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Probability Density Function Models for Float Glass under Mechanical Loading with Varying Parameters.

Evelien Symoens1, Ruben Van Coile1, Balša Jovanović1

  • 1Magnel-Vandepitte Laboratory, Department of Structural Engineering and Building Materials, Ghent University, Technologiepark-Zwijnaarde 60, 9052 Gent, Belgium.

Materials (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

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Predicting structural glass strength is crucial. This study refines models using the Akaike information criterion to select the best probability function for glass panel strength based on flaw distribution and loading conditions.

Area of Science:

  • Structural engineering
  • Materials science
  • Probability theory

Background:

  • Glass is a vital construction material with increasing use.
  • Predicting structural glass strength requires accurate numerical models.
  • Glass failure is complex, driven by microscopic surface flaws with variable properties.

Purpose of the Study:

  • To extend existing strength prediction models for structural glass.
  • To determine the most appropriate probability density function for glass panel strength.
  • To analyze the influence of various parameters on strength prediction.

Main Methods:

  • Utilized the Akaike information criterion for model selection.
  • Extended the strength prediction model developed by Osnes et al.
Keywords:
crack predictionnumerical modellingparameter studystrength predictionstructural glass

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  • Performed a parameter study to identify key influencing factors.

    • Main Results:

    • The choice of probability density function depends on the number of flaws under maximum tensile stress.
    • Normal or Weibull distributions are suitable when many flaws are loaded.
    • Gumbel distribution is more appropriate when fewer flaws are loaded.

    Conclusions:

    • The Akaike information criterion effectively identifies the best-fit probability model for glass strength.
    • The distribution of flaws significantly impacts the selection of the appropriate strength model.
    • Understanding flaw distribution is key to accurate structural glass strength prediction.