Uniaxial and Multiaxial Fatigue Life Prediction of the Trabecular Bone Based on Physiological Loading: A Comparative Study

Fatihhi SJ 1 , Harun MN 1 , Abdul Kadir MR 2 , Abdullah J 3 , Kamarul T 4 , Öchsner A 5 Show all authors , Syahrom A 6

Affiliations 

  • 1 Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Baharu, Malaysia
  • 2 Medical Implant Technology Group, Faculty of Biomedical Engineering and Health Science, Universiti Teknologi Malaysia, Johor Baharu, Malaysia
  • 3 Centre for Computed Tomography and Industrial Imaging, Malaysia Nuclear Agency, Bangi, Malaysia
  • 4 Tissue Engineering Group, NOCERAL, Department of Orthopaedic Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia
  • 5 School of Engineering, Griffith University, Gold Coast Campus, 4222, Southport, Australia
  • 6 Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Baharu, Malaysia. [email protected]
Ann Biomed Eng, 2015 Oct;43(10):2487-502.
PMID: 25828397 DOI: 10.1007/s10439-015-1305-8

Abstract

Fatigue assessment of the trabecular bone has been developed to give a better understanding of bone properties. While most fatigue studies are relying on uniaxial compressive load as the method of assessment, in various cases details are missing, or the uniaxial results are not very realistic. In this paper, the effect of three different load histories from physiological loading applied on the trabecular bone were studied in order to predict the first failure surface and the fatigue lifetime. The fatigue behaviour of the trabecular bone under uniaxial load was compared to that of multiaxial load using a finite element simulation. The plastic strain was found localized at the trabecular structure under multiaxial load. On average, applying multiaxial loads reduced more than five times the fatigue life of the trabecular bone. The results provide evidence that multiaxial loading is dominated in the low cycle fatigue in contrast to the uniaxial one. Both bone volume fraction and structural model index were best predictors of failure (p 

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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