Crack growth-based multiaxial fatigue life prediction

Zizi Lu, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


A crack growth-based multiaxial fatigue life prediction model is proposed in this paper, which uses a characteristic plane-based methodology for multiaxial fatigue damage analysis and the Equivalent Initial Flaw Size (EIFS) concept for life prediction. The orientation of the characteristic plane is theoretically determined by minimizing the damage contribution of the hydrostatic stress amplitude and correlates with the material local failure modes. An equivalent stress intensity factor under the general multiaxial load is proposed. The fatigue life is predicted by integration from the EIFS to the critical crack length. The proposed model can be used for fatigue life predictions of smooth specimens under both in-phase and out-of-phase loading conditions and can automatically adapt for different material failure mechanisms under various loading conditions. The fatigue life prediction results are validated with experimental data for a wide range of metallic materials available in the literature. It is shown that model predictions are in good agreement with experimental data under both proportional and nonproportional load.

Original languageEnglish (US)
Title of host publication12th International Conference on Fracture 2009, ICF-12
Number of pages10
StatePublished - Dec 1 2009
Externally publishedYes
Event12th International Conference on Fracture 2009, ICF-12 - Ottawa, ON, Canada
Duration: Jul 12 2009Jul 17 2009

Publication series

Name12th International Conference on Fracture 2009, ICF-12


Other12th International Conference on Fracture 2009, ICF-12
CityOttawa, ON


  • Crack growth
  • Critical plane
  • EIFS
  • Life prediction
  • Multiaxial fatigue

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology


Dive into the research topics of 'Crack growth-based multiaxial fatigue life prediction'. Together they form a unique fingerprint.

Cite this