Document Type
Conference Proceeding
Publication Date
5-6-2018
Department
Department of Civil, Environmental, and Geospatial Engineering
Abstract
Glass, a brittle material, fractures under tensile stress acting over a time duration. Lateral loads, such as wind, acting on a simply supported rectangular glass lite, put one surface of the lite primarily into tension. ASTM E 1300 defines load resistance of glass as the uniform lateral loading acting over a duration of 3 seconds that is associated with a probability of breakage of 8 lites per 1000 at the first occurrence of the loading. To determine load resistance, the underlying window glass failure prediction model facilitates determination of a probability distribution of 3 second equivalent failure loads, P3. The glass failure prediction model is based on a Weibull distribution, and most people believe the distribution of P3 is, in fact, a Weibull distribution. However, the authors contend that this is not the case. This paper provides an explanation of the glass failure prediction model, its basis, and a discussion of the method for determining surface flaw parameters with an example. The authors demonstrate the distribution of the equivalent failure loads does not follow a Weibull distribution, and they will elucidate the relationship between the distribution of P3 and the Weibull distribution.
Publication Title
Challenging Glass 6: Conference on Architectural and Structural Applications of Glass, CGC 2018 - Proceedings
ISBN
9789463660440
Recommended Citation
Blanchet, S.,
Scott Norville, H.,
&
Morse, S.
(2018).
Probability distributions in the glass failure prediction model.
Challenging Glass 6: Conference on Architectural and Structural Applications of Glass, CGC 2018 - Proceedings.
http://doi.org/10.7480/cgc.6.2188
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15218
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
Copyright © 2018 Samir Blanchet, H. Scott Norville, Stephen M. Morse. All rights reserved. Publisher’s version of record: https://doi.org/10.7480/cgc.6.2188