Numerical validation of two-parameter weibull model for assessing failure fatigue lives of laminated cementitious composites-Comparative assessment of modeling approaches

Asad Hanif, Yongjae Kim*, Cheolwoo Park

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In this paper, comparative assessment of failure fatigue lives of thin laminated cementitious composites (LCCs) modeled by two modeling approaches-double-parameter Weibull distribution model and triple-parameter distribution model-was carried out. LCCs were fabricated of ordinary Portland cement (OPC), fly ash cenosphere (FAC), quartz sand, and reinforcing meshes and fibers. The failure fatigue life assessment at various probabilities by the two-parameter model was based on numerical calculations whereas the three-parameter model was applied by an open source program-ProFatigue®. Respective parameters, shape and scale parameters in the two-parameter Weibull distribution model while shape, scale, and location parameters in three-parameter model were determined, and the corresponding probabilistic fatigue lives at various failure probabilities were calculated. It is concluded that the two-parameter model is more accurate in probabilistic fatigue life assessment of double-layer mesh-reinforced LCCs, whereas for single-layer reinforced LCCs, both models could be used at a fair confidence level.

Original languageEnglish
Article number110
JournalMaterials
Volume12
Issue number1
DOIs
StatePublished - 31 Dec 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 by the authors.

Keywords

  • Cementitious composite
  • Flexural fatigue
  • Laminated composite
  • Modeling
  • ProFatigue

ASJC Scopus subject areas

  • General Materials Science
  • Condensed Matter Physics

Fingerprint

Dive into the research topics of 'Numerical validation of two-parameter weibull model for assessing failure fatigue lives of laminated cementitious composites-Comparative assessment of modeling approaches'. Together they form a unique fingerprint.

Cite this