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Title: Visual and MSR grades of lumber are not 2-parameter Weibulls and why this may matter

Source: Journal of Testing and Evaluation. 48(5). 19 p.

Author(s)Verrill, Steve P.; Owens, Frank C.; Kretschmann, David E.; Shmulsky, Rubin ; Brown, Linda S.

Publication Year: 2019  View PDF »

Category: Journal Articles
Associated Research Project(s):   FPL-4714-1B  FPL-4851-2B

Abstract: It has been common practice to assume that a 2-parameter Weibull probability distribution is suitable for modeling lumber strength properties. Previous work has demonstrated theoreti-cally and empirically that the modulus of rupture (MOR) distribution of a visual grade of lumber or of lumber that has been binned by modulus of elasticity (MOE) is not a 2-parameter Weibull. Instead, the tails of the MOR distribution are thinned via pseudo-truncation. Simulations have established that fitting 2-parameter Weibulls to pseudo-truncated data via either full or cen-sored data methods can yield poor estimates of probabilities of failure. In this article, we support the simulation results by analyzing large In-Grade type data sets and establishing that 2-parameter Weibull fits yield inflated estimates of the probability of lumber failure when spec-imens are subjected to loads near allowable properties. In this article, we also discuss the censored data or tail fitting methods permitted under ASTM D5457, Standard Specification for Computing Reference Resistance of Wood-Based Materials and Structural Connections for Load and Resistance Factor Design.

Keywords: 2-parameter Weibull distribution; pseudo-truncated Weibull distribution; machine stress rated lumber; modulus of elasticity binned lumber; visually graded lumber; thinned tail; lumber property distribution; lumber reliability; censored data; 2-parameter Weibull fits

Publication Review Process: Formally Refereed

File size: 436 kb(s)

Date posted: 09/06/2019

This publication is also viewable on Treesearch:  view
RITS Product ID: 94938
Current FPL Scientists associated with this product (listed alphabetically)
Kretschmann, David E.
Research General Engineer
Verrill, Steve P.
Mathematical Statistician

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