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

M. Sayeed Hasan, A. Faruque, David B. Burr

Research output: Contribution to journalArticle


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


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

ASJC Scopus subject areas

  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Application of artificial neural network for micro-crack and damage evaluation of bone'. Together they form a unique fingerprint.

Cite this