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 theoretically 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 censored 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 specimens 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.

References

1.
Verrill
S. P.
,
Evans
J. W.
,
Kretschmann
D. E.
, and
Hatfield
C. A.
,
Asymptotically Efficient Estimation of a Bivariate Gaussian–Weibull Distribution and an Introduction to the Associated Pseudo-Truncated Weibull, Research Paper FPL-RP-666
(
Madison, WI
:
U.S. Department of Agriculture, Forest Service, Forest Products Laboratory
,
2012
).
2.
Verrill
S. P.
,
Evans
J. W.
,
Kretschmann
D. E.
, and
Hatfield
C. A.
,
An Evaluation of a Proposed Revision of the ASTM D 1990 Grouping Procedure, Research Paper FPL-RP-671
(
Madison, WI
:
U.S. Department of Agriculture, Forest Service, Forest Products Laboratory
,
2013
).
3.
Verrill
S. P.
,
Evans
J. W.
,
Kretschmann
D. E.
, and
Hatfield
C. A.
, “
Reliability Implications in Wood Systems of a Bivariate Gaussian–Weibull Distribution and the Associated Univariate Pseudo-Truncated Weibull
,”
Journal of Testing and Evaluation
42
, no. 
2
(March
2014
):
412
419
, https://doi.org/10.1520/JTE20130019
4.
Verrill
S. P.
,
Evans
J. W.
,
Kretschmann
D. E.
, and
Hatfield
C. A.
, “
Asymptotically Efficient Estimation of a Bivariate Gaussian-Weibull Distribution and an Introduction to the Associated Pseudo-Truncated Weibull
,”
Communications in Statistics– Theory and Methods
44
, no. 
14
(
2015
):
2957
2975
, https://doi.org/10.1080/03610926.2013.805626
5.
Standard Specification for Computing Reference Resistence of Wood-Based Materials and Structural Connections for Load and Resistance Factor Design
, ASTM D5457-19 (
West Conshohocken, P
A
:
ASTM International
,
2019
). https://doi.org/10.1520/D5457-19
6.
Lawless
J. F.
,
Statistical Models and Methods for Lifetime Data
, 2nd ed. (
Hoboken, N
J
:
John Wiley and Sons
,
2003
).
7.
Task Committee on Load and Resistance Factor Design for Engineered Wood Construction
Load and Resistance Factor Design for Engineered Wood Construction: A Pre-Standard Report
(
New York, NY
:
American Society of Civil Engineers
,
1988
).
8.
Green
D. W.
and
Evans
J. W.
,
Mechanical Properties of Visually Graded Dimension Lumber: Vol. 4. Southern Pine, Publication PB-88-159-413
(
Springfield, VA
:
National Technical Information Service
,
1988
).
9.
Green
D. W.
,
Shelley
B. E.
, and
Vokey
H. P.
, eds.,
In-Grade Testing of Structural Lumber, Conference Proceedings 47363
(
Madison, WI
:
Forest Products Research Society
,
1989
).
10.
Evans
J. W.
and
Green
D. W.
,
Mechanical Properties of Visually Graded Dimension Lumber: Vol. 2. Douglas Fir-Larch, Publication PB-88-159-397
(
Springfield, VA
:
National Technical Information Service
,
1988
).
11.
Standard Test Methods for Mechanical Properties of Lumber and Wood-Base Structural Material
, ASTM D4761-18 (
West Conshohocken, PA
:
ASTM International
,
2018
). https://doi.org/10.1520/D4761-18
12.
Standard Practice for Establishing Allowable Properties for Visually-Graded Dimension Lumber from In-Grade Tests of Full-Size Specimens
, ASTM D1990-16 (
West Conshohocken, PA
:
ASTM International
,
2016
). https://doi.org/10.1520/D1990-16
13.
Kretschmann
D. E.
,
Evans
J. W.
, and
Brown
L.
,
Monitoring of Visually Graded Structural Lumber, Research Paper FPL-RP-576
(
Madison, WI
:
U.S. Department of Agriculture, Forest Service, Forest Products Laboratory
,
1999
).
14.
R Core Team
R: A Language and Environment for Statistical Computing
(
Vienna, Austria
:
R Foundation for Statistical Computing
),
2013
, http://web.archive.org/web/20190328220111/https://www.r-project.org/
15.
Krit
M.
,
EWGoF: Goodness-of-Fit Tests for the Exponential and Two-Parameter Weibull Distributions (R package version 2.0)
,
2014
, http://web.archive.org/web/20190328220559/https://cran.r-project.org/web/packages/EWGoF/index.html
16.
Verrill
S. P.
,
Owens
F. C.
,
Kretschmann
D. E.
,
Shmulsky
R.
, and
Brown
L. S.
, “
Visual and MSR Grades of Lumber Are Not 2-Parameter Weibulls and Why It Matters (With a Discussion of Censored Data Fitting)
,” USDA Forest Products Laboratory draft research paper, http://web.archive.org/web/20190328222315/https://www1.fpl.fs.fed.us/weib2.new.pdf.
17.
Verrill
S. P.
,
Owens
F. C.
,
Kretschmann
D. E.
, and
Shmulsky
R.
,
A Fit of a Mixture of Bivariate Normals to Lumber Stiffness–Strength Data, Research Paper FPL-RP-696
(
Madison, WI
:
U.S. Department of Agriculture, Forest Service, Forest Products Laboratory
,
2018
).
18.
Owens
F. C.
,
Verrill
S. P.
,
Shmulsky
R.
, and
Kretschmann
D. E.
, “
Distributions of MOE and MOR in a Full Lumber Population
,”
Wood and Fiber Science
50
, no. 
3
(
2018
):
265
279
, https://doi.org/10.22382/wfs-2018-027
19.
Owens
F. C.
,
Verrill
S. P.
,
Shmulsky
R.
, and
Ross
R. J.
, “
Distributions of Modulus of Elasticity and Modulus of Rupture in Four Mill Run Lumber Populations
,”
Wood and Fiber Science
51
, no. 
2
(
2019
):
183
192
, https://doi.org/10.22382/wfs-2019-019
This content is only available via PDF.
You do not currently have access to this content.