United States

Department of

Agriculture

Forest Service

Forest

Products

Laboratory

Research

Paper

FPL 440

M. D. Vanderbilt,

M. E. Criswell,

J. Bodig,

R. C. Moody, and

D. S. Gromola

Stiffness and

Strength of Uniformly

Loaded Floors with

In-grade Lumber
Abstract

To implement more efficient design

techniques for wood joist floor

systems, existing systems with a

history of satisfactory service must be

characterized. Such a procedure,

known as calibration, is necessary to

ensure that new design techniques and

construction materials do not change

the overall acceptability of wood floor

systems.

To characterize such performance, a

structural analysis model and computer

program for floors, FEAFLO, was used

with data on numerous samples of

lumber from sawmill inventory. Over

500 floor analyses yielded estimates of

the distributions of floor stiffness and

strength that will be useful to designers

and code agencies in establishing or

revising acceptance criteria for floors.

Acknowledgments

This report is based to a large extent

upon the Master of Science thesis

prepared by James T. Bufano while he

was a graduate research assistant at

Colorado State University (5). The

studies made using the complete data

set were performed by Eugene

Schaefer, Graduate Research

Assistant, Colorado State University.

The APA plywood data were furnished

by Paul Post and Dr. Michael

O’Halloran of the American Plywood

Association.
Stiffness and

Strength of Uniformly

Loaded Floors with

In-grade Lumber

United States

Department of

Agriculture

Forest Service

Forest

Products

Laboratory1

Research

Paper

FPL 440

November 1983

Introduction

M. D. Vanderbilt, Professor of Civil Engineering

M. E. Criswell, Associate Professor of Civil

Engineering

J. Bodig, Professor of Forest and Wood Sciences

Colorado State University

Fort Collins, Colorado

and

Ft. C. Moody, Engineer

D. S. Gromala, Engineer

Forest Products Laboratory

Madison, Wisconsin

Wood joist floors support loads in a

complex manner involving interactions

among sheathing, joists, and

connectors. Whereas the existence of

these interactions has been known for

some time, lack of adequate analytical

techniques has precluded their proper

consideration in design. Consequently,

the design methods still in use are

based on the assumption that floor

elements act independently.

Component interaction is recognized

only through indirect procedures such

as the National Design Specification’s

(NDS) increased allowable stresses for

repetitive members (15)2 and the

American Plywood Association’s (APA)

glued floor design in which composite

behavior is considered for stiffness but

not for strength (1). Hence current

design techniques stifle innovation and

hinder the utilization of new knowledge

1

 Maintained in cooperation with the University of

Wisconsin.

2

 Italicized numbers in parentheses refer to

literature cited at the end of this report.

gained on the strength and stiffness

distributions of material and connector

properties.

The purposes of this study were to

develop procedures for obtaining

realistic estimates of the stiffness and

strength at first joist rupture of floors

constructed from material with variable

mechanical properties and to use these

procedures to develop definitive data

on expected floor performance.

Definitive data on the stiffness and

strength of wood joist floors are

needed for two reasons. First, accurate

information on the behavior of real

floors built using in-grade lumber at

both design loads and at failure loads

is needed to quantify current floor

performance. This information aids in

the selection of limit states values for

future design provisions, and may also

have a role in the increasingly

important area of product liability.

Second, probability distributions of both

strength and stiffness data and the

resulting distributions of floor behavior

are needed as input to the new

generation of structural design

procedures receiving worldwide

attention. These new probability-based

design methods have been developed

and in some cases adopted for other

materials and in other countries

(14,16,19). To ensure that the

experience gained through many years

of satisfactory floor service is carried

into the new design techniques, the

new design procedures must be

calibrated against existing designs.

Data on floor strength and deflection

performance are vital components of

the calibration procedure.
Background

Colorado State University (CSU) and

the Forest Products Laboratory (FPL)

have engaged in a research project

both individually and cooperatively, to

develop analytical models that can form

the basis for design procedures to

produce acceptable, economical floors.

Researchers at CSU have developed

a method for performing the structural

analysis of wood joist floors using a

finite element modeling of the floor. The

method is presented in a computer

program, FEAFLO (Finite Element

Analysis of FLOors), which was verified

by experimental tests of T-beams and

floors (6,8,9,20,27,22,23,25). The

computer program, used in making

parameter and simulation studies,

forms the basis for proposed new

design methodologies (20).

Cooperative research by CSU and

the Forest Products Laboratory (FPL)

evaluated the behavior of baseline

floors built using a pilot sample of ingrade lumber (13).

For this report, in-grade lumber is

defined as that sampled from sawmill

production. Lumber data were collected

by the West Coast Lumber Inspection

Bureau, the Western Wood Products

Association, and the Southern Pine

Inspection Bureau as part of a

cooperative research effort with the

FPL (7). A rather limited set of field

data-consisting of modulus of

elasticity (MOE), modulus of rupture

(MOR), and cross-section dimensions

for 60 lots of 10 joists each-that was

available for the demonstration

computer analyses is referred to as the

sample data set. The more extensive

data for over 550 lots that became

available late in this study is denoted

as the complete data set.

The scope of this research was

limited to consideration of one typical

floor configuration.

Research Methods

Many analyses of floors were

performed to determine cumulative

distributions of floor deflections and

strengths. Joist strength and stiffness

data were used both at the moisture

content conditions at the time of test

and adjusted using various moisture

correction procedures. The adjustment

for moisture was made for part of the

data to determine the possible effects

of seasoning on performance.

Analysis Program

The input to the FEAFLO program

consists of a description of the floor

geometry, the material MOE values,

and the connector locations and

stiffnesses. The MOE and connector

stiffness values are constant along the

length of each joist, deflections are

assumed small, and the computed

behavior is linear. As part of a previous

study (13) the FEAFLO program was

used either to input joist MOE values

individually or to specify that the values

be selected from a normal distribution.

The process of selecting values from a

distribution, instead of directly inputting

values, is described as a simulation

procedure. Because values are picked

at random, the method is often

described as Monte Carlo simulation

(11). The FEAFLO program permits joist

and sheathing properties to be input or

simulated in seven different modes

(table 1). The mechanics of inputting

constant, variable, or simulation data

are described by comment cards in the

listing of the program. A statistical and

plotting routine was used to arrange

input and output data, to compute

statistics such as means and standard

deviations for such variables as joist

MOE, midspan joist deflections, joist

stresses, etc., and to plot cumulative

distributions of input and output data.

For this study, all floors were analyzed

using Mode 6. Joist properties were

input in 10-piece serial lots as they

were obtained in the in-grade testing

program (7).

Floor Analyses

To simulate a complete distribution

of performance, it was necessary to

limit the number of floor configurations

to be analyzed. This report contains

the results of computer analyses of

one typical floor construction (fig. 1).

The floor contains 10 simply supported

2- X 8- inch (nominal) joists and two

additional nondeflecting joists on the

edges. The single layer of nominal

5/8-inch plywood sheathing is simply

supported and spans perpendicular to

the joists. Maximum allowable spans

for the two species (75) considered in

the in-grade testing program are shown

in figure 1.

Figure 1.--Data for study floors from

NDS (15) design values:

Douglas-fir:

MOE = 1,700,000 Ib/in.2

Fb(repetitive) = 1,450 lb/in.2

Southern pine:

MOE = 1,600,000 Ib/in.2

Fb(repetitive) = 1,400 Ib/in.2

Joist span:

Douglas-fir = 157 in.

Southern pine = 154 in.

Nominal joist dimensions: 2 x 8

Sheathing thickness: 19/32 in.,

touchsanded

Nail size and spacing: 8d, 6.7 in.

Connecter stiffness: 30,000 Ib/in.

Uniform load: 40 Ib/ft2

Tightly butted gaps

2
Table 1 .—FEAFLO analysis modes

Mode Number Joist Sheathing

of floors MOE MOE

1 1 Assigned constant1 Assigned constant

2 1 Assigned variable2 Assigned variable 3

3 n4 Simulated5 Assigned constant 3

4 n Simulated Assigned variable 3

5 n Assigned variable per simulation run Simulated 6

6 n Assigned variable per floor Simulated

7 n Simulated Simulated

1 All members given the same properties per layer.

2

 Properties manually assigned to individual members, thus may vary between members.

3

 In modes 3, 4, and 5, assigned constant or assigned variable properties remain constant

for each floor in a simulation run.

4

n = ≤ 100.

5

 Material properties automatically assigned to members by Monte Carlo methods.

6

 Mode 6 presents strength-as well as stiffness-estimates using joist MOR values. Joist

properties are manually assigned for each individual floor in a simulated run.

Previous studies have shown that

the distributions of floor response are

affected more by the variability of the

joists than by any other component,

particularly for uniformly loaded floors.

Thus, the floors analyzed included an

emphasis on the properties of the

joists. Other floor component

properties were modeled by constant

values based upon best estimates of

the mean property values. For selected

studies, sheathing stiffnesses were

varied from panel to panel using

industry-provided estimates of plywood

variability to determine the effects of

varying this parameter.

One parameter that has a significant

effect on floor performance is the

stiffness of the short finite element

used to model the contact surface

between adjacent sheets of plywood.

This contact region (“gap,” fig. 1), has

been studied at CSU, and

recommended stiffness values have

been developed. For the sheathing

shown in figure 1, the recommended

value of gap modulus of elasticity (E) is

500 lb/in.2

, which, when divided by the

gap length (L) of 0.1 inch, gives a gap

E/L value of 5,000 Ib/in.2

/in. Because of

an oversight, a value of 50,000 lb/in.2

/in.

was used for the floors analyzed using

the 60 lots of sample data. This value

of gap stiffness does not affect the

relative ranking of floor performances

and thus the analyses were not

repeated for these sensitivity studies

using the sample data. The intended

E/L value of 5,000 was used with the

complete data group that included all of

the sample data. Also with the

complete group, a more flexible E/L

value of 500 was used for the gaps

parallel to the “along-joist” direction

because these gaps are not tightly

compressed as the floor deflects. The

value of E/L used for these gaps along

the joists has almost no effect on the

behavior of uniformly loaded floors.

Joist Properties

The in-grade field testing program

was conducted by testing joists in

lo-piece serial lots from mill inventories

of visually graded material using

portable testing equipment (7). The

MOE obtained from the load-deflection

behavior, the MOR obtained from the

failure load, the moisture content (MC),

the joist grade and species, and crosssectional dimensions were recorded.

Most of the data were grouped in

lots of 10 joists. The “sample data”

included 30 lots of Douglas Fir-Larch3

joist data and 30 lots of southern pine

data. These data were used in studying

effects of moisture adjustments,

sheathing type, and sheathing

variability on floor performances. The

complete data became available late in

the study and were not as extensively

studied. The complete data included

lots of 10 consecutive joists in two

categories: (a) “as-graded,” and (b)

lots containing only joists verified by

the grading agencies as being “ongrade.” “As-graded” lots had a

mixture of lumber stamped No. 2, No.

1, and Select Structural grade while all

of the higher-grade material and that

determined to be off-grade were

eliminated in forming the “on-grade”

lots. Thus, all joists in the “on-grade”

lots were visually mill graded as No. 2

and verified to be that by a grading

supervisor.

The data available and the analyses

performed are described in table 2.

Properties of the 10 joists in each lot

were obtained from the field test data.

A few of the lots in the complete data

set contained 11 joists. The data for

the 11th joist were ignored. Also, a few

lots contained data for fewer than 10

joists. For these lots, the available

joists were assigned starting at joist 1

and data for the first one or more joists

were duplicated in a symmetrical

fashion about the centerline of the floor

as needed to define properties for all

joists.

3

 For the remainder of the report, the marketing

group of Douglas Fir-Larch is referred to as

Douglas-fir.

3
Moisture Adjustments for Table 2.—Data available and analyses performed

Joists

The thickness and width of a joist

decrease as the MC decreases below

the fiber saturation point. Most

mechanical properties of clear wood,

including MOE and MOR, increase as

the MC decreases below the fiber

saturation point. There is ample

evidence, however, that seasoning has

a variable effect on change in strength

of structural lumber and might even

result in strength reduction for some

joists, especially for those having large

knots (12).

The MOE, MOR, thickness, and

width of joists each vary at different

rates as a function of MC. Meaningful

comparisons of floor behavior as a

function of joist stiffness and strength

variability can best be made with all

joists at a standard MC. The sample

MOE and MOR data were adjusted to

an assumed equilibrium MC of 12

percent using ASTM Standard D 2915

(3). The adjusting equations are

(1)

(2)

where 1 and 2 refer to two MC’s in

percent. Equations based on the work

of Wood and Soltis (27) were

developed by FPL to adjust joist

dimensions:

(3)

(4)

where T = thickness (approximately

1-1/2 in. for a 2 X 8) and

W = depth.

Joist type

in floor

Number of

lo-joist

lots

Moisture condition

and adjustment

Modulus of Modulus of

elasticity rupture’

Plywood

properties

SAMPLE DATA

Douglas-fir 30

Southern pine 30

Green

Dry, ASTM2

Dry, ASTM

Dry, ASTM

Dry, ASTM

As received

Dry, ASTM

COMPLETE DATA5

Douglas-fir, as-graded

Douglas-fir, on-grade

Southern pine, as-graded

Southern pine, on-grade

6

138 As received

108 green

30 dry 6

177 As received

138 green

39 dry

107 As received

137 As received

Green

Dry, ASTM

Dry, EVSF3

Dry, ASTM

Dry, ASTM

As received

Dry, ASTM

As received

green

dry As received

green

dry As received

As received

CSU

CSU

CSU

APA

APA

CSU

CSU

APA

APA

APA

APA

APA

APA

APA

APA

1

 All capacities computed using linear FEAFLO were multiplied by 0.90 to approximately

compensate for linear-nonlinear difference of fig. 10.

2

 Material properties adjusted using eqs. (1) and (2); dimensions adjusted using eqs. (3) and

(4).

3

 MOR adjusted using empirical variable seasoning factor (fig. 2); all other adjustments

made as above.

4

 Coefficient of variation = 0.26 for all El and EA values for this floor.

5

 The complete data set includes the sample data; no moisture correction applied.

6

 138 lots as-graded included 108 green and 30 dry lots; 177 lots on-grade = 138 green +

39 dry.

In equations (1) through (4), MC2

was 12 percent and MC, was the astested value. The southern pine astested values ranged from 9 to 30

percent with a mean of about 15

percent for the 30-lot sample of table

2. The 30-lot sample of Douglas-fir

joists was reported as green and these

MOE and MOR values were corrected

from a green MC of 24 percent to the

seasoned value of 12 percent. The 24

percent value was reported as the

point at which material properties begin

to change with drying (24) and was the

maximum value used in equations (1)

and (2). Shrinkage computations were

made using MC, as not more than 28

percent.

A second procedure was also used

in modifying MOR. Termed the

empirical variable seasoning factor

(EVSF), this factor was based on work

by Madsen (12) with some additional

modifications. In drying from green

conditions to 12 percent moisture

content, the EVSF is assumed to vary

between 1.0 and 2.0 (fig. 2) depending

upon a joist’s percentile ranking within

the green MOR distribution. Lots within

the same grade and species were all

combined to form the MOR distribution.

This second procedure was selected

for demonstration purposes only and is

not recommended as a universal

procedure. The EVSF modifications of

joist MOR values were accompanied by

adjustments of the joist MOE and sizes

using equations (1) (3) and (4).

4
Plywood Properties Table 3.—Plywood stiffness values

The FEAFLO program models a floor

as a set of crossing beams. One set

comprises the joists and sheathing

acting as T-beams in the floor span

direction. The second is obtained by

dividing the sheathing into strips

spanning perpendicular to the T-beams.

Two properties of the sheathing are

required in each direction. These are

the EA and El values where E =

modulus of elasticity, A = area, and

I = moment of inertia. The plywood is

assumed to be placed with the face

grain perpendicular to the joists and it

is convenient to describe the EA and El

values as parallel (II) or perpendicular

(⊥) to the face grain.

Rigidity1

Plywood2

Design

Specification

EliI (kip in.2/ft) 184.5 220.8 287.5

El⊥ (kip in.2

/ft) 28.5 61.4 106.3

EAII (kip/ft) 3,927 5,723 7,950

EA⊥ (kip/ft) 2,415 3,640 6,525

1 EA = axial rigidity per foot of width.

El = flexural rigidity per foot of width.

II,⊥ = properties parallel and perpendicular to face grain of panel.

2

 PDS values represent minimum values considering all available ply layups. Because

minimum values for each direction occur for different layups, it is impossible to obtain a

panel for which all the minima occur simultaneously; hence, these values are too

conservative for use in floor analysis (2).

Two sets of values were used. The

first was computed at CSU using a

plywood layup having Group 2 face

plies and Group 3 interior plies from

the Plywood Design Specification

(PDS) (2). The second set was

furnished by the APA as “best

estimates” of these properties. The

APA values are higher than the CSU

values (table 3) primarily because of

the conservative E values used in the

CSU computations. According to APA,

a high percentage of production uses

stiffer Group 1 species in the layups

instead of the Group 2 and 3 permitted

and assumed for the CSU

computation.

3

 Computed using Group 2 faces and Group 3 interior plies (2).

4

 Actual test data for El and calculated for EA. These values represent industry production,

which typically utilizes 90 pct or more of Group 1 woods (2).

5

 COV for APA values = 0.26. Limited data indicate the COV for EA is larger than the

COV’s for the other rigidities. The COV for EA was assumed to apply to the other values in

the study reported herein.

Connector Properties contribution to floor strength can be

reasonably accounted for by a

Methods of determining the nonlinear correction factor. A factor has been

load-slip curve for nails is a subject of estimated based on analyses of three

current research. For this study, a selected floors using both the linear

30,000-lb/in. connector stiffness value model and the nonlinear analysis

was chosen based on previous described by Wheat et al. (26).

research (25) to best represent the

conditions at the service load level for

an 8d common nail connecting

Douglas-fir plywood to a joist with a

density near that of southern pine or

Douglas-fir. A constant 6.7-inch nail

spacing was specified to approximate

the correct number of nails in the floor,

considering that nail spacing is closer

on sheathing edges than in the interior

of the sheet.

MEMBER PERCENTILE

Figure 2. —Description of empirical variable

seasoning factor (EVSF). (ML83 5170)

CSU3

calculated

values

APA

average

values4,5

Because connector stiffness is

nonlinear and decreases with additional

nail slip, the connector stiffness at

overload and failure load levels is less

than at service load level. This

nonlinearity can be included in a

nonlinear analysis (26) or it can be

approximated by specifying a reduced

connector stiffness. In this study, with

a single connector stiffness of 30,000

lb/in. the resulting overestimation of the

connector stiffness at overloads and its

5
Service Load Response

Measures of Deflection

Performance

Three measures of deflection

response were obtained for each floor:

The average deflection of the eight

interior joists, the largest average of

any three adjacent joists, and the

largest individual joist deflection.

Deflections in all cases were at

midspan. The average deflection of the

interior eight joists is a measure of

mean floor performance and is

hereafter referred to as the “mean”

deflection. The deflections of joists 1

and 10 in (fig. 1) were excluded from

this average because they are reduced

by the parallel supports. The largest

average of any three adjacent joists is

a measure of the “soft-spot”

performance. The third measure,

maximum single joist deflection,

typically, but not always, occurs for the

joist with the lowest MOE.

In a hypothetical floor constructed

with materials of uniform stiffness and

having edges that are free to deflect,

the three deflection measures will be

identical. In real floors with variable

properties, the mean deflections will be

the smallest and will show the least

variability; the soft-spot deflection

values will be intermediate both

numerically and in their variability; and

the maximum single joist deflections

will be the largest and the most

variable. All three measures are

influenced by floor size. Thus, all three

are reasonable measures of the relative

performance of floors all having the

same number of joists.

Douglas-fir Floors, Sample

Data

Effects of MC on Deflection

Performance

The MC adjustment made using

equations (1) (3), and (4) resulted in

increasing joist MOE by 25 percent and

decreasing the moment of inertia (MOI)

by 12.6 percent. The final change in

joist El due to this assumed seasoning

was (1.25 X 0.874) X 100 percent =

109 percent. This 9 percent increase

agrees fairly well with limited test

results (27). To provide further

demonstrations of the effects of joist El

on floor performance, analyses were

also made using an arbitrary joist El

increase of 22 percent, which is

believed to be greater than any

possible increase.

Cumulative distribution functions

(CDF) of mean deflections are shown

(fig. 3, upper) for three MC conditions.

Thirty points were used to draw each

curve; each point was obtained using

the joist data for one lot with the

individual joist values assigned in the

order given in the lot. Each point

represents a separate floor analysis

made holding all material properties

constant (fig. 1, table 3 (CSU

calculated plywood values)) except joist

properties. Each distribution curve (fig.

3, upper) has the same general shape,

as all joists underwent the same

seasoning correction. The average

decrease in mean deflection from green

to 12 percent MC is 4 percent when El

is increased by 9 percent. Increasing El

by 22 percent decreased the average

deflection by 12 percent. The changes

in deflections are smaller than the

changes in El because the floor acts as

a complex system in which the joist

plays a major but not exclusive role.

The larger the increase in El, the larger

is the corresponding change in

deflection because the greater the El,

the greater is its influence on the

system.

The span over 360 deflection value is

noted in each distribution of joist

deflections. This span/360 quantity is a

customary deflection limitation value

widely used in the simplified bare joist

design of floors. As currently used in

design, it implies that this value is at

the 50th percentile of a distribution of

floor deflections. It is cited here

primarily because it is familiar to most

designers and researchers of wood

floors. Actual floors with composite

behavior, designed according to the

current simplified procedures with

average-quality material, should deflect

less than this customary design value.

The correct service load deflection limit

state for use with both improved

analysis and design procedures and

actual material properties remains to be

defined. Its selection is complicated by

the subjective and variable judgments

of occupants of wood joist floor

structures on what are acceptable floor

deflection and vibrational properties

(17,18).

In addition to mean floor deflection,

figure 3 includes cumulative distribution

plots for soft-spot and maximum single

joist deflections.

Sheathing Stiffness Effects on

Deflections

Effects of varying sheathing stiffness

were studied by analyzing 30 floors

using the 30 lots of Douglas-fir data

adjusted to 12 percent MC and 3 sets

of plywood values (table 3): The CSU

values, the APA values without

variability, and the APA values with a

coefficient of variation (COV) of 26

percent (fig.4). In the study of the floor

response for a COV of 26 percent, the

plywood El and EA values for each

sheet were randomly selected from a

normal distribution that was truncated

three standard deviations above and

below the mean.

6
Figure 3.–Cumulative mean (upper), soft-spot ,(center), and 

maximum single (lower) Joist deflections, Douglas-fir sample 

data. (ML83 5169) 

7
Cumulative distributions for mean

floor deflections and for maximum

single joist deflections are plotted in

figure 4; soft-spot distributions fell

about halfway between the other

values, allowing several observations.

First, the values for the APA plywood

with and without variation are so close

that, for all practical purposes,

variability of plywood can be omitted

from consideration in the design of

uniformly loaded floors. However, it is

expected that point (concentrated)

loaded floors will be more sensitive to

plywood variations. Second, the APA

deflections are about 8 percent less

than the CSU deflections largely

because the APA EA⊥ value is 80

percent greater than the CSU values.

Of the four EA and El values, the EA⊥

value has the greatest influence on the

behavior of uniformly loaded floors

because it controls the stiffnesses of

the compression flanges of the

T-beams. (Note that the plywood

perpendicular to the face grain direction

is parallel to the joists.)

Comparisons of Deflection

Performance Criteria

Distributions of floor deflection as

measured by the three deflection

criteria are shown for green joists and

CSU plywood (fig. 5, upper) and for

seasoned (12 pct MC) joists and APA

plywood (fig. 5, lower). Values for

seasoned joists and CSU plywood lie

between those values and are not

shown. Deflection statistics are

summarized in table 4. The plots and

table show two results of interest.

First, the ratio of mean interior

deflection to mean maximum single

joist deflection is about 0.89 for each

of the moisture conditions listed in

table 4. The ratio of mean soft-spot to

maximum single joist deflection is

about 0.95. These differences are not

very great for the uniform loading

condition, the shapes of the distributions are similar, and any one of

the criteria appears satisfactory for use

in calibrating future design. Second, all

floors were considerably stiffer (i.e.,

deflected less) than the span/360 value

shown in each figure. In fact, the

maximum single joist deflection of all

60 floors was less than span/360.

Figure 4.—Mean (upper) and maximum single (lower) joist

deflections, Douglas-fir sample data, dry, with CSU and

APA plywood data (see table 3). (ML83 5171)

8
Figure 5. –Deflection performance criteria for Douglas-fir 

sample data, green (upper) and dry (lower), with APA 

plywood values and no plywood variation. (ML83 5172) 

Table 4.–Summary of deflection statistics, Douglas-fir sample data 

Moisture

condition

Deflection statistics Deflection

performance Range Mean 

criteria Low High Mean COV ÷ 

L/360

- - - - - - -In. - - - - - - - 

Green, Mean 0.246 0.383 0.296 0.109 0.679

CSU Soft-spot .253 .406 .318 .116 .729

plywood data Maximum single .262 .427 .333 .120 .763

Adjusted to dry, Mean .234 .368 .283 .111 .649

CSU Soft-spot .241 .391 .304 .119 .697 

plywood data Maximum single .250 .412 .319 .122 .731

Adjusted to dry, Mean .216 .332 .258 .106 392 

APA Soft-spot .222 .352 .277 .114 .635 

plywood data, Maximum single .229 .368 .289 .117 .662 

no variability 

Adjusted to dry, Mean .215 .332 .260 .110 .596 

APA Soft-spot .221 .358 .280 .106 .642 

plywood data, Maximum single .228 .370 .292 .124 .670 

0.26 COV 

9
Douglas-fir Floors, 

Complete Data 

Cumulative distributions of floor 

deflections for the complete set of data 

are shown in figures 6 and 7 in the 

form of span/deflection ratios. 

Statistics are given in table 5. An 

estimate of the 5th and 50th 

percentiles (by count) along with the 

360 value of the span/deflection ratio 

are identified on each plot for purposes 

of comparison only. The 5 percent and 

50 percent values (by count) for 

maximum single joist deflections 

correspond to a span/deflection ratio of 

about 330 and 420, respectively. 

Similar values for mean joist deflection 

are 380 and 460, respectively. The 

soft-spot data fall about midway 

between the individual and mean joist 

data. The deflections observed are 

somewhat different from the sample 

data because, as previously noted, a 

lower, more realistic gap stiffness value 

was used with the complete joist data. 

reported in the green condition were 

used in computing the CDF of figure 6, 

center, and the reported dry data were 

used for figure 6, lower. The 

performance of the dry floors appears 

to be poorer than the performance of 

the total sample (fig. 6, upper). 

However, the sample size of 30 is 

small and the more meaningful CDF’s 

are those in the total sample. 

shown in figure 7. 

The as-graded lots that were 

The CDF’s for the on-grade data are 

Southern Pine Floors,

Sample Data

In cumulative plots for mean

deflections (fig. 8, upper) and maximum 

joist deflections (fig. 8, lower) for the

southern pine floors, the curves for the

two moisture conditions do not display

the similarity of shapes shown by the

Douglas-fir floors because of the

variable moisture adjustments applied

to the southern pine. The average MC

adjustment was small and hence the

two curves on each figure are close 

together. For example, the mean

deflection for the as-received condition

was 0.289 inch, which is only 1.7

percent greater than the 0.284-inch

value for the dry case. Deflection data 

are summarized in table 6.

Figure 6.–Cumulative span-deflection ratios, Douglas-fir

as-graded, 138 lots (upper), of which 108 were green 

(center) and 30 were dry (lower). (ML83 51 73) 

10
Figure 7.—Cumulative span-deflection ratios, Douglas-fir

on-grade, 177 lots (upper), of which 138 were green

(center) and 39 were dry (lower). (ML83 5174)

11
Table B.-Summary of span-deflection statistics, complete data

Number

of

floors

Joist type

in floor

107

137

138

108 green

30 dry

177

138 green

39 dry

Southern pine,

as-graded

Southern pine,

on-grade

Douglas-fir,

as-graded

Douglas-fir,

on-grade

Deflection

performance

criteria

Distributions

shown in

Mean

soft-spot

Maximum single

Figure 9, upper

Mean

soft-spot

Maximum single

Figure 9, lower

Mean

Soft-spot

Maximum single

Figure 6, upper

Mean

Soft-spot

Maximum single

Figure 6, center

Mean

soft-spot

Maximum single

Figure 6, lower

Mean

soft-spot

Maximum single

Figure 7, upper

Mean

soft-spot

Maximum single

Figure 7, center

Mean

soft-spot

Maximum single

Figure 7, lower

Range

343-691

305-651

295-605

352-681

316-647

306-580

Span-deflection

ratio statistics

Mean

478

443

417

474

438

415

342-678 464

315-587 434

303-542 416

342-678 472

315-587 441

303-542 422

357-552 437 .119

322-509 412 .121

306-488 394 .123

334-570 446 .112

290-533 416 .121

264-508 398 .118

334-566 450 .107

290-533 420 .117

264-493 400 .114

342-570 433 .126

318-530 405 .132

306-508 390 .131

Table 6.—Summary of deflection statistics, southern pine sample data1

Moisture

condition

Deflection

performance

criteria

Deflection statistics

Range Mean

Low High Mean COV ÷

L/360

cov

0.133

.145

.142

.134

.143

.143

.118

.116

.119

.113

.111

.114

– – – – – – –In. – – – – – – –

As-received Mean 0.223 0.394 0.289 0.129 0.676

soft-spot .233 .425 .313 .138 .732

Maximum single .249 .431 .333 .134 .778

Adjusted to Mean .220 .387 .284 .129 .664

dry soft-spot .231 .418 .308 .137 .720

Maximum single .248 .423 .328 .136 .767

1

 Plywood stiffness based on CSU calculations.

12
Figure 8.—Cumulative mean (upper) and maximum single

(lower) joist deflections, southern pine sample data. (ML83

5175)

Southern Pine Floors, Discussion of Performance

Complete Data Criteria

The deflection data for the southern Each of figures 6, 7, and 9 shows

pine as-graded (fig. 9, upper) and on- several possible ways of defining

grade (fig. 9, lower) MOE values are acceptable performance. Deflections

quite close to the results for the are reported for three cases: the

Douglas-fir. Again, the lower sheathing average of the eight interior joists

gap stiffness value used with the (mean), the softest of the eight sets of

complete joist data is responsible for at contiguous three joists in a floor (softleast part of the difference between the spot), and the joist in each floor

sample and the complete data results. exhibiting the maximum deflection.

Various statistics are given in table 5. These are compared with two criteria:

The lower 5th percentile usually

associated with allowable stresses in

engineered wood products and the

50th percentile often associated with

stiffness criteria. The percentile

corresponding to the historic L/360

value is a third possible criterion (table 7).

If the acceptance criterion is chosen

to be the L/360 limit, then from 5 to 21

percent of the floors deflect more than

this limit depending upon the measure

of performance chosen. The

applicability of the L/360 limit for actual

floor performance, as opposed to

calculations using bare joists and mean

MOE, has not been yet resolved. This

limit certainly cannot be expected to

apply equally to these three different

deflection measures.

One important source of evidence on

which deflection performance is now

judged to be adequate is to examine a

reasonably lower bound of values now

being provided. If it is assumed that 95

percent of the floors built using ongrade Douglas-fir and APA plywood

are satisfactory, then the

corresponding span/deflection ratios

range from 320 to 360, depending

upon the performance measure

selected. An infinite number of other

values could be obtained by choosing

other exclusion limits and span/

deflection criteria.

Design values of MOE have

historically been selected to represent

the population mean for the species

and grade considered. Thus, it is

interesting to compare mean floor

performance to the commonly used

L/360 limit. The mean ratios (table 5)

range from 8 to 33 percent greater

than the L/360 value, showing that the

mean floor behaviors were better than

the bare-joist design limit. This would

certainly be expected due to the

consideration of composite action.

There is no consensus on what

constitutes the best measure of

acceptable floor performance. A

generally accepted definition is required

for use in calibrating probability-based

procedures for floor design, especially

if both concentrated and uniform loads

are to be considered.

13
Failure Load Response

Figure 9.—Cumulative span-deflection ratios, southern pine

as-graded, 107 lots (upper) and on-grade, 137 lots (lower).

(ML83 5176)

Table ‘I.-Three criteria for judging acceptability of floors with on-grade Douglas-fir

joists.

Measure Span/deflection Span/deflection Percentile

of ratio at lower ratio at corresponding

performance 5th percentile 50th percentile to L/360

Mean 360 440 5

Soft-spot 330 420 13

Maximum single 320 400 21

Linear Prediction Technique

Although floor strength is a function

of all the factors affecting deflections, it

is most greatly influenced by joist

MOR. As shown by Wheat et al. (26),

floor strength at first joist failure can be

predicted accurately only if paired

MOE-MOR data for each joist are used

with a nonlinear analysis program. An

approximate nonconservative estimate

can be made using paired data with the

linear FEAFLO program. The linear

prediction technique used in obtaining

the strength data presented in this

paper consists of determining the

uniform load at first joist rupture by

linearly extrapolating joist stresses at

design load using the expression

Wf = Ws (MORI/σI) (5)

where Wf = failure load, Ib/ft2,

Ws = service load of 40 Ib/ft2

,

MORI

 = MOR of controlling joist,

and

σI = stress in controlling joist

at 40-lb/ft2

 loads,

Ib/in.2.

The controlling joist for a given floor

is the joist that gives the lowest

predicted failure load.

Equation (5) results in overestimating

floor strength at first joist failure

because it is based on the assumption

of linear behavior to failure and on a

connector stiffness chosen to be most

valid in the service load range. In reality

there are several sources of nonlinear

material behavior with increasing floor

load. These include nonlinear loaddeflection behavior of joists, sheathing,

gaps, and connectors.

In the floor-strength model,

connector nonlinearity was assumed to

be the dominant nonlinear effect. Thus,

the stiffnesses of the joists, sheathing,

and gaps were assumed to be linear.

Strengths predicted by linear and

nonlinear analyses (for connectors only)

for three floors were compared (fig. 10)

with the linear analysis from FEAFLO

and the use of equation (5) and the

nonlinear analysis from the techniques

described by Wheat et al. (26). The

three floors consisted of green

Douglas-fir joists and were chosen to

represent low, average, and high

strengths with respect to the sample

14
Figure 10.–Controlling joist MOR versus 

floor strength, linear and nonlinear analyses. 

(ML83 5178) 

population. The linear model with the 

service load connector stiffness value 

overestimated capacities by 8 percent 

(low MOR) to 12 percent (high MOR). 

overpredicts strength, it apparently can 

provide a reasonable estimate of 

strength at virtually no additional cost 

after the service performance analysis 

has been made. The linear model also 

provides a useful way to study the 

effects of the MC adjustment technique 

and other behaviors. 

Whereas the linear model 

Effects of MC Adjustment 

on MOR 

Determining the effects of MC 

changes on MOR is complicated by the 

necessity of performing destructive 

tests to measure MOR. Hence defining 

effects of MC change on MOR must be 

made by testing many similar 

specimens at various MC levels. 

Because of the uncertainty in 

describing MOR adjustments due to 

MC changes, two techniques were 

studied using the sample data, the 

ASTM method of equation (2) and the 

empirical variable seasoning factor 

(EVSF) (fig. 2). 

The ASTM adjustment was applied 

to the 30 lots of both Douglas-fir and 

southern pine joists to adjust their 

MOR values to a 12 percent MC. 

Because all the Douglas-fir joists were 

adjusted from the assumed green or 

“intersection” MC of 24 percent to the 

seasoned value of 12 percent using 

equation (2), each MOR value for 

Douglas-fir was given by the 

expression

MORdry = 1.42 MORgreen (6)

This adjustment differs from the 35 

percent increase in MOR given by 

ASTM D 245 (4) for drying from green 

to 15 percent maximum MC, because 

of the assumed value of the 

“intersection” moisture content (MC1)

in equation (6). To obtain an 

adjustment of 35 percent, a value of 

MC1 of 22.5 percent must be used (10).

The MOR adjustment for southern pine 

differed for each piece as reported MC 

values ranged from 8 to 30 percent. 

The average adjustment was an 

increase of 8 percent. 

The EVSF procedure is based on the 

theory that weaker joists do not 

improve with seasoning as much as do 

stronger joists. Lower strength joists 

may more often develop seasoning 

defects such as checks and shakes 

that can further reduce their strength. 

The EVSF was assumed to be 1.0 for 

the joists in the lower 5th percentile 

although there is some evidence that 

lower strength joists can lose strength 

with seasoning (12). The EVSF method 

was used only with the Douglas-fir

data. It was assumed that the southern 

pine data reflected the effects of 

seasoning defects as these data were 

reported for an average MC of 15 

percent.

In all of the floor strength analyses in 

which moisture corrections were made, 

the joist width and thickness were 

adjusted in computing the MOI (and 

section modulus), and the MOE and 

MOR were adjusted as previously 

described. Hence the deflection 

calculations consider these dimensional 

changes. However, because of a 

programming oversight, the flexural 

strains were computed using the 

nominal depth of 7.25 inches rather 

than the actual depth. For the Douglasfir joists, the actual depth using 

equation (4) should have been 7.25 × 

(1 – 0.0345) = 7.00 inches. Hence the 

flexural component of the total strain 

for the Douglas-fir floors was 3.5 

percent too high. The Corresponding 

average error for the southern pine 

floors was 0.7 percent too high. The 

capacities reported in the following 

section and in figures 11 through 13 

are thus slightly in error but the 

relative rankings are still correct.

The nominal depth error and gap 

E/L = 50,000 error do not occur in 

figure 10. 

Effects of MC Adjustment 

on Failure Loads 

The cumulative distributions of failure 

loads, based on predicted first joist 

failure, for floors of southern pine joists 

and CSU plywood are nearly the same 

for the two conditions, with and 

without the moisture content 

adjustments (fig. 11, upper). This 

similarity is due to the small average 

ASTM seasoning adjustment for MOR 

of only 8 percent for the 30 lots of 

sample data. The mean failure load for 

the as-received floors (no MC 

adjustment) was 190 Ib/ft2, and for the 

assumed-dry condition (12 pct MC) 

was 6 percent higher, 201 Ib/ft2.

Mean failure loads of the Douglas-fir

floors adjusted to dry using the ASTM 

procedure are about 22 percent greater 

than the mean for floors with green 

lumber and CSU plywood (fig. 11, 

lower). The effect of using APA 

plywood with its 80 percent greater EA 

value, compared to the CSU plywood, 

is to increase the mean strength by 5.4 

percent. This shows, as expected, that 

increasing sheathing stiffness has little 

influence on increasing strength. 

Statistics are summarized in table 8. 

The combination of increasing MOE 

by 25 percent using equation (1), 

reducing MOI by 16 percent using 

equations (3) and (4), and increasing 

MOR by 0 to 100 percent using figure 

2 results in a net decrease in strength 

except for the strongest floors (EVSF 

curve, fig.11, lower). The reason is 

that the seasoning correction increases 

the stiffness of the joist layer with 

respect to the sheathing layer, thus 

increasing the proportion of the total 

strain energy carried by the joists, but 

the EVSF does not provide a 

commensurate increase in strength for 

the low-strength joists most likely to 

control the floor strength. Stiffness 

attracts stress, and this results in lower 

floor strength when these stiffer joists 

do not have at least a corresponding 

increase in strength. 

15
Figure 11.–Cumulative floor strength, southern pine 

sample data (upper) and Douglas-fir sample data (lower). 

(ML83 5177) 

Empirical variable 

seasoning factor 

Green

ASTM adjustment CSU 103 222 .26 

Dry,

ASTM adjustment APA 108 234 .26 

1 Floor strength values (at first joist failure) computed using nominal joist depth of 7.25 in. 

The high-strength floors contained no 

low-MOR joists. The controlling joists 

in these floors were in the mid- to highMOR range and were assigned 

significant seasoning increases by the 

EVSF curve, which more than 

compensated for the additional stress 

attracted by these joists by the 

increased joist El. Consequently, these 

strong floors became even stronger. 

This is why the EVSF curve (fig. 11, 

lower) crosses the green curve near 

the top of the distributions. 

These analyses have been limited to 

the case in which only the joists 

change in moisture content. For an 

entire system that undergoes a change 

in moisture content, changes in both 

connector and sheathing stiffness must 

be considered. 

Failure loads for the complete data 

sets for southern pine as-graded and 

on-grade, and for Douglas-fir as-graded

and on-grade (fig. 12) were computed 

16

Floor Failure Loads 

with no moisture adjustment factors 

and using the APA-provided plywood 

properties. The strengths computed 

using the linear model were all reduced 

by 10 percent to compensate 

approximately for the differences 

between linear and nonlinear analyses 

shown in figure 10. Strength statistics 

are summarized in table 9. 

The failure loads at the lower 5 

percent level (shown on each figure) 

and ratios of these loads to the 

50 lb/ft2 (40 lb/ft2 live load plus 10 lb/ft2

dead load) design load are shown in 

table 10. The raitos of load/50 are not 

directly comparable to factors of safety 

associated with publushed values of 

allowable stresses as these MOR data 

are unadjusted for either MC or 

duration of loading. Also, thses are 

measures of loads at first joist failure 

and likely underestimate the actual 

collapse load. 

Dry,

- - - - - - - - -Lb/ft2 - - - - - - - - - 

CSU 72 

CSU 84 

170 0.34 

18.2 .26 

Table 8.–Floor stength, Douglas-fir smaple data1

Moisture condition 

or adjustment type 

Plywood

values strength 

Lowest floor Mean floor 

strength COV
Figure 12.–Cumulative floor strength, southern pine as-graded, 107 lots (upper left) and on-grade, 

137 lots (upper right), Douglas-fir as-graded, 138 lots (lower left) and on-grade, 177 lots 

(lower right). (ML83 5179) 

Table 9.–Summary of floor strength capacity statistics, complete data 

Number Floor strength statistics1

Mean COV of Joist type in floor Range 

floors Low High 

107

137

138

108

30

138

39

177

Southern pine, as-graded

Southern pine, on-grade

Douglas-fir, as-graded

Douglas-fir, on-grade

green

dry

green

dry

- - - - - - - - -Lb/ft2 - - - - - - - - - 

64 338 168 0.32

64 277 153 .29

71 277 158 .25

71 277 161 .23

72 249 148 .32

62 249 143 .26

62 249 147 .25

67 228 127 .29

1 Floor strength values (at first joist failure) computed using nominal joist depth of 7.25 in., 

and linear FEAFLO with a reduction factor of 0.90. 

17
Correlation of Joist

Characteristics with Floor

Strength

Possible correlation between joist

properties MOE, MOR, and joist

deflection capacity index (DCI) and the

floor strength were studied using the

sample data. MOE, MOR, and DCI

were each plotted against floor

strength for floors with green Douglasfir joists (sample data) and CSU

plywood. The DCI is defined as the

ratio of joist MOR to MOE and is a

combined measure of both strength

and stiffness. This index assumes that

the joists deflect linearly to failure.

In a plot of MOE of the controlling

joist versus floor strength, the

correlation coefficient (r) is 0.27 and the

standard error of the estimate (SEE) is

45.5 lb/ft2

 (fig. 13, upper). Clearly,

attempting to compute floor strength

based on MOE is unsatisfactory.

The DCI was developed in an

attempt to relate floor strength to both

strength and stiffness of the joists (fig.

13, center; r = 0.87, SEE = 23.1 Ib/ft2

),

but it does not provide any

improvement over considering MOR

(fig. 13, lower; r = 0.92, SEE = 18.1

Ib/ft2

) alone and hence may be

discarded. Thus, accurate knowledge

of joist MOR leads to acceptable

definition of floor strength as predicted

by the linear model.

Table 10.—Calculated near-minimum floor

failure loads, complete data sets

Failure

Species Grade load, lower Load/50

5 percent

Lb/ft2

Southern As-graded 102

pine On-grade 93

Douglas-fir As-graded 99

On-grade 83

2.04

1.86

1.98

1.66

Figure 13.—Controlling joist MOE (upper), controlling joist DCI (center), and controlling

joist MOR (lower) versus floor strength, Douglas-fir sample data. (ML83 5180)

18
Summary

Data from in-grade lumber tests were

used to predict the stiffness and

strength of one configuration of

uniformly loaded floors. Over 500

floors, each consisting of ten 2 X 8’s,

were analyzed using an expanded

version of FEAFLO. Distributions of

performance were generated and

compared to current design

requirements.

In addition to average floor

deflection, two new (other) measures of

floor stiffness are examined. Estimates

of floor strength distributions,

generated by a linear analysis and

modified by a nonlinearity scaling

factor, are also presented.

Stiffness

The deflection data shown as

cumulative distributions (CD’s) for the

sample data and for the complete data

set identify the following trends:

1. System filtering effect. The

interactions among the joists,

sheathing, and connectors in a floor

act to filter out part of the variability of

the input data.

2. Stiffening of sheathing layer. Two

different estimates of plywood

properties were used. The APA

plywood data provided an EA value 80

percent greater than did the CSU data.

This stiffened the sheathing layer and

provided a more effective T-beam

flange, but this 80 percent increase

reduced mean deflections by only 8

percent.

3. Variability of sheathing properties.

Analyses made assuming that

sheathing MOE values vary following a

normal distribution with a coefficient of

variation of 26 percent showed that

this variability has no significant effect

on the deflection performance of

uniformly loaded floors. However, the

effect of sheathing variability may be

important for floors subjected to

concentrated loads.

4. Moisture adjustments. As MC

decreases, MOE increases but the

moment of inertia decreases. The

product, El, is inversely related to

deflections. Even an extreme increase

in El of 22 percent for the Douglas-fir

floor resulted in only a 12 percent

decrease in deflections compared to

the green condition. This suggests that

uncertainties in the moisture

adjustment factors will be reduced by

about one-half in determining

uncertainties in deflections of the

uniformly loaded floors studied.

5. Calibration data. Data are

presented for one floor configuration. A

major use of the computed

performance data will be in the

calibration of probability-based design

procedures for wood joist floors. The

plots for the complete data set can be

used for this purpose after decisions

have been made concerning which

measures of acceptable deflection

performance are best suited for

calibration purposes.

Strength

A number of trends relative to

strength computations were discerned.

1. Single analysis estimations. The

linear FEAFLO program using a scaling

factor provides a way to estimate both

deflection performance and strength

data for a floor using a single analysis.

The strength data will be

nonconservative but refinement of a

correction factor accounts for

nonlinearities should provide adequate

estimates. In this study the correction

factor of 0.90 provided a useful way to

examine effects of various seasoning

adjustment factors on strength.

2. Effect of plywood stiffness.

Increasing plywood stiffness causes the

plywood to carry more of the total

strain energy in the system, thus

relieving the joists of load and

producing a slightly stronger floor.

However, increasing floor capacity by

increasing sheathing stiffness does not

appear to be an efficient approach.

3. ASTM seasoning. Increasing both

joist MOE and MOR using the ASTM

seasoning adjustment while decreasing

dimensions results in a net increase in

strength. The relatively stiffer joists

attract more stress but the increase in

strength more than compensates for

this increase.

4. EVSF seasoning. The EVSF

adjustment procedure leaves MOR

values near the bottom of the

distribution unchanged. These joists,

which usually control the floor failure

load, undergo a stiffness increase

because of seasoning and hence

attract more stress. Because the joist

strength is unchanged, use of the

EVSF results in a net decrease in

strength except for floors with all joists

from the average or high-strength

ranges of the distribution.

5. Joist parameters as failure load

predictors. MOE, MOR, and DCI were

studied to determine their utility as

predictors of floor strength. Both MOR

and DCI showed good correlation with

strength but they cannot be defined

except through destructive testing.

While MOE can be obtained by

nondestructive means, it correlates

poorly with predicted failure loads.

Hence it is not feasible to predict the

true capacity of a given floor using

MOE information alone.

Conclusion

A computer model, FEAFLO, can be

used to estimate the stiffness and

strength distributions of wood joist

floor systems. By using lumber

samples collected at sawmills,

estimates of the distributions of actual

in-place performance of such floors

were made. Data on the stiffness and

strength performance of one floor

configuration will be helpful in

establishing acceptance criteria for

probabilistic based design of wood joist

floor systems.

19
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20

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factor design for steel. ASCE Journal of the Structural

Division 104(ST9, Sept.): 1337-1353; 1978.

20. Sazinski, R. J.; Vanderbilt, M. D. Behavior and design

of wood joist floors. Wood Science 11(4):209-220;

1979.

21. Thompson, E. G.; Goodman, J. R.; Vanderbilt, M. D.

Finite element analysis of layered wood systems. ASCE

Journal of the Structural Division 101 (ST12,

Dec.):2659-2672; 1975.

22. Thompson, E. G.; Vanderbilt, M. D.; Goodman, J. R.

FEAFLO: A program for the analysis of layered wood

systems. Computers and Structures VII:237-248; 1977.

23. Tremblay, G. A.; Goodman, J. R.; Criswell, M. E.

Nonlinear analysis of layered T-beams with interlayer

slip. Wood Science 9(1):21-30; 1976.

24. U.S. Department of Agriculture, Forest Service,

Forest Products Laboratory. Wood Handbook: Wood

as an Engineering Material. Agric. Handb. 72 rev.;

Washington, DC: U.S. Department of Agriculture; 1974.

25. Vanderbilt, M. D.; Goodman, J. R.; Criswell, M. E.

Service and overload behavior of wood joist floor

systems. ASCE Journal of the Structural Division

100(ST1, Jan.):11-20; 1974.

26. Wheat, D. L.; Vanderbilt, M. D.; Goodman, J. R. Wood

floors with nonlinear nail stiffness. Journal of Structural

Engineering 109(5, May): 1290-1302.

27. Wood, L. W.; Soltis, L. A. Stiffness and shrinkage of

green and dry joists. Res. Pap. FPL 15. Madison, WI:

U.S. Department of Agriculture, Forest Service, Forest

Products Laboratory; 1964.

U.S. GOVERNMENT PRINTING OFFICE: 1 9 8 3 7 5 4 0 4 0 4 0 2 6
Forest Products Laboratory

Stiffness and Strength of Uniformly Loaded Floors with In-grade Lumber, by

M. D. Vanderbilt, M. E. Criswell, J. Bodig, R. C. Moody, and D. S. Gromala,

Madison, Wis., FPL, 1983.

20 pp. (USDA For. Serv. Res. Pap. FPL 440).

To ensure that new design techniques and construction materials do not change

the overall acceptability of wood floor systems, it is necessary to characterize

existing systems with a history of satisfactory performance. A structural analysis

model and computer program for floors, FEAFLO, was used with data on lumber

samples from sawmill inventory. Over 500 floor analyses yielded estimates of floor

stiffness and strength distributions that will be useful to designers or code

agencies.

Keywords: floors, strength, stiffness, joists, analysis, calibration, light-frame

construction, structural.

2.5-11/83