeed ; Faruque, A. ; Burr, David. / Application of artificial neural network for micro-crack and damage evaluation of bone. In: Biomedical Sciences Instrumentation. 1997 ; Vol. 33. pp. 382-387.
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