Nondestructive 

Evaluation of Wood

Second Edition

General Technical Report

FPL–GTR–238

United States Department of Agriculture

Forest 

Service

Forest Products

Laboratory

July

2015
July 2015

Ross, Robert J. (Ed.). 2015. Nondestructive evaluation of wood: second 

edition. General Technical Report FPL-GTR-238. Madison, WI: U.S. 

Department of Agriculture, Forest Service, Forest Products Laboratory. 

169 p.

A limited number of free copies of this publication are available to the 

public from the Forest Products Laboratory, One Gifford Pinchot Drive, 

Madison, WI 53726–2398. This publication is also available online at 

www.fpl.fs.fed.us. Laboratory publications are sent to hundreds of libraries 

in the United States and elsewhere.

The Forest Products Laboratory is maintained in cooperation with the 

University of Wisconsin. 

The use of trade or firm names in this publication is for reader information 

and does not imply endorsement by the United States Department of 

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Abstract

This report summarizes information on nondestructive testing and evaluation of wood. It includes information on a 

wide range of nondestructive assessment technologies and 

their uses for evaluating various wood products.

Keywords: Wood, timber, nondestructive testing, 

nondestructive evaluation

Conversion Table

English unit Conversion factor SI unit

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

board foot 0.0023597 cubic meter (m3)) 

(nominal)

feet per minute 

(ft/min)

0.005080 meters per second

(m/s)

pound (lb) 0.448 newton (N)

pounds per square 

inch (lb/in2)

0.0069 pascal (Pa)

in4 4.162 × 10–7 m4
Nondestructive Evaluation

of Wood

Second Edition

Edited by

Robert J. Ross

Forest Products

Laboratory
Contents

Preface ..................................................................................................................................................... iii

Preface to the First Edition.......................................................................................................................iv

Chapter 1—Nondestructive Testing and Evaluation of Wood...................................................................1

Chapter 2— Static Bending, Transverse Vibration, and Longitudinal Stress Wave 

Nondestructive Evaluation Methods..........................................................................................................5

Chapter 3—Ultrasonic-Based Nondestructive Evaluation Methods for Wood .......................................21

Chapter 4— Proof Loading......................................................................................................................53

Chapter 5—Sounding, Probing, Moisture Content, and Resistance Drilling Techniques.......................59

Chapter 6—A Review of the Piezoelectric Effect in Wood.....................................................................67

Chapter 7—Nondestructive Testing in the Urban Forest.........................................................................77

Chapter 8— Acoustic Assessment of Wood Quality in Trees and Logs..................................................87

Chapter 9— Laser Scanning of Logs and Lumber ................................................................................103

Chapter 10—Ultrasonic Veneer Grading ...............................................................................................109

Chapter 11—Machine Grading of Lumber............................................................................................115

Chapter 12— Inspection of Timber Structures Using Stress Wave Timing Nondestructive 

Evaluation Tools ....................................................................................................................................141

Chapter 13— Use of Laser Scanning Technology to Obtain As-Built Records of Historic 

Covered Bridges.....................................................................................................................................149

Appendix—Proceedings of the International Nondestructive Testing and Evaluation of Wood

Symposium Series..................................................................................................................................167

ii
iii

This edition of Nondestructive Evaluation of Wood builds upon the first edition with important 

new content:

•	 Ultrasonic nondestructive testing methods (Chapter 3)

•	 Piezoelectric effect in wood (Chapter 6)

•	 Urban tree assessment (Chapter 7), reflecting significant research and technology transfer 

advances

•	 Laser scanning of logs and lumber (Chapter 9), reflecting the importance of scanning systems in the 

wood manufacturing industry

•	 Updates on mechanical grading methods for structural lumber (Chapter 11)

•	 Use of laser scanning technologies for evaluation of historic, covered timber bridges (Chapter 13)

•	 An appendix providing information to access digital copies of all proceedings from the International 

Nondestructive Testing and Evaluation of Wood Symposium Series

The objective of this book is the same as that of the first edition—to serve as a primary reference on nondestructive evaluation of wood. Sections of the book originally appeared in technical journals, research 

reports, and proceedings from various symposia. For example, the chapters on ultrasonic nondestructive 

testing methods, the piezoelectric effect in wood, and the use of laser scanning technologies for evaluation of bridges were originally published as USDA Forest Products Laboratory (FPL) reports.

I worked with several well-respected technical authorities in preparing this edition. I thank the 

technical contributors to this edition:

•	 R. Bruce Allison, PhD, Adjunct Professor, University of Wisconsin–Madison; and Allison Tree, LLC, 

Verona, Wisconsin, USA

•	 Brian K. Brashaw, PhD, Program Manager, Forest Products Marketing Unit, USDA Forest Service, 

Madison, Wisconsin, USA

•	 Peter Carter, CEO, Fibre-gen, New Zealand

•	 William Galligan, Consultant, USA

•	 Gregory Schueneman, PhD, Project Leader, USDA Forest Products Laboratory, USA

•	 C. Adam Senalik, PhD, P.E., Research General Engineer, USDA Forest Products Laboratory, USA

•	 Edward Thomas, Research Computer Scientist, USDA Forest Products Laboratory, USA

•	 Xiping Wang, PhD, Research Forest Products Technologist, USDA Forest Products Laboratory, USA

I also acknowledge Professor (retired) Roy F. Pellerin, Washington State University, for his valuable 

contributions to the first edition of Nondestructive Evaluation of Wood. 

Financial support from the USDA Forest Service for this second edition of Nondestructive Evaluation of 

Wood is gratefully acknowledged.

This edition of Nondestructive Evaluation of Wood is dedicated to Roy F. Pellerin and John R. Erickson, 

my good friends and colleagues.

Roy F. Pellerin is a pioneer in the field of nondestructive testing and evaluation, leading the successful 

nondestructive evaluation research program at Washington State University for nearly 40 years. His 

technical accomplishments are numerous, and his leadership of the International Nondestructive Testing and Evaluation of Wood Symposium Series has resulted in the worldwide development and use of 

nondestructive evaluation. He is an inspiration and source of knowledge to a generation of graduate 

students, scientists, and engineers working in the field.

Preface
Preface to the First Edition

This book on nondestructive evaluation (NDE) of wood was prepared at the request of our colleagues 

throughout the world who expressed the desire for a synthesized source of information on the subject. 

We have attempted to meet their needs through this book.

We contacted several widely respected technical authorities and asked them to prepare chapters dealing with their areas of expertise. Professor Frank Beall (University of California–Berkeley), Brian 

Brashaw (University of Minnesota–Duluth), Kevin Cheung (Western Wood Products Association), 

Bill Galligan (retired, Frank Lumber Company), John Kearns (Weyerhaeuser Company), and Xiping 

Wang (University of Minnesota–Duluth) responded positively to our request and prepared excellent 

chapters on acoustic emission/acousto–ultrasonics, ultrasonic veneer grading, visual grading of lumber, mechanical grading of lumber, and recent research on NDE of green materials, respectively.

Sections of the book originally appeared in technical journals, research reports, and various symposia 

proceedings. We have included, for example, a chapter on inspection of timber structures that originally appeared as a research paper, which was widely distributed and used by engineering consultants 

who inspect structures as a part of their business.

We prepared this book to serve as a primary reference on NDE of wood. More than 400 technical 

articles are cited in the chapters or listed in the Appendix. We also intended it to be used as a primer 

or handbook on several widely used NDE technologies. The chapters on machine grading of lumber, 

ultrasonic veneer grading, and inspection of wood in service were written to provide a concise source 

of information to manufacturers and users of wood products who are considering use of various commercially available NDE technologies in their manufacturing operation or consulting business.

This book was made possible through the efforts of dedicated scientists who spent countless hours in 

laboratories developing our technical knowledge base on NDE of wood.

Roy F. Pellerin

Robert J. Ross

iv

John R. Erickson (1934–2014), P.E., Director and Research Engineer, FPL, was an internationally 

recognized leader in the wood science profession. He conducted research on wood harvesting and 

utilization issues with wood and guided the FPL through numerous changes. He initiated new 

research initiatives at FPL in recycling, green technologies, biotechnology, and ecosystem management. Under his leadership, FPL provided critical technical support to the Forest Service, wood products industry, and various government agencies, including the Department of Defense, Department 

of Energy, National Park Service, and Department of Housing and Urban Development. John was a 

strong supporter and advocate for the nondestructive evaluation field; after retiring from the Forest 

Service he joined the staff at Michigan Technological University, where he worked on the development of advanced tree and log nondestructive assessment and grading technologies that have been 

widely adapted for use. John was a well-respected scientist, worldwide, and he served the public in a 

professional manner for over 40 years. His reputation is impeccable.

Nondestructive Evaluation of Wood, Second Edition is available in digital format from the USDA Forest 

Service Forest Products Laboratory website. 

Special thanks to Jim Anderson, Madelon Wise, Tivoli Gough, and Karen Nelson for their extraordinary 

efforts in the production of this book.

Robert J. Ross, Editor
1

Chapter 1

Nondestructive Testing and Evaluation 

of Wood

Robert J. Ross, Supervisory Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

Wood is an extremely valuable material because it is renewable, widely distributed, and requires relatively small 

amounts of energy to be converted into useful products. As 

a consequence, wood has been used for centuries for both 

shelter and as an industrial raw material. 

It is important to note that wood is a material of biological 

origin. Consequently, its molecular composition and basic 

physiological structure are fixed by nature. The fact that it 

is a biological material provides the basis for many of its 

unique properties and the challenges manufacturers and subsequent users of wood products are confronted with. 

The following list of wood characteristics, originally prepared by Marra (1979), summarizes some characteristics of 

wood that are important to introducing the subject of nondestructive evaluation of wood.

1. Wood is a fibrous material. The fiber is a small tubular 

structure, incorporating nature’s engineering to make it 

an efficient load-carrying member. The microstructure 

of the walls of a fiber present an interesting arrangement 

related to biological functions performed in the living 

tree and a contribution to the properties of wood as a 

material. Alignment of fibers primarily in longitudinal 

aspect in the tree is the main source of wood’s 

anisotropic properties. 

2. Wood is a complex chemical material. Made up largely 

of several chemical identities, such as cellulose and 

lignin, it can be infiltrated with numerous extraneous 

compounds. The type and amount of these extraneous 

compounds are often distinctive with certain species of 

trees, and in some cases, have a pronounced effect of 

properties.

3. Wood is anisotropic. Perhaps the most striking quality 

of wood from an engineering standpoint is its properties 

along the grain compared to properties across the grain. 

This ratio may reach as high as 50 for some properties.

4. Wood is viscoelastic. Although wood follows Hooke’s 

law at low stresses, the proportional limit is difficult to 

precisely define. Under high stress, wood exhibits creep 

behavior, which is greatly influenced by the presence of 

moisture and is temperature dependent.

5. Wood is hygroscopic. Wood is characterized chemically 

by an abundance of hydroxyl groups. These groups have 

an affinity for water molecules and seek to maintain 

equilibrium with the partial vapor pressure of moisture 

in the surrounding environment. The immediate 

consequence of the gain or loss of moisture is a swelling 

or shrinkage of wood. Moisture also has a profound 

effect on properties.

6. Wood is variable. Wood varies in properties between 

species, between trees of the same species, and between 

pieces from the same tree.

7. Wood in its original solid form has adequate properties 

for many uses. 

8. Wood is plentiful. Each major land mass in the world is 

blessed with extensive forest lands.

9. Wood is renewable. This factor is extremely important 

when considering the roles of various non-renewable 

materials in sustaining a technologically based society.

10. Wood is beautiful. The beauty of wood, aside from the 

warmth for which it is universally cherished, arises 

principally from variability in its fibrous structure. 

Fundamental Concept 

By definition, nondestructive evaluation is the science of 

identifying the physical and mechanical properties of a piece 

of material without altering its end-use capabilities and then 

using this information to make decisions regarding appropriate applications. Such evaluations rely upon nondestructive 

testing technologies to provide accurate information pertaining to the properties, performance, or condition of the material in question.

Many tests or techniques can be categorized as nondestructive (Fig. 1.1). A variety of tests can be performed on a material or product, with selection of the appropriate test dictated by the particular performance or quality characteristic 

of interest. Evaluation of visual characteristics of a piece of 

lumber is probably one of the most widely used nondestructive techniques in the forest products industry. Characteristics such as the size, number, and location of knots are 

common visual characteristics considered when grading 

both structural and nonstructural lumber.
General Technical Report FPL–GTR–238

2

One of the first uses of a nonvisual nondestructive testing 

method involved a procedure to evaluate timbers for boats 

used by the Hudson Bay Company, Canada (Fig. 1.2): 

A York boat, which took two skilled men only two 

weeks to build, would last three seasons with minimum 

maintenance. The trickiest part of the construction process was to find the proper piece of spruce or tamarack 

for a seaworthy keel. Samples were tested by being 

placed on stocks and a pocket watch held against the 

butt at one end. The builder listened for the tick at the 

other. Only if the ticking resonated loudly and clearly 

through the wood was it judged suitable to withstand 

the stresses of being carved into a keel. From Caesars 

of the Wilderness: Company of Adventures, Volume II,

by Peter C. Newman.

The field of nondestructive testing (NDT) and nondestructive evaluation (NDE) of materials is constantly evolving. 

This is especially true in the area of wood and fiber-based 

materials. For example, early research on NDT/NDE technologies for wood products focused on methods for assessing the performance characteristics of structural lumber in 

North America. Nondestructive testing techniques, equipment, and evaluation procedures that resulted from those 

efforts are now in widespread use. Currently, worldwide 

research and development efforts are under way to examine 

potential use of a wide range of NDT technologies for evaluating wood and woodbased materials—from assessment of 

standing trees to in-place structures.

The original impetus for research in NDT/NDE of wood 

was the need to provide methodologies for assessing woodbased materials and products so that more accurate decisions could be made about proper use. This remains the 

major driving force for NDT/NDE wood research, with two 

significant additional challenges. First, there is an increased 

emphasis around the world to address forest and ecosystem 

health issues. Utilization of woody biomass from widely 

varying growing conditions will play a key role in providing 

economical options for managing the health of these forests 

and ecosystems. Second, the marketplace has become increasingly global in nature. Shipments of raw materials and 

products between countries on different continents is now 

commonplace. Both of these challenges will require accurate, cost-effective NDT/NDE technologies.

An international forest products research community is responding to these driving forces by conducting NDT/NDE 

research to provide the technologies needed to address these 

challenges. 

International Nondestructive

Testing and Evaluation of Wood 

Symposium Series

In an effort to provide a forum for researchers, the international NDT/NDE research community sponsors a series of 

technical symposia for exchange of technical information. 

These symposia are scheduled on a biannual basis at locations throughout the world.

The International Nondestructive Testing and Evaluation 

of Wood Symposium Series was initiated by Washington 

State University and the USDA Forest Products Laboratory 

(FPL). The first symposium was held at FPL in the fall of 

1963, with proceedings produced and distributed in 1964. 

At the meeting, nearly 100 scientists, engineers, and industry leaders discussed possibilities of a wide range of scientific means for testing wood nondestructively.

This symposium series has two goals:

1. To provide a technical and scientific forum for 

researchers to present and exchange results from their 

latest research endeavors.

Figure 1.1—Nondestructive techniques.

Figure 1.2—A late 18th-century Hudson Bay Compay 

York boat.

Chapter 1

Nondestructive evaluation of wood

Evaluation of

visual characteristics

Color

Presence of defects

Chemical

tests

Composition

Presence of treatments

▪ Preservatives

▪ Fire retardants

Mechanical

tests

Flexural stiffness

Proof loading

▪ Bending

▪ Tension

▪ Compression

Probes/coring

Physical

tests

Electrical properties

Vibrational properties

Wave propagation

Acoustic emissions
Nondestructive Evaluation of Wood: Second Edition

3

2. To bring researchers and industry together in an attempt 

to bridge the gap between the results of research efforts 

and the use of those results by the wood industry.

In 2012, an FPL report was prepared that contained digital 

copies of earlier proceedings (Fig. 1.3), and proceedings of 

the 2013 symposium were distributed at the meeting (Ross 

Nondestructive Testing 

and Evaluation of 

Wood—50 Years 

of Research 

International Nondestructive 

Testing and Evaluation of Wood 

Symposium Series

Robert J. Ross

United States

Department of

Agriculture

Forest Service

Forest

Products

Laboratory

General 

Technical

Report

FPL–GTR–213

Figure 1.3—Nondestructive Testing and 

Evaluation of Wood—50 Years of Research.

and Wang 2012, 2013; see Appendix). Eighteen symposia 

have been held to date, and the nineteenth is scheduled 

for September 22–25, 2015, in Rio de Janeiro, Brazil 

(Table 1.1).

Although the early symposia focused on basic NDE principles and lumber assessment procedures, the symposium 

now attracts researchers and industry representatives from 

throughout the world and represents the full spectrum of 

technical interests, from basic and applied science to the 

use of various methods in industrial and field applications. 

Recently, papers have been presented on the latest NDT 

techniques including those that focus on tomography, near 

infrared scanning, and innovative combinations of stress 

wave, laser, and ultrasound techniques. A significant number 

of papers have been presented on assessment of trees and 

logs, with most of the work on lumber focusing on the use 

of established NDT techniques for evaluating species in 

Asia, Europe, and South America.

Objectives

The objectives of this book are (1) to serve as a primary 

reference on the subject of nondestructive testing and 

evaluation of wood and (2) to present a synthesized source 

of information on the subject. 

In an attempt to meet these objectives, we have prepared 

chapters on various nondestructive testing techniques and 

basic concepts, including ultrasound, stress wave, transverse 

vibration, and proof-loading methods. Application of NDE, 

Nondestructive Testing and Evaluation of Wood

Table 1.1—Dates and locations for the International Nondestructive Testing and 

Evaluation of Wood Symposia 

Symposium Dates held Host institution and location 

1 October 7–9, 1963 U.S. Forest Products Laboratory, Madison, WI, USA 

2 April, 1964 Washington State University, Spokane, WA, USA 

3 April–May, 1970 Washington State University, Vancouver, WA, USA 

4 August 28–30, 1978 Washington State University, Vancouver, WA, USA 

5 September 9–11, 1985 Washington State University, Pullman, WA, USA 

6 September 14–16, 1987 Washington State University, Pullman, WA, USA 

7 September 27–29, 1989 U.S. Forest Products Laboratory, Madison, WI, USA 

8 September 23–25, 1991 Washington State University, Vancouver, WA, USA 

9 September 22–24, 1993 U.S. Forest Products Laboratory, Madison, WI, USA 

10 August 26–28, 1996 IBOIS-Chair of Timber Construction of the Swiss Federal 

Institute of Technology, Lausanne, Switzerland 

11 September 9–11, 1998 U.S. Forest Products Laboratory, Madison, WI, USA 

12 September 13–15, 2000 University of West Hungary, Sopron, Hungary 

13 August 19–21, 2002 University of California, Berkeley Campus, CA, USA 

14 May 2–4, 2005 University of Applied Sciences Eberswalde, Hanover, 

Germany 

15 September 10–12, 2007 University of Minnesota Duluth, Duluth, MN, USA 

16 May 11–13, 2009 Beijing Forestry University, Beijing, P.R. China 

17 September 14-16, 2011 University of West Hungary, Sopron, Hungary 

18 September 24-27, 2013 U.S. Forest Products Laboratory, Madison, WI, USA 
General Technical Report FPL–GTR–238

4

including applications that highlight the use of various NDE 

methodologies to assess the entire spectrum of wood, from 

standing trees to historic structures, are included in subsequent chapters.

Literature Cited

Marra, G.G. 1979. Overview of wood as a material. In: F.F. 

Wangaard, Ed. Wood: its structure and properties. 42 p.

Ross, R.J.; Wang, Xiping. 2012. Nondestructive testing and 

evaluation of wood research. 50 years of research. International nondestructive testing and evaluation of wood symposium series. General Technical Report. FPL-GTR-213. 

Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 6,702 p. 

Ross, R.J.; Wang, Xiping. 2013. Proceedings, 18th international nondestructive testing and evaluation of wood symposium. General Technical Report. FPL-GTR-226. Madison, 

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

Products Laboratory. 808 p. 

Chapter 1
5

Chapter 2

Static Bending, Transverse Vibration, and 

Longitudinal Stress Wave Nondestructive 

Evaluation Methods

Robert J. Ross, Supervisory Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

Static Bending Nondestructive 

Evaluation Methods

Measuring the modulus of elasticity (MOE) of a member by 

static bending methods is a relatively simple procedure that 

involves using the load–deflection relationship of a simply 

supported beam. Modulus of elasticity can be computed 

directly by using equations derived from fundamental mechanics of materials.

Initial laboratory studies to verify the relationships between 

static bending methods and structural performance characteristics were conducted with lumber products. Considerable research in the early 1960s examined the relationships 

between static bending test methods and the strength of 

softwood dimension lumber. Summaries of various projects 

designed to examine this relationship are presented in 

Tables 2.1 to 2.3. A wide range of lumber products was 

evaluated, and the bending, compressive, and tensile 

strength of the materials was investigated. In all cases, 

useful correlative relationships were discovered.

Some commercially available equipment uses static bending techniques to evaluate the MOE of structural lumber. A 

detailed discussion of equipment is included in Chapter 11, 

Machine Grading of Lumber.

The following text outlines essential laboratory testing procedures and additional key information to be used in a laboratory or field environment.

Centerpoint Loading Test Setup

Figure 2.1 illustrates a typical centerpoint loading test setup. 

The specimen is simply supported at both ends, a load 

applied, and the midspan deflection that results from the 

load measured. MOE of the specimen is calculated as

Table 2.1—Research on the correlation between modulus of elasticity (tested flatwise) and flatwise bending 

strength of softwood dimension lumber

Reference Species

Nominal 

moisture 

content (%) Gradea

Nominal 

width 

(in.)b Growth location

Correlation 

coefficient r

Hoyle (1961) Douglas-fir 12 SS, C, U 4, 6, 10 Western Oregon, Washington, Idaho 0.79 

 Washington 0.72 

Western hemlock 12 SS, C, U 4, 6, 10 Western Oregon, Washington 0.74 

Western larch 12 SS, C, U 4, 6, 8 Idaho, Washington 0.70 

Hoyle (1962) Grand fir 12 C, S, U 8 Idaho 0.75

Hofstrand 

and Howe 

(1963)

Grand fir 12 C, S 4, 6, 8 Idaho 0.75

Pellerin (1963b) Douglas-fir 12 Combination of 

visual grades 

4, 8 Idaho 0.76 

Hoyle (1964) Southern Pine 12 1D, 1, 2D, 2, 3 4, 6, 8 Southeastern United States 0.76

Kramer (1964) Southern Pine 12 1D, 2, 3 4 , 6, 10 Southeastern United States 0.88

Johnson (1965) Douglas-fir 10 SS, C, U 6 Western Oregon, Washington 0.85

Western hemlock 10 SS, C, U 6 Western Oregon, Washington 0.86 

a

Grades are by regional rules in use at time of research. Western Products Association and West Coast Lumber Inspection Bureau Grades: SS, Select 

Structural; C, Construction; S, Standard; U, Utility. Western Wood Products Association Grades: 1, 2, 3. Southern Pine Inspection Bureau Grades: 1D, 

No. 1 Dense; 1, No. 1; 2D, No. 2 Dense; 2, No. 2; 3, No. 3.

b

1 in. = 25.4 mm.
General Technical Report FPL–GTR–238

6 Chapter 2

Table 2.2—Research on the correlation between modulus of elasticity (tested flatwise and on edge) and edgewise 

bending strength of softwood dimension lumber

Reference Species

Nominal 

moisture 

content (%) Gradea

Nominal 

width 

(in.)b Growth location

Correlation 

coefficient

Hoerber (1962) Douglas-fir 12 SS, C, U 4, 6, 8 Idaho, eastern Washington 0.651 

Hoyle (1962) Grand fir 12 C, S, U, SS 8 Idaho 0.59–0.701

Hoyle (1964) Southern Pine 12 1D, 1, 2D, 2, 3 4, 6, 8 Southeastern United States 0.571 

Sunley and Hudson 

(1964) 

Norway spruce and 

Scots pine (pooled) 

— — 4, 7 Great Britain 0.681 

Corder (1965) Douglas-fir 12 SS, C, S 4, 6, 10 Inland northwestern United States 0.64 

Johnson (1965) Douglas-fir 10 SS, C, U 6 Western Oregon, Washington 0.80–0.87 

Johnson (1965) Western hemlock 10 SS, C, U 6 Western Oregon, Washington 0.86 

Littleford (1965) Douglas-fir 

Western hemlock 

Noble fir

Western white 

spruce 

Lodgepole pine

10 

10 

12 

12 

17

— 

— 

— 

— 

— 

— 

6 

6 

6 

6 

6 

6 

6

British Columbia, Canada 0.74 

0.70–0.77

0.66 

0.79 

0.80 

Miller (1965) White spruce 

Jack pine

12 

12

— 

—

6 

6

Eastern Canada 0.78–0.84

0.69–0.73

Doyle and 

Markwardt (1966)

Southern Pine 12 1D, 1, 2D, 2, 3 4, 6, 8, 10 Southeastern United States 0.66

Hoyle (1968) Southern Pine 12 1D, 1, 2D, 2, 3 4, 6, 8 Southeastern United States 0.671

a

Grades are by regional rules in use at time of research. Western Products Association and West Coast Lumber Inspection Bureau Grades: SS, Select 

Structural; C, Construction; S, Standard; U, Utility. Western Wood Products Association Grades: 1, 2, 3. Southern Pine Inspection Bureau Grades: lD, 

No. 1 Dense; 1, No. 1; 2D, No. 2 Dense; 2, No. 2; 3, No. 3. 

b

1 in. = 25.4 mm.

Table 2.3—Research on the correlation between modulus of elasticity (tested flatwise) and the compressive 

and tensile strength of softwood dimension lumber 

Strength 

property Reference Species

Nominal 

moisture 

content (%) Gradeb

Nominal 

width 

(in.)b Growth location

Compression 

coefficient r 

Compressive Hofstrand and 

Howe (1963)

Grand fir 12 Ungraded 4, 8 Idaho 0.84 

Pellerin (1963a) Douglas-fir 12 SS, S, E 4, 8 Idaho 0.78 

Hoyle (1968) Southern Pine 12 1, 2, 3 4, 8 Southeastern 

United States 

0.67

Tensile Hoyle (1968) Douglas-fir 15 1.0, 1.4, 1.8, 2.2 4, 8 Idaho 0.74 

 White fir 14 Idaho 0.75 

 Western 

hemlock 

15 Western Oregon, 

Washington 

0.81 

a

Grades are by regional rules in use at time of research. Western Products Association and West Coast Lumber Inspection Bureau Grades: SS, Select 

Structural; S, Standard; E, Economy. Western Wood Products Association Grades: 1, 2, 3. Machine Stress Grades: 1.0, 1.4, 1.8, 2.2. b

1 in. = 25.4 mm.
Nondestructive Evaluation of Wood: Second Edition

7

(MOE) where for a beam freely supported at two nodal 

points 

 (2.4)

and for a beam simply supported at its ends

 

(2.5)

In Equations (2.4) and (2.5),

MOE is dynamic modulus of elasticity (lb/in2, Pa),

fr resonant frequency (Hz),

W beam weight (lb, kg∙g),

L beam span (in., m),

I beam moment of inertia (in4, m4), and

g acceleration due to gravity (386 in/s2, 9.8 m/s2).

Solving Equation (2.3) for D leads to an expression of the 

internal friction or damping component. The logarithmic 

decrement of vibrational decay δ is a measure of internal 

friction and can be expressed in the form (for free 

vibrations)

(2.6)

where A1 and An are the amplitudes of two oscillations n – 1 

cycles apart (Fig. 2.4).

For forced vibrations,

(2.7)

where Δf is difference in frequency of two points of amplitude A on each side of a resonance curve, fr

 is frequency at 

resonance, and Ar is amplitude at resonance (Fig. 2.4b).

Sharpness of resonance Q is frequently used to measure 

damping capacity; Q is defined as the ratio fr/Δf. Note that if 

the value 0.707Ar (half-power point method) is substituted 

for A in Equation (2.7), the equation reduces to

(2.8)

and

(2.9)

(2.1)

where P is applied load (lb), L is span (in.), I is moment of 

inertia (in4), and δ is midspan deflection (in.).

Alternative Bending Test Setup

Figure 2.2 shows an alternative bending test setup. The only 

difference between this setup and a centerpoint setup is that 

equal loads are applied at a known distance from each end. 

MOE of a specimen is calculated using

 

(2.2)

For both test setups, significant attention must be placed on 

design of the end supports. Ideally, the supports should be 

rigid so that no vertical displacement of the supports occurs. 

In addition, horizontal movement of the specimen on the 

end support should not be restricted, and the end supports 

need to be rotated and fixed to accommodate twist in the 

specimen.

Transverse Vibration Techniques

Background

Transverse vibration techniques have received considerable 

attention for nondestructive evaluation (NDE) applications. 

To illustrate these methods, an analogy can be drawn between the vibration of a mass that is attached to a weightless 

spring and internal damping force and the behavior of a vibrating beam (Fig. 2.3). In Figure 2.3, mass M is supported 

from a rigid body by a weightless spring whose stiffness is 

denoted by K. Internal friction or damping is represented 

by the dashpot D. A forcing function equaling P0 sin ωt or 

zero is applied for forced and free vibration, respectively. 

When M is set into vibration, its equation of motion can be 

expressed by the following:

 

(2.3)

Equation (2.3) can be solved for either K or D. A solution 

for K will lead to an expression for modulus of elasticity 

Figure 2.1—Centerpoint loading test setup. Figure 2.2—Alternative bending test setup.

Static Bending, Transverse Vibration, and Longitudinal Stress Wave Nondestructive Evaluation Methods

I

PL

48

MOE

3

 (2.1) 

 

I

Pa L a

48

3 4 MOE

2 2   (2.2) 

Kx P t

dt

dx D

dt

d x M 0 sin 2

2

   

 



 

















(2.3) 

Ig

f WL

12.65

MOE

2 3 r  (2.4)

Ig

f WL

2.46

MOE

2 3 r  (2.5) 

An

A

n

1 ln

1

1

  

(2.6) 

9  1

1

2 r r 

 

A A f

 f  (2.7) 

rf

f   

(2.8) 



 Q  (2.9) 

Ig

f WS

2.46

MOE

2 3

 (2.10) 

I

PL

48

MOE

3

 (2.1) 

 

I

Pa L a

48

3 4 MOE

2 2   (2.2) 

Kx P t

dt

dx D

dt

d x M 0 sin 2

2

   

 



 

















(2.3) 

Ig

f WL

12.65

MOE

2 3 r  (2.4)

Ig

f WL

2.46

MOE

2 3 r  (2.5) 

An

A

n

1 ln

1

1

  

(2.6) 

9  1

1

2 r r 

 

A A f

 f  (2.7) 

rf

f   

(2.8) 



 Q  (2.9) 

I

PL

48

MOE

3

 (2.1) 

 

I

Pa L a

48

3 4 MOE

2 2   (2.2) 

Kx P t

dt

dx D

dt

d x M 0 sin 2

2

   

 



 

















(2.3) 

Ig

f WL

12.65

MOE

2 3 r  (2.4)

Ig

f WL

246

MOE

2 3 r  (2.5) 

I

PL

48

MOE

3

 

 

I

Pa L a

48

3 4 MOE

2 2   

Kx P t

dt

dx D

dt

d x M 0 sin 2

2

   

 



 

















Ig

f WL

12.65

MOE

2 3 r  

Ig

f WL

2.46

MOE

2 3 r  

An

A

n

1 ln

1

1

  

9  1

1

2 r r 

 

A A f

 f  

rf

f   



 Q  

Ig

f WS

2.46

MOE

2 3

 

I

PL

48

MOE

3

 

 

I

Pa L a

48

3 4 MOE

2 2   

Kx P t

dt

dx D

dt

d x M 0 sin 2

2

   

 



 

















Ig

f WL

12.65

MOE

2 3 r  

Ig

f WL

2.46

MOE

2 3 r  

An

A

n

1 ln

1

1

  

9  1

1

2 r r 

 

A A f

 f  

rf

f   



 Q  

Ig

f WS

2.46

MOE

2 3

 

I

PL

48

MOE

3

 

 

I

Pa L a

48

3 4 MOE

2 2   

Kx P t

dt

dx D

dt

d x M 0 sin 2

2

   

 



 

















Ig

f WL

12.65

MOE

2 3 r  

Ig

f WL

2.46

MOE

2 3 r  

An

A

n

1 ln

1

1

  

9  1

1

2 r r 

 

A A f

 f  

rf

f   



 Q  

Ig

f WS

2.46

MOE

2 3

 

I

PL

48

MOE

3

 

 

I

Pa L a

48

3 4 MOE

2 2   

Kx P t

dt

dx D

dt

d x M 0 sin 2

2

   

 



 

















Ig

f WL

12.65

MOE

2 3 r  

Ig

f WL

2.46

MOE

2 3 r  

An

A

n

1 ln

1

1

  

9  1

1

2 r r 

 

A A f

 f  

rf

f   



 Q  

Ig

f WS

246

MOE

2 3

 

I

PL

48

MOE

3

 

 

I

Pa L a

48

3 4 MOE

2 2   

Kx P t

dt

dx D

dt

d x M 0 sin 2

2

   

 



 

















Ig

f WL

12.65

MOE

2 3 r  

Ig

f WL

2.46

MOE

2 3 r  

An

A

n

1 ln

1

1

  

9  1

1

2 r r 

 

A A f

 f  

rf

f   



 Q  

Ig

f WS

2.46

MOE

2 3

 

( ) 1

1

2 r r −

Δ =

A A f

π f δ
General Technical Report FPL–GTR–238

8

Research and Development

Table 2.4 summarizes research on the use of transverse 

vibration NDE techniques. Jayne (1959) designed and conducted one of the first studies that used these techniques for 

evaluating the strength of wood. He was successful in demonstrating a relationship between energy storage and dissipation properties measured by forced transverse vibration 

techniques and the static bending properties of small clear 

wood specimens. Using a laboratory experimental setup, 

Jayne (1959) was able to determine the resonant frequency 

of a specimen from a frequency response curve. In addition, 

sharpness of resonance (energy loss) was obtained using the 

half-power point method.

Pellerin (1965a,b) used a similar experimental setup to 

examine the free transverse vibration characteristics of 

dimension lumber and glulam timbers. After obtaining a 

damped sine waveform for a specimen, he analyzed it using 

equations for MOE and logarithmic decrement. Measured 

values of MOE and logarithmic decrement were then compared to static MOE and strength values. O’Halloran (1969) 

used a similar apparatus and obtained comparable results 

with softwood dimension lumber. Wang et al. (1993) used 

transverse vibration techniques to evaluate the static bending MOE of structural lumber. Ross et al. (1991) obtained 

comparable results through coupling relatively inexpensive 

personal computer technologies and transverse vibration 

NDE techniques. Research on the use of transverse vibration 

techniques to assess the potential of hardwood lumber for 

structural applications is summarized in Table 2.5.

The use of transverse vibration techniques has evolved 

considerably, especially in the 1990s. Excellent equipment 

that uses free transverse vibration techniques is described in 

Table 2.6. The equipment uses relatively low-cost personal 

computer technologies as part of their data acquisition and 

computing package. This equipment is used by lumber grading agencies and research organizations worldwide to assist 

in assessing the potential yield of structural lumber that can 

be obtained from a given wood species or species group.

Figure 2.3—Mass-spring dashpot vibration model (left) and transversely vibrating beam (right).

Figure 2.4—Transverse vibration of a beam: (a) damped 

sine wave for a free vibration, (b) frequency response curve 

for a forced vibration. 

Chapter 2
Nondestructive Evaluation of Wood: Second Edition

Static Bending, Transverse Vibration, and Longitudinal Stress Wave Nondestructive Evaluation Methods 9

Table 2.4—Summary of research using transverse vibration nondestructive evaluation techniquesa

Reference 

NDE 

technique Material 

NDE 

parameter 

measured 

Static 

test 

 

Reported 

properties 

Comparison of NDE 

parameters and properties 

(correlation coefficient r, unless noted) 

Jayne 

(1959)b 

Forced 

tranverse 

vibration 

Small clear 

Sitka spruce 

specimens 

Resonant 

frequency, 

Ed, Q 

Bending ESB, MOR ESB and Ed, 1,00,000 lb/in2 

MOR and Ed, 1,000 lb/in2 

MOR and Ed, 1,000 lb/in2 

MOR and density/Q, ± 1,000 lb/in2 

MOR and Ed/δ, ± 900 lb/in2

Pellerin 

(1965a) 

Free 

transverse 

vibration 

Douglas-fir 

glulam 

Natural 

frequency, 

Ed, δ

Bending ESB, MOR Predicted relative strength of three gluedlaminated members 

Pellerin 

(1965b) 

Free 

transverse 

vibration 

Inland Douglasfir dimension 

lumber 

Natural 

frequency, 

Ed,δ

Bending ESB, MOR ESB and Ed, 0.98 

MOR and Ed, 0.67–0.93 

MOR and 1/δ, 0.46–0.88 

MOR and 1/δ, 0.68–0.92 

O’Halloran 

(1969) 

Free 

transverse 

vibration 

Lodgepole pine 

dimension 

lumber 

Natural 

frequency, 

Ed, δ

Bending ESB, MOR ESB and Ed, 0.98 

MOR and Ed, 0.89 

MOR and 1/δ, 0.82 

MOR and Ed/δ, 0.91 

Wang et al. 

(1993) 

Free 

transverse 

vibration 

Spruce–Pine– 

Fir dimension 

lumber 

Ed Bending ESB ESB and Ed, 0.96–0.99 

Ross et al. 

(1991) 

Free 

transverse 

vibration 

Spruce–Pine– 

Fir dimension 

lumber 

Ed Bending ESB Ed and ESB, 0.99 

a

δ, logarithmic decrement; Ed, dynamic modulus of elasticity (MOE); ESB, MOE obtained from static bending test; MOR, modulus of rupture; 

Q, sharpness of resonance. 1 lb/in2 = 6.9 kPa. b

Correlation coefficients were not reported by Jayne. However, he did report 95% confidence intervals.

Table 2.5—Summary of research on the correlation between modulus of elasticity and other mechanical 

properties of hardwood lumbera

Reference

Species 

group

Nominal 

moisture 

content (%) Gradeb

Nominal

width 

(in.)

Growth 

location

NDE 

technique

Static 

property

Correlation 

coefficient

Green and 

McDonald 

(1993a) 

Northern 

red oak 

(Quercus 

velutina, 

Q. rubra)

12 1, 2, 3 4 Central 

Wisconsin

Transverse 

vibration 

(flatwise) 

ESB, UCS, 

UTS, MOR

ESB and Ed, 0.92

MOR and Ed, 0.58

UTS and Ed, 0 54

UCS and Ed, 0.70

ESB and Ed, 0.85

Green and 

McDonald 

(1993b) 

Maple 

(Acer 

rubra) 

12 SS, 2, 3 4 Central 

Vermont

Transverse 

vibration 

(flatwise) 

ESB, UCS 

UTS, MOR

ESB and Ed, 0.85

ESB and Ed, 0.42

UTS and Ed, 0.46

UTS and Ed, 0.60 a

Ed, dynamic modulus of elasticity (MOE) obtained from transverse vibration measurements; ESB, MOE obtained from static bending test; 

MOR, modulus of rupture; UTS, ultimate tensile stress; UCS, ultimate compressive stress. 1 lb/in2

 = 6.9 kPa. b

Grades by procedures given in the National Grading Rule performed by a quality supervisor of Southern Pine Inspection Bureau. 

SS, Select Structural.
General Technical Report FPL–GTR–238

10

Essential Laboratory Testing 

Procedures and Key Information

Figure 2.5 illustrates a typical free transverse vibration test 

setup. The specimen is simply supported at both ends. A 

slight deflection is introduced at the midspan of the specimen. The specimen is then allowed to freely oscillate in the 

vertical (transverse) direction. Frequency of oscillation is 

determined and, along with the specimen weight and dimensions, used to compute MOE. The MOE is calculated from 

the following formula:

 

(2.10)

where

f is resonant frequency (Hz),

W weight of specimen (lb),

S span (in.),

I moment of inertia (in4), and

g acceleration due to gravity (386 in/s2).

Three elements are essential to the test setup: a support apparatus, an excitation system, and a measurement system.

Support Apparatus

•	 The apparatus should provide vertical support to the 

ends of the specimen yet permit rotation.

•	 The specimen should be supported so as to prevent damage at the point of contact between the specimen and 

the reaction support. The reactions should be such that 

shortening and rotation of the specimen about the reaction resulting from deflection are not restricted.

•	 Provisions should be made at the reactions to allow for 

initial twist in the length of the specimen. If the bearing 

surfaces of the specimen at its reaction are not parallel, 

the specimen should be shimmed or the bearing surfaces 

rotated about an axis parallel to the span to provide adequate bearing across the width of the specimen.

•	 No lateral support should be applied. Specimens unstable in this mode should not be tested.

•	 The specimen should be positioned such that an equal 

portion of the length overhangs each support. Excessive 

overhang may alter results. If Equation (2.10) is used, 

the span to length (S/L) ratio of the specimen should 

exceed 0.98. Other S/L ratios may be used, but a more 

exacting analysis and equation are needed (Murphy 

1997). An overhang of approximately 1 in. (25.4 mm) on 

each end is often used in tests of dimension lumber. The 

amount of overhang may be influenced by the convenience of handling and positioning, but it should be kept 

uniform from specimen to specimen.

Excitation System

•	 The member should be excited so as to produce a vertical 

oscillation in a reproducible manner in the fundamental 

mode.

•	 Manual deflection of the specimen will provide sufficient 

impetus for oscillation for many products. The deflection 

should be vertical with an effort to exclude lateral components; neither excessive impact nor prolonged contact 

with the specimen is recommended. For example, a manual tap on a 16-ft. (4.8-m) nominal 2- by 12-in. (standard 

38- by 286-mm) piece of lumber with MOE of 2.0 × 

106 lb/in2 (13.8 GPa) and supported flatwise will result in 

a vertical oscillation of 3 to 4 Hz.

•	 Specimens with very high stiffness require mechanical 

excitation by a high force or carefully regulated impact/

release.

Measurement System

•	 Measurement of the frequency of oscillation should be 

obtained by either a force or displacement measuring 

device.

•	 Changes in force in response to the vibration at one or 

both supports are used to obtain frequency of oscillation.

•	 Frequency of oscillation can also be determined by 

measuring midspan displacement in response to initial 

displacement.

Figure 2.5—Schematic of typical free transverse vibration 

test setup.

Chapter 2

Table 2.6—Equipment for free transverse vibration techniques

Manufacturer Description of equipment

Metriguard, Inc. 

2465 NE Hopkins Court 

Pullman, WA 99163 

www.metriguard.com 

Model 340 Transverse Vibration E Computer—

Measures modulus of elasticity, weight, specific 

gravity, and damping ratio from transverse vibration 

of simply supported specimens. 

Ig

f 

2.46

MOE

r  (2.5) 

An

A

n

1 ln

1

1

  

(2.6) 

9  1

1

2 r r 

 

A A f

 f  (2.7) 

rf

f   

(2.8) 



 Q  (2.9) 

Ig

f WS

2.46

MOE

2 3

 (2.10) 
Nondestructive Evaluation of Wood: Second Edition

11

•	 It is critical that only the frequency associated with the 

fundamental vertical oscillation mode be used. Immediately after the specimen is excited, many vibration modes 

appear. The modes associated with higher frequencies 

than the fundamental bending frequency usually dissipate rapidly. Therefore, a short delay is necessary before 

acquiring the data to ensure that the data are related only 

to the fundamental vertical mode. This is an effective 

way to filter undesired modes from the data.

•	 The span-to-depth ratio should be greater than 60 unless 

special precautions are taken to permit higher frequency 

measurements. With small span-to-depth ratios, it is difficult to verify that the specimen is oscillating in a bending mode. Best results are obtained when the frequency 

of oscillation is less than 30 Hz.

Longitudinal Stress Wave 

Techniques

Background

Several techniques that employ stress wave propagation 

have been researched for use as NDE tools. Speed-of-sound 

transmission and attenuation of induced stress waves in a 

material are frequently used as NDE parameters.

To illustrate longitudinal stress wave techniques, consider 

application of one-dimensional wave theory to a homogeneous viscoelastic bar (Fig. 2.6). After an impact hits the 

end of the bar, a compression wave is generated. This wave 

immediately begins moving down the bar as particles at the 

leading edge of the wave become excited, while particles 

at the trailing edge come to rest. The wave depth or the 

distance between the leading and trailing edges of the wave 

is controlled by the length of the impactor and the velocity 

of propagation of the material that makes up the impactor. 

The wave moves along the bar at a constant speed, but its 

individual particles have only small longitudinal movements 

as a result of the wave passing over them. After traveling the 

length of the bar, this forward-moving wave impinges on the 

free end of the bar and is reflected as a tensile wave traveling back down the bar (Fig. 2.7). The velocity of the wave is 

independent of the intensity of the impact.

Energy is dissipated as the wave travels through the bar; 

therefore, although the speed of the wave remains constant, 

movement of particles diminish with each successive passing of the wave. Eventually all particles of the bar come to 

rest. Monitoring the movement of a cross section near the 

end of such a bar in response to a propagating stress wave 

results in waveforms that consist of a series of equally 

spaced pulses whose magnitude decreases exponentially 

with time (Fig. 2.8).

The propagation speed C of such a stress wave can be determined by coupling measurements of the time between 

pulses, Δt, and the length of the bar L by

(2.11)

The MOE can be computed using C and the mass density of 

the bar ρ:

(2.12)

Wave attenuation can be determined from the rate of decay 

of the amplitude of pulses using Equation (2.6) for logarithmic decrement.

Note that wave attenuation calculated by this formula is 

highly dependent upon characteristics of the excitation system used. Thus, results reported by various researchers cannot be directly compared because several excitation systems 

were employed. Nonetheless, these results show that energy 

loss characteristics as measured by stress wave techniques 

provide useful information on the performance of woodbased materials.

A more rigorous treatise on the measurement of energy loss 

by stress wave techniques is presented by Kolsky (1963). In 

Figure 2.6—Viscoelastic bar of length L subjected to an 

impact.

Figure 2.7—Travel of compression wave along bar. The 

forward-moving wave impinges on the free end of the bar, 

is reflected as a tensile wave, and begins to travel back 

down the bar. 

Figure 2.8—Waveforms consist of a series of equally 

spaced pulses whose magnitude decreases exponentially 

with time.

Static Bending, Transverse Vibration, and Longitudinal Stress Wave Nondestructive Evaluation Methods

t

L C

  2

2 MOE  C 

Transmission time 

L

t

2

 

Transmission time 

L

t 

t

L C

  2

2 MOE  C 

Transmission time 

L

t

2

 

Transmission time 

L

t 
General Technical Report FPL–GTR–238

12

general, a more appropriate method for evaluating energy 

loss would be to determine the quantity of energy imparted 

into a member and the corresponding rate of energy loss. 

Loss of energy would be calculated using an integral of a 

waveform, as is done for determining the energy emitted 

during acoustic emission testing of materials (Harris et al. 

1972). This is defined as the root mean square (RMS) value.

Wood is neither homogeneous nor isotropic; therefore, the 

usefulness of one-dimensional wave theory for describing 

stress wave behavior in wood could be considered dubious. 

However, several researchers have explored application of 

the theory by examining actual waveforms resulting from 

propagating waves in wood and wood products and have 

found that one-dimensional wave theory is adequate for 

describing wave behavior. For example, Bertholf (1965) 

found that the theory could be used to accurately predict 

dynamic strain patterns in small wood specimens. He verified predicted stress wave behavior with actual strain wave 

measurements as well as dependence of propagation velocity on the MOE of clear wood. Ross (1985) examined wave 

behavior in both clear wood and wood-based composites 

and observed excellent agreement with one-dimensional 

theory. Similar results were obtained with clear lumber in 

tests conducted by Kaiserlik and Pellerin (1977).

An interesting series of experiments designed to explore 

wave behavior in lumber was conducted by Gerhards (1981, 

1982). He observed changes in the shape of a wave front in 

lumber containing knots and cross grain by measuring the 

change in wave speed in the vicinity of such defects. He 

concluded that a stress wave traveling in lumber containing 

knots and cross grain does not maintain a planar wave front.

One common technique that employs stress wave NDE technology uses simple time-of-flight type measurement systems 

to determine stress wave propagation speed (Figs. 2.9 and 

2.10). In these measurement systems, a mechanical or ultrasonic impact is used to impart a longitudinal wave into a 

member. Piezoelectric sensors are placed at two points on 

the member and used to sense passing of the wave. The time 

required for the wave to travel between sensors is measured 

and used to compute wave propagation speed.

Stress wave transmission times on a per length basis for various wood species are summarized in Table 2.7. Note that 

stress wave transmission times are shortest along the grain 

(parallel to fiber) and longest across the grain (perpendicular 

to fiber). Note that for Douglas-fir and Southern Pine, stress 

wave transmission times parallel to the grain are approximately 200 μs/m (60 μs/ft). Stress wave transmission times 

perpendicular to the grain range from 850 to 1,000 μs/m 

(259 to 305 μs/ft).

Several research projects have been conducted on the use 

of one-dimensional wave theory for assessing the MOE of 

clear wood, lumber, and veneer. These studies have examined relationships between MOE values obtained from stress 

wave measurements and those obtained by static testing 

techniques. The strong correlative relationships found in 

these studies are shown in Table 2.8.

Table 2.9 summarizes research on the use of longitudinal 

stress wave techniques for assessing the strength properties 

of lumber and composite products. Kaiserlik and Pellerin 

(1977), for example, used stress wave techniques to evaluate the tensile strength of a small sample of clear lumber 

containing varying degrees of slope of grain. They used the 

one-dimensional wave equation, Equation (2.11), to compute MOE and the equation presented by Pellerin (1965b) 

for logarithmic decrement.

Extensive research has been conducted on the use of stress 

wave techniques for assessing the mechanical properties 

of wood composites (Fagan and Bodig 1985; Pellerin and 

Morschauser 1974; Ross 1984; Ross and Pellerin 1988; Vogt 

1985). Pellerin and Morschauser (1974) used the setup in 

Figure 2.11 to show that stress wave speed could be used to 

predict the flexural behavior of underlayment-grade particleboard. Ross (1984) and Ross and Pellerin (1988) showed 

that wave attenuation is sensitive to bonding characteristics 

and is a valuable NDE parameter that contributes significantly to the prediction of tensile and flexural mechanical 

Figure 2.9—System used to measure impact-induced 

stress wave propagation speed in various wood 

products.

Figure 2.10—Ultrasonic measurement system used to 

measure speed-of-sound transmission in various wood 

products.

Chapter 2
Nondestructive Evaluation of Wood: Second Edition

13

behavior of wood-based particle composites. Vogt (1985) 

examined the use of stress wave techniques with woodbased fiber composites. In an additional study, Vogt (1986) 

found a strong relationship between internal bond and stress 

wave parameters of particle and fiber composites.

The use of stress wave techniques with wood subjected 

to different levels of biodeterioration, which adversely 

affects mechanical properties, has been limited to studies 

that have employed only energy storage parameters 

(Table 2.10). For example, Pellerin et al. (1985) showed that 

stress wave speed could used to monitor the degradation of 

small, clear wood specimens exposed to brown-rot fungi. 

They showed a strong correlative relationship between 

stress wave speed and parallel-to-grain compressive strength 

of exposed wood. Rutherford et al. (1987) showed similar 

results. These authors also revealed that MOE perpendicular 

to the grain, measured using stress wave NDE techniques, 

was significantly affected by degradation from brown-rot 

decay and could be used to detect incipient decay. Chudnoff 

et al. (1984) reported similar results from experiments that 

utilized an ultrasonic measurement system and several hardwood and softwood species. Patton-Mallory and De Groot 

Static Bending, Transverse Vibration, and Longitudinal Stress Wave Nondestructive Evaluation Methods

Table 2.7—Summary of research on stress wave transmission times for various wood species

 

Stress wave transmission time 

(μs/m (μs/ft))

Reference Species

Moisture content

(% ovendry) Parallel to grain Perpendicular to grain

Smulski (1991) Sugar maple 12 256–194 (78–59) — 

Yellow birch 11 230–180 (70–55) — 

White ash 

Red oak 

12 

11 

252–197 (77–60) 

262–200 (80–61) 

— 

Armstrong et al. (1991) Birch 

Yellow-poplar 

Black cherry 

Red oak

4–6 

4–6 

4–6 

4–6 

213–174 (65–53) 

194–174 (59–53) 

207–184 (63–56) 

226–177 (69–54)

715–676 (218–206) 

715–676 (218–206) 

689–620 (210–189) 

646–571 (197–174)

Elvery and Nwokoye (1970) Several 11 203–167 (62–51) — 

Jung (1979) Red oak 12 302–226 (92–69) — 

Ihlseng (1878, 1879) Several — 272–190 (83–58) — 

Gerhards (1978) Sitka spruce

Southern Pine 

10 

9 

170 (52) 

197 (60) 

— 

Gerhards (1980) Douglas-fir 10 203 (62) — 

Gerhards (1982) Southern Pine 10 197–194 (60–59) — 

Rutherford (1987) Douglas-fir 12 — 1,092–623 (333–190)

Ross (1982) Douglas-fir — — 850–597 (259–182) 

Hoyle and Pellerin (1978) Douglas-fir 9 — 1,073 (327)

Pellerin et al. (1985) Southern Pine 9 200–170 (61–52) 613–1,594 (187–486) 

Soltis et al. (1992) Live oak 12 — 613–1,594 (187–486) 

Ross et al. (1994) Northern red and

white oak 

Green — 795 (242) 

Table 2.8—Summary of research on correlation between stress wave 

and static modulus of elasticity of clear wood, lumber, and veneer

Reference Material

Static

loading

Correlation 

 coefficient r

Bell et al. (1954) Clear wood 

Clear wood 

Compression 

Bending

0.98 

0.98 

Galligan and Courteau (1965) Clear wood

Lumber

Bending 0.96 

Koch and Woodson (1968) Veneer Tension 0.96–0.94

Porter et al. (1972) Lumber Bending 0.90–0.92 

Pellerin and Galligan (1973) Lumber 

Veneer 

Bending 

Tension 

0.96 

0.96 

McAlister (1976) Veneer Tension 0.99 

Gerhards (1982) Knotty lumber

Clear lumber 

Bending 

Bending 

0.87 

0.95 
General Technical Report FPL–GTR–238

14

(1989) reported encouraging results from a fundamental 

study dealing with the application of acousto-ultrasonic 

techniques. Their results showed that energy loss parameters 

may provide useful additional information on early strength 

loss from incipient decay caused by brown-rot fungi.

Verkasalo et al. (1993) and Ross et al. (1992) obtained 

encouraging results when using stress wave techniques to 

identify bacterially infected red oak. They found that speed 

of sound transmission perpendicular to the grain was significantly slower in sections of wood containing bacterial 

infection.

Stress wave techniques, especially those relying on measurement of transmission times, are used worldwide. Current manufacturers of stress wave timing equipment are 

listed in Table 2.11.

Essential Laboratory Testing 

Procedures and Key Information

The following text describes essential laboratory testing 

procedures and additional key information for using stress 

wave measurements in a laboratory environment.

Pulse Echo Test Setup

Figure 2.11 illustrates a typical pulse echo test setup. An 

impact or “pulse” on one end of the specimen induces a 

Figure 2.11—Typical pulse echo test setup. 

Chapter 2

Table 2.9—Summary of results from studies using nondestructive stress wave testing techniques on 

lumber and wood composite productsa

Reference 

NDE 

technique Material 

NDE 

parameter

measured 

Static

test 

Reported 

properties 

Comparison of NDE parameters

and static properties (correlation 

coefficient r, unless noted)

Kaiserlik and 

Pellerin (1977)

Longitudinal 

stress wave

Douglas-fir 

boards

C Ed, δ Tension UTS UTS and Ed, 0.84; UTS and 

combination of Ed, δ, 0.90 

Pellerin and 

Morschauser 

(1974)

Longitudinal 

stress wave

Underlayment 

particleboard

C Bending ESB, MOR ESB, and C2, 0.93–0.95 

MOR and C2

, 0.87–0.93

Ross (1984), 

Ross and 

Pellerin 

(1988)

Longitudinal 

stress wave

Underlayment 

and industrial 

particleboard, 

structural panel 

products 

C, Ed, δ Tension EST, UTS EST and C2

, 0.98

EST and Ed, 0.98

UTS and C2

, 0.91

UTS and Ed, 0.93 

UTS and 1/δ, 0.63

UTS and combination of Ed, 

1/δ, 0.95

Fagan and 

Bodig (1985) 

Longitudinal 

stress wave

Wide range of 

wood composites

C Bending MOR Simulated and actual MOR 

distributions were similar.

Vogt (1985) Longitudinal 

stress wave

Medium-density 

fiberboard 

C, Ed, δ Tension EST, UTS EST and C2

, 0.90 

EST and Ed, 0.88 

UTS and C2

, 0.81 

ESB, and Ed, 

Combination, 0.88

Underlayment 

and industrial 

particleboard 

 Bending ESB, MOR ESB, and C2

, 0.76 

ESB, and Ed, 0.72 

MOR and C2

, 0.96 

MOR and C2

, 10.92 

Combination, 0.97

Vogt (1986) Stress wave 

(through 

transmission) 

Underlayment 

and industrial 

particleboard, 

structural panel 

products 

Ct, Edt Internal 

bond 

IB IB and Ct

2

, 0.70–0.72 

IB and Edt, 0.80–0.99

a

C, speed of sound; Ct, speed-of-sound transmission through thickness; δ, logarithmic decrement; Ed, dynamic modulus of elasticity (MOE) from 

transverse vibration or stress wave measurements; Edt, dynamic MOE through thickness orientation; ESB, MOE from static bending test; IB, 

internal bond; MOR, modulus of rupture; UTS, ultimate tensile stress.
Nondestructive Evaluation of Wood: Second Edition

15

wave to flow along the length of a specimen. The pulse is 

“echoed” from the opposite end of the specimen, hence the 

term pulse echo. Characteristics of waveforms observed 

when using the pulse echo technique are highly dependent 

upon the type of sensor used. Various types of sensors, 

including those that measure particle displacement, particle 

velocity, and particle acceleration or strain, can be used with 

this type of test setup. Figure 2.12 illustrates example waveforms obtained by transducers that measure particle velocity 

and acceleration. Note that the time required for a pulse to 

Static Bending, Transverse Vibration, and Longitudinal Stress Wave Nondestructive Evaluation Methods

Table 2.10—Research summary of correlation between nondestructive testing parameters and properties of 

degraded wood 

Reference

NDE 

technique Material

Degradation 

agent

NDE 

parameter 

measured

Static 

test 

Reported

properties 

Comparison of NDE parameters 

and static properties (correlation 

coefficient r, unless noted) 

Chudnoff 

et al. 

(1985)

Longitudinal 

stress wave 

(parallel to 

the grain) 

Decayed 

and sound 

mine props; 

26 species 

or species 

groupings

— Ed Compression 

parallel to 

grain 

Ec, UCS Ec and Ed, 0.84–0.97 (all species 

combined, hardwoods, maple, 

and oaks) 

Ec and Ed, 0.73–0.81 (all species, 

combined, southern pines, 

lodgepole pine) 

UCS and Ed, 0.85–0.95 (all species 

combined, hardwoods, maple, 

and oaks)

Pellerin 

et al. 

(1985)

Longitudinal 

stress wave 

(parallel to 

the grain) 

Small clear 

southern 

yellow pine 

specimens 

Brown rot 

fungi 

(Gloeophyllum 

trabeum) 

C, Ed Compression 

parallel to 

grain 

UCS UCS and C: 

 0.47 (control) 

 0.73 (exposed) 

 0.80 (control and exposed) 

UCS and Ed, 0.86 (control) 

 0.86–0.89 (exposed) 

 0.94 (control and exposed)

 Termites 

(subterranean) 

C, Ed UCS and C: 

 0.65 (control) 

 0.21 (exposed) 

 0.28 (control and exposed) 

UCS and Ed: 

 0.90 (control) 

 0.79 (exposed) 

 0.80 (control and exposed)

Rutherford 

(1987), 

Rutherford 

et al. 

(1987) 

Longitudinal 

stress wave 

(parallel to 

the grain) 

Small, clear 

Douglas-fir 

specimens 

Brown-rot 

fungi 

(Gloeophyllum 

trabeum) 

C, Ed Compression 

parallel to 

the grain 

Ec, UCS Ec and C, 0.91 

Ec and Ed, 0 94 

UCS and C, 0.67–0.70 

UCS and Ed, 0 79

UCS and MOE, 0.80

PattonMallory 

and 

De Groot 

(1989)

Longitudinal 

stress wave 

Small, clear 

southern 

yellow pine 

specimens 

Brown rot 

fungi 

(Gloeophyllum 

trabeum) 

C, root 

mean 

square 

voltage 

frequency 

content of

received 

signal 

Bending Maximum 

moment, 

alkali 

solubility 

Linear decrease in C and decrease 

in strength with increased wood 

degradation. High-frequency 

components of signal attenuated in 

very early stages of decay.

Ross et al. 

(1992)

Longitudinal 

stress wave 

(parallel to 

the grain)

Red and 

white oak 

lumber 

Bacteria 

(Clostridium

and 

Erwinia sp.)

C None Presence 

of infection

Decrease in C with presence 

of infection 

Verkasalo 

et al. 

(1993)

Longitudinal 

stress wave 

(perpedicular 

to the grain)

Red oak 

lumber 

Bacteria 

(Clostridium

and 

Erwinia sp.)

C Tension 

perpendicular 

to grain 

UTS, 

presence 

of infection

Decrease in C and UTS with 

presence of infection

a

AE, acoustic emission; C, speed of sound; Ec, modulus of elasticity (MOE) from static compression test; Ed, dynamic M or stress wave measurements; 

MOR, modulus of rupture; UCS, ultimate compressive stress; UTS, ultimate tensile stress.
General Technical Report FPL–GTR–238

16

travel a round trip through the specimen can be obtained by 

using any of these transducers. The formula for transmission 

time is

where Δt is time between pulses and L is specimen length. 

Note that this technique, as described, requires that both 

ends of the specimen are freely supported. A fixed end 

support condition will yield waveforms with different 

characteristics.

Pitch and Catch Test Setup

Figure 2.13 illustrates a typical pitch and catch test setup. A 

pulse is introduced in the specimen, sensed by a transducer, 

and then allowed to flow through the specimen. The leading edge of the pulse is then sensed by a second transducer 

located further down the specimen. A pitch and catch test 

setup yields an electronic signal similar to that illustrated 

in Figure 2.14. The time required for the wave or pulse to 

travel between the sensors can be calculated by the following equation:

where Δt is time between pulses and L is distance between 

transducers.

There are two key points to consider when using the pitch 

and catch test setup:

1. The transducers must be in line with each other.

2. Many transducers are sensitive to the manner in which 

they are installed. For example, commonly used accelerometers yield wave forms that are strongly dependent 

upon which direction they sense the pulse. Note that in 

Figure 2.15, the base of the accelerometer directly faces 

an approaching compressive wave. Simply turning the 

accelerometer so that its base faces away from the approaching compressive wave changes the characteristics 

of the waveform.

Figure 2.12—Example waveforms obtained by transducers 

that measure particle velocity and acceleration. 

Figure 2.13—Typical pitch and catch test setup. Pulse 

is introduced in specimen, sensed by transducer, and 

allowed to flow through specimen. 

Figure 2.14—Electronic signal yielded by pitch and 

catch test setup. 

Chapter 2

Table 2.11—Commercially available stress wave timing equipment

Manufacturer Product Website Location 

Agricef USLab www.agricef.com.br 

CBS-CBT Sylvatest-Duo www.cbs-cbt.com Saint-Sulpice, Switzerland 

Fakopp Enterprise Various models www.fakopp.com Agfalva, Hungary 

IML GmbH IML Micro Hammer www.imlusa.com Moultonborough, 

New Hampshire, USA 

Metriguard, Inc. Models 239A Stress 

Wave Timer 

www.metriguard.com Pullman, Washington, USA 

James Instruments, Inc. V-Meter MK IV www.ndtjames.com Chicago, Illinois, USA 

Olson Instruments, Inc. Various models www.olsoninstruments.com Wheat Ridge, Colorado, USA 

t

L C

  2

(2.11) 

2 MOE  C (2.12)

Transmission time 

L

t

2

 

Transmission time 

L

t 

t

L C

  2

(2.11) 

2 MOE  C (2.12)

Transmission time 

L

t

2

 

Transmission time 

L

t 
Nondestructive Evaluation of Wood: Second Edition

17

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General Technical Report FPL–GTR–238

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Galiginaitis, S.V.; Bell, E.R.; Fine, A.M. [et al.]. 1954. Nondestructive testing of wood laminates. Final report. Louisville, KY: Office of Naval Research, Institute of Industrial 

Research, University of Louisville. 

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

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dimension lumber. Special Tech. Pub. 353. Philadelphia, 

PA: American Society for Testing Materials.

Static Bending, Transverse Vibration, and Longitudinal Stress Wave Nondestructive Evaluation Methods

21

Chapter 3

Ultrasonic-Based Nondestructive 

Evaluation Methods for Wood 

C. Adam Senalik, Research General Engineer 

Greg Schueneman, Supervisory Research Materials Engineer

Robert J. Ross, Supervisory Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

The purpose of this chapter is twofold: to provide a basic 

primer to nondestructive testing using ultrasonic inspection 

and to provide a literature review of the use of ultrasonic 

techniques in the inspection, characterization, classification, 

and evaluation of wood and wood products. This chapter 

does not present a detailed explanation of wave theory, a 

robust description of wave propagation through heterogeneous anisotropic mediums, such as wood, or derivations 

of fundamental equations. For a more rigorous explanation 

of ultrasonic wave motion through wood and foundational 

equations upon which the motion is based, the reader is directed to Bucur (2003, 2006, 2011a). 

This chapter was originally published in 2014 as a Forest 

Products Laboratory General Technical Report:

Senalik, C. Adam; Schueneman, Greg; Ross, Robert 

J. 2014. Ultrasonic-based nondestructive evaluation 

methods for wood: a primer and historical review. 

General Technical Report FPL–GTR–235. Madison, 

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

Forest Products Laboratory. 31 p.

Background

When preparing an ultrasonic test of a specimen, several 

factors must be considered. Among these are source, power, 

frequency, receiver, and coupling. These factors must be 

determined holistically for any inspection as the choice of 

any one affects the others. For example, a particularly large 

specimen might require high power for the signal to be 

observable. Generally the size at which internal defects are 

discernable is related to the frequency used to inspect the 

specimen; higher frequencies can detect smaller defects. The 

source selected must be capable of generating the necessary 

signal power at the desired frequency. The source may also 

act as the receiver, such as in a pulse-echo test setup. If the 

receiver is non-contact, such as a microphone or non-contact 

transducer, then no coupling is needed. If contact is necessary, then a coupling agent must be chosen that is adequate 

for the test and will not corrupt the condition of the 

specimen.

Sources generate stress waves that are used to inspect the 

specimen. A source may be anything capable of generating 

detectable stress waves. In a nondestructive test, the chosen 

source must be able to generate the desired signal without 

altering the condition of the specimen post-test. Two common external sources are mechanical impacts and piezoelectric transducers. The source may originate from within 

the specimen itself. Some internal phenomena are capable 

of generating a detectable emission. Microfractures, check 

formation, water movement, and pit aspiration are just a few 

internal phenomena in wood that may be detectable by external receivers. This type of inspection is known as acoustic 

emission.

Ultrasonic waves have frequencies of 20 kHz and higher. 

The resolution of any ultrasonic scan is dependent upon the 

frequency. As frequency increases, wavelength decreases. 

The smallest discernable feature during an ultrasonic scan is 

about one half the wavelength. Signal attenuation in wood 

increases as frequency increases; the phenomenon becomes 

more evident at higher frequencies. As attenuation increases, 

the energy loss of the wave as it transverses the wood cross 

section becomes large. Balance must be struck between 

obtaining the best resolution possible while maintaining an 

observable level of signal energy. Today, ultrasonic inspection equipment exists with sufficient power for inspection 

of standing trees and poles, but this has not always been 

the case. Historically, because of power limitations of the 

inspection equipment, ultrasonic waves have more commonly been used for inspection of members with smaller 

cross sections such as rectangular members and engineered 

wood products. Standing trees and poles were more often 

inspected with higher power acoustic techniques using mechanical impacts as the signal source. Ultrasonic waves for 

testing are commonly produced by piezoelectric transducers 

that convert voltage to mechanical motion. Inspectors can 

purchase ultrasonic transducers with center frequencies between 20 kHz and up to the megahertz range.

Transducers may or may not require contact with the wood 

specimen. If contact is necessary, a coupling agent, known 
General Technical Report FPL–GTR–238

22

as couplant, is normally required. The couplant aids the 

transmission of the transducer pulses into the test specimen. The couplant used is chosen based upon inspection or 

test application. Common couplants include water, grease, 

glycerin gels, petroleum jelly, starch glucose, cellophane 

sheets, and silicone rubber, just to name a few. Care must 

be used when selecting a couplant, as they may be capable 

of corrupting the condition of the specimen (Bucur 2006). 

Pressure between transducers and the specimen must also be 

considered. In general, as the pressure between the source 

and specimen increases, the power transmission increases; 

however, there is a point of diminishing returns above which 

additional applied pressure yields little additional transmitted energy. Non-contact transducers use the medium surrounding the test specimen as the coupling agent. Aircoupled transducers, as the name implies, use surrounding 

air as the coupling agent. Immersion tests submerge transducers and specimen in a liquid that acts as the coupling 

agent.

Stress Waves in Wood

Wood in its natural form is often assumed to be a cylindrically orthotropic material with three principle directions: 

longitudinal along the tree length, radial from the exterior 

to the tree center, and tangential around the tree center circumferentially. Orthotropic materials have nine independent 

elastic constants: three Young’s moduli, three shear moduli, 

and three Poisson’s ratios (Hearmon 1961). For wood, an 

assumption of six independent Poisson’s ratios rather than 

three has been found to more accurately represent wood 

structure (Bucur 2006).

Within an infinite solid medium are two types of waves, 

longitudinal and shear. Longitudinal waves have particle 

motion normal to the wave front and parallel to the direction 

of wave propagation. Shear waves have particle motion parallel to the wave front and normal to the direction of wave 

propagation. In an orthotropic material, each principle direction will have an associated longitudinal wave velocity and 

two shear wave velocities. The relationship between stresswave velocity and material stiffness has been established 

through the equations of motion, Hooke’s law, and Christoffel stiffness tensors (Hearmon 1961; Graff 1975; Bucur 

2006). Derivation of the relationship can be found in many 

texts, including the two references provided and will not 

be repeated here. For a longitudinal stress waves traveling 

along a principle axes, the relationship can be expressed as 

 CD = ρVL

2 (1)

where

CD is dynamic stiffness,

ρ density, and 

VL velocity of the longitudinal wave.

Similar relationships can be developed for shear waves 

(Bucur 2006). Stress-wave velocity, therefore, yields information about elastic constants of the material. In many 

analyses, velocity or the squared value of velocity is correlated to modulus of elasticity. Whereas modulus of elasticity 

and stiffness are related, it is important to remember that 

values are not interchangeable and that stiffness is influenced by the Poisson’s ratio of the material. The description 

above characterizes bulk wave behavior. Wave velocity 

is affected by dimensions of the specimen. Wave motion 

through plates and rods require special consideration beyond 

the simple analysis given here and can be found in Hearmon 

(1961), Graff (1975), and Bucur (2006).

Energy storage and dissipation properties of wood and engineered wood products are controlled by the same mechanisms that dictate many of their physical and mechanical 

properties. For example, consider how the microscopic 

structure of clear wood affects its energy storage, loss properties, and mechanical behavior. Clear wood is a composite 

material composed of long microfibrils of cellulose and 

hemicellulose cemented together with lignin. At the microscopic level, energy storage properties are controlled by 

orientation of the microfibrils and their structural composition, factors that contribute to stiffness and strength. Energy 

storage properties are observable as frequency of oscillation 

in vibration or speed of sound transmission. Conversely, 

energy dissipation properties are controlled by internal friction characteristics, to which bonding behavior within and 

between microfibrils contribute significantly. Rate of decay 

of free vibration or wave attenuation are frequently used to 

observe energy dissipation properties of wood-based materials (Pellerin and Ross 2002).

Wave velocity and attenuation are directly related to material properties. As a result, they are often used for defect 

detection. Fungal decay, cracks, and voids are defects that 

can degrade the structure of wood, lessening its strength and 

toughness. Fungal-infected wood decreases wave velocity and increases signal attenuation. Waves traveling from 

a source to a receiver will have to travel around cracks 

and void lengthening their travel distances. Greater travel 

distances increase both travel time and signal attenuation. 

Sensitivity to internal defects makes wave velocity and attenuation useful and commonplace parameters when assessing wood. However, there are a myriad of parameters that 

researchers have used for condition assessment. The list of 

parameters includes but is not limited to spectral content, 

rise time, pulse count above a threshold, signal length, root 

mean square of signal energy, time centroid, frequency 

centroid, decay rate, peak amplitude, and peak frequency. 

Inspectors and researchers may use one or dozens within a 

single assessment and often create new parameters to correlate to particular phenomenon.

Table 3.1 contains several common analysis techniques that 

are applied to ultrasonic measurements and recorded signals 

obtained during testing. Descriptions of techniques and 

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

23

the mathematical expression for each are also given. The 

literature review that follows this section contains several 

examples where these and other analysis techniques are applied. The study ends with Table 3.2, which is a list of the 

tree species examined by studies in the literature review and 

associated bibliographic references.

Ultrasonic Inspection Methods

Several types of ultrasonic inspection can be used. Presented 

here are summaries of basic one or two sensor methods. 

Many other methods use multiple sensors; for instance, in 

linear arrays (Bray and Stanley 1997) or oriented around 

the specimen at multiple locations (Divos and Divos 2005). 

Single sensor methods are often referred to as pulse-echo 

methods. Two sensor methods are often referred to as 

through transmission methods. The stress waves from the 

source may be either longitudinal or shear waves depending 

upon the source selected. The source may be oriented such 

that the pulse incident to the specimen surface is either normal or angled. Below are brief descriptions of pulse-echo, 

through-transmission, and angled beam techniques. For 

greater detail regarding ultrasonic inspection techniques, the 

reader is directed to Bray and Stanley (1997), Bucur (2002, 

2006), and Divos and Divos (2005).

Pulse Echo Test Method

Figure 3.1 illustrates a typical normal oriented pulse echo 

test configuration. A pulse on one side of the specimen 

induces stress waves that travel through the cross section, 

as shown in Figure 3.1a. As previously mentioned, stress 

waves may be either longitudinal or shear waves depending upon the source selected. A common pulse source is a 

piezoelectric transducer with a center frequency and power 

selected by the user. The pulse is initiated from the same 

transducer used to sense returning waves. As shown in Figure 3.1c, waves created by the pulse strike the opposite side 

of the specimen and are reflected, or “echoed,” back toward 

the transmitting sensor; hence the term pulse echo. Characteristics of waveforms observed are highly dependent upon 

the type of sensor used. Various types of sensors, including 

those that measure particle displacement, particle velocity, 

and particle acceleration can be used with this type of setup.

Table 3.1—Data analysis techniques 

Ultrasonic 

parameter Description Mathematical expression 

Time of flight 

(TOF) 

Measure of the time 

required for an 

ultrasonic packet of 

energy to travel 

through the material. 

Usually expressed as 

per unit length. 

TOF =

t2–t1

l

where 

t2 is arrival time, 

t1 initiation time, and 

l travel distance between source and receiver. 

Pulse length (PL) Measure of spreading 

of received waveform 

with respect to a 

standard waveform. 

Influenced by 

differences in path 

length and sound 

speed that tend to 

spread waveform. 

PL = KΔt∫v(tt)�dt

where 

K is a constant, 

Δt time required for received wave energy integral 

from 10% to 90% of its final value, 

v signal voltage as a function of time, and 

t time. 

Insertion loss (IL) Ratio of energy 

received, after 

transmission through 

material, to energy 

input. 

IL = 10 log �G

Er

Et

�

where 

Er is received energy, 

Et transmitted energy, and 

G receiver gain. 

Elastic constants 

determination 

Relate longitudinal 

and shear wave 

velocities to 

Poisson’s ratio and 

dynamic modulus of 

elasticity. 

CD = ρVL

2 GD = ρVτ

2

where 

μ is Poisson’s ratio, 

VL longitudinal wave velocity, 

Vτ shear wave velocity, 

ρ density, 

CD dynamic stiffness, and 

GD dynamic shear elastic modulus. 

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

24

Defects such as splits and voids can also be detected in this 

manner. Incident waves are reflected from these defects as 

shown in Figure 3.1b. If reflected waves are reflected back 

to the transmitting/receiving sensor as shown in 3.1d, then 

the depth of the defect can be calculated using the known 

speed of the traveling wave. Orientation of the defect with 

respect to the direction of wave travel can greatly affect how 

well it is discerned. A wood split oriented perpendicular to 

the wave travel path will reflect more wave energy than a 

split oriented parallel to the wave path. In a worst case inspection scenario, it is possible for a narrow split parallel to 

the wave path to be completely unobserved because of low 

defect reflectivity. A wave could travel on either side of a 

narrow split, reflect from the far edge of the specimen, and 

return to the transmitting/receiving sensor. In this case, the 

inspector may believe the specimen is completely free of 

defects. A more common case is a defect that is oriented at 

an angle to the wave path other than perpendicular (90°) or 

parallel (0°). In this case, some portion of the wave energy 

may be reflected away from the transmitting/receiving sensor. The inspector may be able to determine presence of the 

defect by a drop in magnitude in the received signal or a 

lack of an echo from the far edge of the specimen (Bray and 

Stanley 1997), or both.

Pulse echo method is convenient as only one side of the 

specimen needs be accessible for inspection. Also, if the 

wave speed through the specimen is known, depth of defects 

and thickness of specimen can be estimated from the travel 

time of the wave. Pulse echo does have some drawbacks. 

The pulse source must impart sufficient energy into the 

specimen such that an observable reflected signal is measureable. Wood is highly attenuating, and the traveling wave 

will need to be of sufficient magnitude to lose energy during 

two trips across the cross section and still be measureable 

at the end. If the transmitting and receiving transducer is 

one in the same, then ring down time must be considered. 

After the transducer pulses, the mechanical element requires 

a finite period of time to come to rest. This time is known 

as the ring down time. If the specimen is thin, the traveling 

wave can reflect from the far side of the specimen and return 

to the sensor before the mechanical element in the sensor 

has come to rest. In this case, the reflected signal will be 

contaminated by element motion still present from the initial 

pulse.

Through Transmission Test Method

Figure 3.2 illustrates a typical normal oriented through 

transmission test configuration. Like the pulse echo method, 

the signal source initiates a pulse that travels through the 

cross section of the specimen. As previously mentioned, 

stress waves may be either longitudinal or shear waves 

depending upon the source selected. Unlike the pulse echo 

method, a second separate sensor receives the signal and the 

source and receiving sensor are not co-located. For simplicity in this description, the source is a transducer of the same 

type as the receiving transducer; however, many different 

types of signal sources may be used. In Figure 3.2a, the 

source is on one side of the specimen and the receiver is 

directly across the specimen on the opposite side. This configuration is commonly known as through transmission. Because the waves need only travel across the specimen once, 

the power for the source signal can be lower than in pulse 

echo. This method reduces the probability of ring-down 

contamination as the receiving sensor was not in a state of 

motion prior to arrival of the wave. In Figure 3.2b, presence 

of a defect blocks the wave from the receiver. The defect 

may also redirect the wave energy around the defect that 

increases travel time between the source and receiver. Defects are detected by a loss of signal or an increase in time 

of flight; however, the depth of the defect is not known. One 

practical consideration of through transmission methods is 

that operating two sensors increases testing complexity and 

can increase complexity of analysis of results.

Angled Beam Methods

Figure 3.3 shows an alternative through transmission configuration. The transmitting source is tilted off normal to 

the specimen edge to direct the wave path at an angle to the 

cross section. Pulse-echo test configurations may also have 

an off-normal pulse direction. The incident wave reflects 

Figure 3.1—Typical pulse-echo test configuration using (a) clear path from transducer to opposite 

specimen edge, (b) defect is in the wave path, (c) wave reflects off opposite specimen edge and 

returns to the sensor, (d) wave reflects off the defect and returns to sensor.

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

25

off of observable boundaries such as the opposite edge of 

the specimen or defects, and travels back towards the edge 

from which the wave originated. The reflecting wave arrives 

at a location away from the source and is observed with a 

second sensor. This method is capable of detecting defects 

that are oriented normal to the edges of the specimen. This 

method can require more power than the through transmission method, as the wave travel distances are often greater.

A surface inspection can be performed by angling the source 

sensor to initiate a surface wave. This type of inspection is 

used to assess surface quality. The wave travels along the 

specimen surface and reflects off of near surface defects or 

sharp corners. Presence of surface waves in subsurface inspections has potential to cause spurious signals to be 

observed.

Literature Review

This literature review is divided into two subject areas: testing and analysis and inspection and assessment. Testing and 

analysis describes studies in which the process of testing 

using ultrasonic stress waves was the focus. Inspection and 

assessment describes studies that focus upon inspection of 

wood members or structures, determining their strength, and 

defect detection.

Testing and Analysis

There are several considerations when testing using ultrasonics. Beyond source, power, frequency, receiver, and 

coupling, other concerns include testing methodology, 

moisture content (MC), specimen geometry, and post-test 

analysis. The purpose of testing is also relevant; evaluating 

elastic properties of wood can involve testing protocols that 

are very different than those needed for inspection and assessment. This section describes how researchers have dealt 

with many of these issues.

The subsections address testing considerations, acoustic 

emission/acousto-ultasonics, MC, and determination of elastic constants of wood. The first subsection addresses testing considerations and focuses upon testing methodologies 

and post-test analyses. Moisture content of wood has great 

potential to change wood properties and affect ultrasonic 

results. Ultrasonic techniques are powerful tools when determining elastic constants of any material, including anisotropic materials such as wood. Acoustic emission is a testing 

method that uses stress waves emitted from the examined 

specimen. Acousto-ultrasonics assumes a source other than 

the specimen itself, but uses many of the same post-test 

analysis techniques.

Testing Considerations

Reliable and repeatable test results are only achieved when 

testing parameters are well suited for the testing conditions 

and specimen. Sensor placement, sensor coupling, resolution, specimen shape, and wood anisotropy are a few of the 

issues that must at be considered. Several researchers have 

examined the effect these conditions have upon the test 

results.

Anthony and Phillips (1991) refined the use of acoustoultrasonic technique to evaluate fingerjoint strength through 

an examination of the test configuration. Advantages and 

disadvantages of various sensor locations were assessed. 

Broadband and narrowband excitation signals were compared. High frequencies of the broadband excitation were 

greatly attenuated. Narrowband excitation focused the signal 

energy in a narrow range, maximized the potential of high 

frequency signals to propagate through the wood. Several 

transducer types were studied with a variety of coupling 

agents. At the time of the study in 1991, Anthony and Phillips concluded that the ideal dry couplant for a manufacturing environment was compliant with respect to wood, had 

low creep properties, did not attenuate stress waves, and 

bonded easily to transducers. No such material was available at that time. 

Hamstad and others (1993) explored farfield wideband 

acoustic wave behavior in wood rods and plates. Orientation of the waveform source and use of narrow band 

measurement systems were found to significantly affect 

the measured waveform. Potential to measure frequency 

Figure 3.2—Typical through transmission test configuration: (a) clear specimen, (b) defect blocking 

the wave from the receiver.

Figure 3.3—Angled through transmission test 

configuration.

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

26

dependence of material attenuation with a single waveband 

experiment was examined.

Relationship between wavelength and resolution and difficulties in visualizing data from acoustic microscopy were 

discussed in Bucur (1996). 

Feeny and others (1996) examined how the frequency 

domain was affected by changes in wave speed through different types of wood within a tree. Speed through juvenile 

earlywood, juvenile latewood, adult earlywood, and adult 

latewood were measured. The goal of the research was to 

determine whether periodicity of wood caused stop bands 

within the frequency spectrum. Evidence of stop bands was 

found in Scots pine (P. sylvestris L.) but not spruce; however, wood variability and mode conversion made measurements difficult. 

Berndt and others (2000) explored movement of wave energy through wood and noted that anisotropy had significant 

interaction with wave pulses. The experimental setup was 

constructed to primarily examine bulk waves. Phase shift 

was investigated as a tool to determine arrival time when 

high frequencies with attenuation are used. Mode conversion led to inaccuracies in energy measurements. The results 

strongly suggest that single path measurements were not 

advisable.

Bucur (2002) described three algorithms used for ultrasonic 

tomographic imaging: transform, iterative, and direct 

inversion.

Andrews (2002) examined the differences in measured 

wave velocity depending upon the method used and sensor 

location. Wave velocity of radiata pine (Pinus radiata) logs 

was measured end to end and incrementally along the length 

along the edge of the log. Wave speed measurements taken 

incrementally along the length of the log were highest, followed by time of flight end to end of the log, then followed 

by resonance methods. Time of flight was found to differ by 

as much as 6% depending upon source and receiver location. Resonance methods were not affected by sensor location on opposite sides of the log. Differences between wave 

velocity measured using resonance methods and end to end 

time of flight were expected to decrease with increasing 

length to diameter ratio.

Berdnt (2002) noted a stiffness gradient across tree trunks in 

specimens of white fir. Mature wood had a greater stiffness 

than juvenile wood. 

He and others (2005) applied a series of filters to acoustic 

emission to increase the signal to noise ratio. Five different 

filters were applied to measurements collected from through 

transmission ultrasonic tests. Filters used included averaging, symmetrical moving average, two infinite impulse 

responses, and a filter created through fuzzy logic, which 

was the subject of the study. 

In a 2005 study, Berndt and others developed phase slope 

methods to increase accuracy of time of flight measurements. The threshold method of measuring time of flight 

was affected by low amplitude or high attenuation. The 

phase slope method was effective at measuring thin wood 

samples. The phase slope method was also effective at 

separating multiple pulses within a single pulse. 

Tjondro and Soryoatmono (2007) explored how specimen 

geometry affected elastic constant measurements. The 

assumption that MOE was the same as stiffness yielded an 

error of 6% in the longitudinal direction and up to 24% in 

the radial and tangential directions. Higher length to diameter ratios yielded higher estimates for longitudinal velocity. 

A square cross section for the specimen was found to yield 

the best results. 

The effect of coupling media upon wave attenuation for longitudinal and transverse transducers was explored by Trinca 

and others (2009). A 100-kHz transducer was coupled to 

nine different species of Brazilian woods using six different 

coupling agents. Attenuation for each coupling was measured. To obtain consistent measurements, it was necessary 

to apply pressure to transducers. 

Agrawal and Choudhari (2011) estimated the strength of 

wood using 22 measured NDE parameters. Signals collected 

using a through transmission test setup were subjected 

to FFT and the power spectral density was obtained. The 

model, known as IDSAM, was reported to yield strength estimates, which correlated to actual strength at a level higher 

than most other contemporary and previous studies.

Acoustic Emission/Acousto-Ultrasonics

In acoustic emission (AE), stress waves caused by a source 

within a structure are monitored. Internal sources of stress 

waves include, but are not limited to, microfractures, check 

formation, water movement, and pit aspiration. Several 

parameters are extracted from recorded waves, and the 

extracted parameters are then correlated to different phenomenon or properties inherent to the structure. Any number 

of parameters can be examined, including but not limited to, 

time of flight, peak voltage, time to peak voltage, stresswave factor, root mean square voltage, number of threshold 

crosses, signal length, time centroid, frequency centroid, 

attenuation, etc. Acousto-ultrasonics (AU) uses many of the 

same post-test analysis techniques; however, the source is 

user-dictated such as a transducer.

The fundamentals of AE and AU were described in Beall 

(1987a). AE was not repeatable; AU was repeatable. AU 

relied on averaging several signals to minimize variability, 

and narrow band filters were used to improve signal-tonoise ratio. Attenuation of signals traveling through wood 

increased as frequency increased. Issues of coupling to 

wood-based material such as porosity and surface roughness 

were discussed. Pressure and couplant together minimized 

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

27

signal loss. A limited glossary of terms was included (Beall 

1987a). In a follow-up paper, the future uses of AE/AU were 

discussed. Potential areas of use were laboratory testing, 

field testing, proof testing, online sensing, sensing adhesive 

integrity, sensing creep, fracture analysis, and drying defects 

(Beall 1987b). 

Sandoz and others (2000) used AU variables in conjunction 

with stress-wave information to increase the reliability of 

strength grading. Parameters examinded included stresswave factor, maximum peak, energy, and attenuation after 

first peak. MOE of glued-laminated beams was highly correlated to ultrasonic wave speed, whereas local defects had a 

significant effect upon MOR. 

Kánnár (2000) examined the influence of moisture and 

temperature upon the Kaiser effect in Scots pine. The Kaiser 

effect is a phenomenon in which acoustic emissions are 

absent in a material for a load at or beneath previous loading 

levels. The Kaiser effect was observed in short-term experiments; however, the effect diminished as time elapsed. The 

Kaiser effect was observed 95% of the time if a second load 

was applied immediately, 60% if applied within 15 days, 

and 25% after two months.

Ballarin and others (2002) explored parameters that could 

be correlated to the physio-mechanical properties of clear 

wood. Douglas-fir, redwood, and spruce–pine–fir specimens 

were used. Time domain parameters exhibited more differences than frequency domain parameters. 

In a follow-up study, Seeling (2002) examined additional 

parameters. The parameters included time of flight, maximum and minimum voltage, time to voltage maximum 

and minimum, root mean square voltage, total power, time 

and frequency centroid, stress-wave factor, and maximum 

frequency for the first quarter of the wave. Most of the AU 

parameters examined showed weak correlation to wood 

characteristics.

Moisture Content and Drying

Wood physical and mechanical properties are affected by 

MC of wood. Modulus of elasticity, wave speed, attenuation, and creep characteristic are just a few of the properties 

of wood affected by MC. Wave behavior through medium 

is dictated by properties of material. As properties change, 

wave propagation alters. Several researchers have explored 

how wave propagation changes as a function of MC with 

the goal of assessing and monitoring wood MC level.

Quarles and Zhou (1987) monitored the development of 

defects during drying using AE. The rate of acoustic emission events was correlated to drying conditions of California 

black oak (Quercus kelloggii) In addition, defects created 

during drying were related to the AE signals. 

Groom (1991) examined the integrity of the truss-plate joint 

during moisture cycling using acousto-ultrasonics (AU). 

Signal energy was strongly correlated to the number of teeth 

of the plate and quality of the coupling between the teeth 

and the wood but was poorly correlated to the total coupling 

surface area. Presence of high frequencies in signals passing 

across the truss plate to the wood was indicative of sound 

joints. Weakened joints exhibited stronger low-frequency 

components. 

Kawamoto (1993) used several AE parameters to predict the 

location of check formation during drying of Japanese red 

pine (Pinus densiflora). Low frequencies were associated 

with check growth. The number of checks in the location of 

the transducer was correlated to the AE count rate. Radial 

attenuation was an indicator of large check formation during 

drying. In a follow-up study, Kawamoto (1996) concluded 

that using only AE parameters for the detection of checking 

was imprecise. 

Booker and others (1996) examined radiata pine (P. radiata) 

for variation in both sound velocity and dynamic MOE 

in three principle directions with changing MC. Velocity 

increased linearly as the MC decreased from fiber saturation 

point (FSP) to oven dry. The dynamic MOE had the lowest 

value at FSP, increasing both above and below. Below FSP, 

approximately 60% of the variation in MOE was attributed 

to wood shrinkage. 

Schafer and others (1998) identified wetwood and honeycombing defects using TOF measurements. They concluded 

that wetwood increased viscoelastic damping in the wood 

and that honeycombing caused scattering of waves. Despite 

having different mechanisms, both types of defects decreased signal amplitude across the wood. 

Booker and others (2000a) examined the timeframe of internal check development of radiata pine using AE. Internal 

checking occurred within the first 6 hours of drying. The 

maximum rate of checking occurred between 1.6 and 2 

hours after drying began. This timeframe corresponded to 

within a half hour of the kiln reaching operating temperature. The number of AE events did not correlate to the 

number of internal checks.

Soma and others (2000) examined the effects of frozen 

moisture upon wave velocity in frozen cubes of Japanese 

cedar (Cryptomeria japonica). Wave velocity through the 

cubes was recorded in all three principle directions: longitudinal, radial, and tangential. Wave velocity through the 

frozen cube increased. The increase was most noticeable 

in the longitudinal, radial, and tangential directions for MC 

above 200%, 150%, and 150%, respectively. 

Kang and Booker (2002) examined moisture gradient within 

radiata pine during kiln drying. The boards were weighed 

and the wave velocity was measured during the kiln-drying 

process. The velocity appeared to be both a function of MC 

and moisture gradient. 

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

28

Miettinen and others (2005) made electrical and ultrasonic 

measurements of pine specimens. Measurements were correlated to MC, density, growth ring angle, hardness, and 

strength. A multivariable model accounted for a greater 

amount of the variability of parametric estimators such as 

density and MC. 

A 2005 study by Rosner and Wimmer used acoustic emissions to monitor spruce trees during drying. The trees were 

debarked and dried. Mature trees were more susceptible to 

dehydration than juvenile wood. Juvenile wood had more 

AE events because of a higher number of tracheids per volume. High energy AE events were measured at the start of 

the drying process and were caused by the shrinking 

process. 

Gonçalves and Costa (2005) performed a combined examination of ultrasonic wave velocity and stiffness on 

several Brazilian tree species. Stiffness terms decreased 

with increasing MC up to the FSP. Above the FSP, stiffness 

would sometimes increase with increasing MC. Stiffness 

values were partially corrected by accounting for mobility 

of free water in the wood. The MC had a greater effect upon 

wave velocity in the longitudinal direction than in the radial 

or tangential directions. Correlations were positive, but key 

supporting figures were absent. 

In a later study (Rosner 2011), explored reversible and irreversible effects of drying upon spruce were explored using 

acoustic emission. Specimens were dried and then rewetted 

in successive cycles. During the first drying, the highest 

peak amplitudes were associated with moderate moisture 

loss. After rewetting, the highest peak amplitudes occurred 

near the end of the drying cycle. Wood that had never been 

dried had a higher rate of AE events below 175 kHz. Earlywood appeared more sensitive to irreversible changes during 

drying than latewood. 

In a 2011 study, van der Beek and Tiitta (2011) monitored 

the drying process of spruce using AE. One goal of the 

research was to discriminate between AE events caused by 

crack formation and propagation and those caused by water 

movement. The cool down and moisture conditioning 

phases of drying were most critical in crack formation. 

Gao and others (2011) examined the combined effects of 

temperature and MC on specimens of red pine. Specimens 

were conditioned to four different MC levels: green, 24%, 

12%, and 0%. Wave velocity and energy loss through the 

specimens was measured over a range of temperatures from 

–45 °C to 35 °C. The MC had a significant effect upon the 

relationship between velocity and temperature. When green 

wood was well above freezing, temperature had little effect 

upon wave velocity. When green wood was near freezing, 

energy loss and wave velocity changed abruptly because of 

the phase change of water held in the cell lumens. As MC 

decreased, change near freezing became less pronounced. 

In subsequent research from many of the same authors, the 

combined effects of temperature and MC upon modulus of 

elasticity was examined. Dynamic and static bending tests 

were performed upon specimens of red pine. Similar to 

velocity and energy loss, green wood had abrupt changes 

in MOE near freezing. As MC decreased, the change near 

freezing became less pronounced (Gao and others 2013). 

In a similar study conducted by Llana and others (2013), the 

combined effects of temperature and MC upon MOE and 

ultrasonic velocity were examined on Scots pine. Between 

10% and 18% MC, the effects of MC were close to linear. 

Trends above and below 0 °C were different from each 

other.

Dunbar and others (2013) used ultrasound to predict 

dimensional stability of sweet chestnut (Castanea sativa) 

and sessile oak (Quercus petraea). Wood samples were 

below the FSP. Wave velocity in the woods decreased as 

MC increased. Specific gravity alone was a poor predictor 

of wood shrinkage; ultrasonic velocity alone was a good 

predictor. The combination of both parameters produced the 

best predictor, explaining 72% and 77% of shrinkage in oak 

and chestnut, respectively.

Determination of Wood Elastic Constants 

and Properties

Ultrasound has been used in a variety of ways to quantify 

elastic constants of wood. The relationship between stresswave velocity, density, and stiffness make it a powerful 

experimental tool.

An early property assessment study used vibrations and high 

frequency waves to explore the theoretical relationship between wave velocity, density, and stiffness (Hearmon 1965). 

Bucur (1978) correlated changes in velocity to stress levels 

in wood. Stress was normalized by the rupture stress. 

Normalized values were between zero and one. Four stress 

levels were identified and correlated to physical phenomenon: 0.2, 0.2 to 0.7, 0.7 to 0.9, and 0.9 and above. In the 

first zone, velocity values increased. In the second zone, 

velocity was close to constant. The third and fourth were 

both characterized by a decreasing slope, but the slopes are 

different from each other. 

Ross and Pellerin (1988) reported on use of acoustic techniques for assessing mechanical properties of a wide range 

of wood composite materials. They obtained excellent 

correlative relationships between acoustic parameters and 

mechanical properties. 

Bucur (1996) discussed methods by which three different 

wave techniques were used to obtain material constants. 

Vibratory, acoustic, and ultrasound techniques were described, and an explanation of the theoretical foundation of 

the relating wave velocity and elastic constants was given. 

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

29

The complex modulus of anisotropic materials, such as 

wood, was derived by Navi (1996). Navi noted that the 

inhomogeneity and viscosity of wood causes both dispersion and attenuation in waves traveling through the material. 

A dispersion relationship was developed by measuring the 

phase velocity by frequency. Attenuation was measured by 

frequency as well. Together, these properties were used to 

develop the complex modulus for anisotropic materials. 

de Oliveira and others (2002) measured static and dynamic 

MOE of loblolly pine. Like many similar tests, dynamic 

MOE was approximately 20% higher than the static, and 

good correlation existed between the two MOE values.

Bucur and Berndt (2002) calculated nine elastic constants of 

an orthotropic material, which wood is often considered to 

be, using wave velocity and energy flux deviation. Six of the 

nine constants were calculated using longitudinal and shear 

wave velocity measurements. The six terms were along the 

diagonal of the stiffness matrix; the three remaining terms 

were off of the diagonal. Accuracy of the calculated value of 

three off-diagonal terms was greatly increased if considerations were made for energy flux. 

Karlinasari and others (2009) calculated the MOE of two 

tropical species, mangium (Acacia mangium) and jackfruit 

(Artocarpus heterophyllus) using three different methods: 

dynamic method using ultrasonic velocity, static bending, and a mechanical grader. Each MOE was correlated to 

MOR. The best correlation existed between static MOE and 

MOR, with dynamic MOE second best; however, overall, 

the correlation between dynamic MOE and MOR was low. 

Baradit and Niemz (2011) investigated mechanical properties of four Chilean species of wood using ultrasonic measurements. Longitudinal and transverse waves were used to 

determine MOE in three primary directions as well as the 

three shear moduli. Four hardwoods species: tepa, olivillo, 

laurel, and lenga, and two softwood species, alerce and 

manio, were tested. Hardwoods tested were found to have 

higher anisotropy than softwoods. 

Grimberg and others (2011) determined the elastic tensors 

of wood plates using the phase velocity of lamb waves. Both 

symmetric and antisymmetric modes were employed in the 

calculation process. Good agreement was found between 

calculated values and the destructive tests. Wood plates 

were made of sycamore, alder, cherry, beech, and pine. 

Air-coupled transducers were employed to determine 

several elastic constants. 

Wood specimens of three distinct shapes were tested ultrasonically by Gonçalves and others (2011) to obtain elastic 

constant data. The shapes were a 26-sided polyhedron, a 

multifaceted disk, and cubic prisms. Three species were 

tested: eucalipto, garapeira, and cupiúba. Cubic prisms were 

cut at specific angles to the wood grain. The prisms produced the best correlation to elastic constant values obtained 

from static test; however, prism specimens themselves were 

used for static tests after ultrasonic testing was concluded. 

The multifaceted disc and the polyhedron exhibited the 

second and third highest correlation to the static tests, 

respectively. Poisson’s ratios proved harder to correlate with 

static results with some matching across all shapes and some 

diverging for all shapes. 

In two related studies, Inés and others (2011) and Palacios 

and others (2011) used 30 wood species to develop correlations between MOE static bending, MOE from resonant 

frequency, MOE from ultrasound velocity, MOR, and specimen density. The high number of species represented made 

this study unique. Specimens were grouped according to 

density. Strong agreement existed between the MOE from 

ultrasonic velocity and MOR.

In a follow-up study to Gonçalves and others (2011), 

Vázquez and others (2013) extracted samples from 13 specimens of Castanea sativa and shaped them into 26-faced 

polyhedrons. Ultrasonic time of flight measurements for 

longitudinal and shear waves were used to estimate elastic 

constants of the polyhedron. Prisms were extracted from 

the specimens and used in compression tests to obtain the 

same elastic constants. No significant difference was found 

between the elastic constant values obtained using the two 

methods. 

The acoustoelasticity of three wood species, Eucalyptus 

citriodora, E. grandis, and E. pellita, was explored by 

Bertoldo and others (2013). Specimens were subjected to 

bending tests and strain was measured using extensometers. Wave speeds longitudinal and transverse to the 

wood grain were measured. Longitudinal wave velocity 

tended to decrease as strain increased regardless of whether 

the strain was compressive or tensile in nature. Longitudinal 

wave speed was more affected by presence of strain than 

transverse traveling waves. Waves traveling in the radial or 

tangential direction during the bending test exhibited differing behaviors depending upon whether the specimen was a 

hardwood or softwood. Velocity increased with increasing 

strain for hardwoods and decreased for increasing strain for 

softwoods.

Inspection and Assessment

The purpose of ultrasonic inspections is to assess the 

condition of a member or structure. The condition assessment allows the inspector to decide whether or not the 

current condition is sufficient for the member or structure 

to perform its function safely and reliably. Internal defects 

can have a significant and deleterious effect on member 

strength. Therefore, a reliable assessment can be made if 

the inspection process can detect defects. The studies in this 

section focus upon defect detection and strength assessment 

using ultrasonic inspection. The literature review begins 

with studies involving inspection of wood in its least refined 

state, standing trees. Subsequent sections will focus upon inspection methods on increasingly processed wood products: 

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

30

round members, sawn lumber, and engineered wood products. Inspection of structures and artifacts follows.

Standing Trees

Modern ultrasonic equipment is capable of generating sufficient signal energy to perform inspection and assessment of 

standing trees and poles, and its use in that area has noticeably increased in the last decade. Historically, ultrasonic 

inspection of standing trees was not as common as acoustic 

stress-wave inspection. Acoustic stress waves were more 

commonly used to inspect standing trees and round members such as logs, poles, and piles. Acoustic stress waves 

have frequencies below 20 kHz and are often caused by an 

external impact. Impacts from hammers, pendulums, and 

BBs have greater signal energy than that produced using 

piezoelectric transducers. Also, low frequencies attenuate more slowly than high frequencies and can therefore 

travel farther in wood. As a result, signals from impacts had 

a greater probability of being sensed across the diameter 

of full-sized trees. Acoustic stress-wave inspection is not 

covered in this paper. With technological improvements, use 

of ultrasonic inspection of standing trees is becoming more 

widespread.

Huang (2000) used ultrasonic wave propagation through the 

outer wood of standing trees to predict lumber stiffness of 

loblolly pine. Wave velocity measurements were taken radially at 50 and 150 cm above the ground on standing trees. 

Speeds were correlated to modulus of elasticity (MOE) in an 

effort to determine the quality of stands of trees prior to harvesting. Huang (2000) found that trees with large percentages of corewood had higher radial wave velocity. 

In a 2009 study, Najafi and others inspected a group of 

112 Iranian birch trees for internal decay. Wave velocity 

was measured parallel and perpendicular to the slope of the 

grain. Trees were felled and cross sections were compared 

to inspection results. The study found that of the trees containing defect, 95% were detectable. When defects did occur 

in birch trees, they were usually around the pith. Because 

of the mountainous nature of the region in which the study 

took place, many trees contained reaction wood, causing eccentricities in the cross sections. Eccentricities could cause 

velocity measurements taken perpendicular to the grain to 

miss the decayed pith regions. 

Pedroso and others (2011) measured the longitudinal ultrasonic velocity of a group of 210 Brazilian trees including 

the species Eucalyptus grandis, Pinus elliottii, and Toona 

ciliata. The measurements were taken vertically along the 

trunk above and below breast height. The trees were felled, 

cut into logs, and ultrasonic velocity through the logs was 

measured. Whereas it was possible to correlate velocity 

values between logs and trees, a nonlinear relationship 

existed because of differences in the manner in which waves 

traveled through each.

In a 2011 study, Brancheriau and others (2011) constructed 

an automatic ultrasonic inspection system mounted on a 

track around a standing tree. Initially, a 300-kHz transducer 

was used as a source, but attenuation through the tree was 

too great. An 80-kHz transducer was then used. Resolution 

provided by the lower frequency transducer was 17.7 mm 

longitudinally and 36.4 mm lateral to the tree. Radial wave 

velocity measurements were used to construct a tomographic view of an eastern cottonwood (Populus deltoides).

The effect of pruning upon a variety of loblolly pine was 

examined in eastern Argentina. Fassola and others (2011) 

found that trees without pruning had higher wave speeds. 

The trend continued when trees were cut into logs. 

Several tree species in Bogor City, Indonesia, were examined (Karlinasari and others 2011). The focus of the study 

was evaluating trees in an urban environment. Tree species 

included Swietenia sp., Pterocarpus indicus, Bauhinia purpurea, Mimusops elengi, and Agathis alba. Trees were selected for radial ultrasonic testing based upon visual inspection. Sound, decayed, and questionable trees were selected. 

The velocity values were compared with results of the visual 

inspections, and a tree soundness criterion was developed 

based upon ultrasonic velocity. Trees with velocities above 

1600 m/s were assumed to be good; decayed trees had 

velocities less than 500 m/s. 

Song and others (2011) correlated radial ultrasonic and 

acoustic stress-wave velocities for the tree species Populus simonii, Ulmus pumila, Salix matsudana Koidz, and 

Fraxinus mandshurica. Good agreement was found between 

the two measurement methods in standing trees, although 

ultrasonic velocities were higher than acoustic velocities. 

In a 2011 study by Yoza and Mallque, tangential and radial 

measurements were taken on Cedrelinga cateniformis

Ducke. Elastic constants were determined using two ultrasonic frequencies, 23 kHz and 45 kHz. 

Karlinasari and others (2013) used ultrasound inspection 

to determine if a commercially important fungus, known 

as agarwood, was present in standing Aquilaria microcarpa trees. Karlinasari and others (2013) found that radial 

ultrasound velocities less than 1000 m/s were indicative of 

agarwood. 

Ultrasonic cross-sectional tomographs formed from radial 

and tangential wave velocity measurements were found in a 

2013 study by Lino and others to be sensitive to the degree 

of irregularity (non-round) trees. Wave paths in irregularly 

shaped trees were complex and difficult to determine. Exterior digital tomographic profiles of the trees were obtained 

using triangulated laser measurements. The digital profiles 

were used to supplement ultrasonic measurements to make a 

more accurate ultrasonic tomograph.

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

31

Round Members

Round members include logs, poles, and piles. In round 

members, the orthotropic structure of the original tree 

remains largely intact. Historically, acoustic stress-wave 

inspection was generally favored over ultrasonic inspection 

for round members. The size of round members was such 

that the greater power and lower attenuation of acoustic 

stress-wave inspection yielded more easily observed signals. 

An example of the differences between acoustic stress-wave 

and ultrasonic inspection can be found in Booker and others 

(2000b). In that study, radiata pine logs were examined with 

several ultrasonic and acoustic measuring devices. This 

study found that ultrasonic devices were unsuitable because 

of the low power of their signals. The final evaluations were 

made with the acoustic devices only. Modern ultrasonic 

equipment produces greater signal power than those of past 

devices. Advancements in technology have overcome 

previous limitations, and use of ultrasonic techniques in 

inspection and assessment of round members, like standing 

trees, has increased in the last decade.

Strength and Grading

Log grading and strength characterization are important to 

the commercial value of logs. The ability to grade wood 

while it is still a standing tree or a log aids in obtaining the 

highest quality yields from each piece of wood and minimizes waste. Several papers have been written about log 

grading and characterization. Sandoz (1996) developed a 

transducer that was driven through the bark to the wood, 

eliminating the need for a couplant. Longitudinal wave 

speeds of logs were used for log grading and estimating performance of lumber made from the logs. 

In a 1996 study, Curtu and others evaluated Romanian 

beech trees at three different levels: bottom, middle, and top. 

This study found variations in the wave speeds depending 

upon stress in the wood. Wave speeds decreased from base 

to canopy. In addition, the wave speed of logs cut from the 

trees was different than the wave speed of lumber cut from 

the logs. 

Hauffe and Mahler (2000) compared the capability of two 

inspection methods for estimating log strength. They used 

x-ray inspection and ultrasound to evaluate 225 spruce 

logs. Ultrasound was found to be superior to x-rays in log 

strength estimation. 

In a 2005 study, Yin and others estimated MOE of Chinese 

fir logs using ultrasound, stress wave, and vibration methods. Correlations were drawn between static MOE and 

modulus of rupture (MOR). The MOE based upon vibration methods had the best correlation. Between the top and 

bottom of the trees, the variation of MOE was significant, 

whereas the variation of MOR was insignificant. 

In a related study, Gonçalves and others (2009) evaluated

Eucalyptus citriodora poles using both ultrasonic and static 

tests. The static tests were carried out in accordance with 

ASTM D1036/1999 and NBR 6231/1980 specifications. The 

research goal was to evaluate the variability between new 

poles and suppliers and establish a nondestructive method of 

grading. The researchers found that ultrasound assessment 

allowed poles to be sorted by strength and suppliers to be 

sorted by quality. 

Turpening (2011) constructed high frequency ultrasonic 

tomographs using densely spaced transducers positioned 

around a log. The images produced showed a high correlation with sandalwood oil, an important commercial derivative of sandalwood trees. 

In 2013, Freitas and others evaluated deteriorated Brazilian 

utility poles for reuse in other capacities after they were removed from use. Eucalyptus citriodora and E. saligna poles 

were inspected using ultrasound. The research showed ultrasonic inspection can be used to determine which poles can 

be reused and which ones must be discarded.

Defect Detection

Abbott and Elcock (1987) inspected poles in Europe using 

a 40-kHz transducer. Signal attenuation was examined as an 

indicator of internal rot. The proposed procedure required 

multiple tests around the circumference of the pole. 

A more recent study by Han and Birkeland (1991) used 

three ultrasonic techniques to evaluate logs: pulse echo, 

through-transmission, and grain sounding trace. Correlations 

were drawn between test results, and an attempt was made 

to use artificial intelligence to improve automated defect 

detection.

Lemaster and others (1993) examined the sensitivity of 

acousto-ultrasonic parameters in detecting holes in Douglasfir utility poles. Three pole conditions were studied: lightly 

checked, heavily checked, and creosote treated. Several 

parameters were pulled from the signal data to evaluate 

sensitivity. Parameters that showed sensitivity to the presence of holes included transit time, centroid time, centroid 

frequency, and velocity. The technique detected holes 

50 mm or larger in a 300-mm-diameter pole.

A group of six sensors was employed by Dill-Langer and 

others (2002) along with acoustic emission techniques to 

identify and locate defects within a European spruce (Picea 

abies) wooden pole. Approximately 90% of the source locations were identified to an accuracy of ±7% of the transverse 

dimension and ±4.4% of the longitudinal dimension.

Divos and Divos (2005) explored the resolution of acoustic tomography to determine the dimension of the smallest 

detectable defect. Artificial defects were created within 

discs of larch. Defect sizes ranged from 10 mm to 100 

mm. The number of sensors used to construct a tomograph 

of the cross section was varied from 6 to 30. The 10-mm 

defect was not detected using 30 sensors; a 25-mm defect 

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

32

was detected. In another study of detecting voids in cross 

sections, holes of different shapes, circular versus slots, 

were manually created in large wood discs. Wave velocity 

decreased linearly with the size of the hole, but slots had a 

greater adverse effect.

In the Najafi and others (2007) study on beech trees, the 

location of the hole had no influence on the wave velocity. 

The potential to detect the presence of tension wood using 

ultrasonic inspection was confirmed.

Yang and others (2007) measured the radial and longitudinal 

wave velocities in Eucalyptus globulus discs. Longitudinal 

velocity was found to be higher in areas of tension wood, 

but radial velocity was lower.

A portable ultrasonic CT device was developed by Kim and 

others (2007) to detect decay in wood poles. The devices 

created tomographs that were capable of detecting decay 

regions with less than 10% mass loss and less than 30% loss 

in strength.

Gonçalves and others (2011) examined the presence of 

artificial holes in wood cross sections composed of Pequía 

(Aspidosperma desmanthum). Tomographic images were 

created, but the image shapes of the holes were distorted. 

In another void analysis, Wang and others (2013) constructed wave time of flight isolines along the surface of a Korean 

birch (Betula costata) log. Cavities of varying diameters 

were created within the log. The study examined how waves 

traveled through the cross section. The accuracy of the 

simulated reconstructions was able to identify the smallest defect, 40 mm, with an 83% accuracy. The accuracy 

improved as the hole size increased.

Oh and others (2013) used signal attenuation to detect small 

holes in round red pine members. The holes were used to 

simulate insect damage to the poles. A through transmission 

setup was used to collect data with varying levels of contact 

pressure on the transducers. A spectral analysis was carried 

out on the data. The results indicate that spectral analysis 

had great potential in locating insect damage. 

Sawn Lumber

Sawn lumber has been cut from trees into rectangular cross 

sections. The dimensions of the cross section are typically 

smaller than those of round members and standing trees. 

The sawn lumber review is broken into two subsections, 

strength and grading and defect detection.

Strength and Grading

Like log grading, grading sawn lumber is important for its 

commercial value. Ultrasonic inspection has been used in a 

variety of applications to grade sawn lumber. Joint assessment has been included in this section as it directly relates 

to strength of the final member. In an effort to improve grading beyond what could be assessed visually, Sandoz (1991) 

measured ultrasonic wave velocity in spruce and fir beams. 

The ultrasonic longitudinal wave speed was correlated to the 

values of MOR and MOE. Moisture content and temperature were taken into account in the calculations. 

Anthony and Phillips (1993) conducted a study in which the 

original finger joint paper was expanded with an examination of the sensors and the sensor coupling to the board. 

The signal from air-coupled transducers was found to lack 

the power necessary for use in tests. The signal from wheel 

sensors varied as the wheel turned, causing unacceptable 

inconsistencies. Coupling materials were limited to those 

that could be used in a manufacturing environment. Silicone 

rubber sheets and moldable urethane were examined and 

their relative advantages and disadvantages were discussed. 

Sensor placement was moved from the narrow edge of the 

boards to the wide face. 

In a 2000 study of timbers, Duju and others applied six 

methods of measuring MOE to five different timber species 

of Malaysian wood: Parashorea macrophlla, Gonystylus bancanus, Shorea albida, Dipterocarpus rigidus, and 

Cotylelobium burcki. One MOE measurement method relied 

upon ultrasonic wave velocity. Good correlation was found 

between the calculated MOE and the values of MOR. 

Another study (Diebold and others 2000) examined several 

methods of grading for hardwood and softwood. Softwoods 

examined included spruce, pine, larch, and Douglas-fir. 

Hardwoods included oak and beech. Testing methods 

included bending tests, x-ray, transverse vibration, and ultrasonic inspection. Of the methods used, ultrasonic inspection 

had the lowest correlation to bending strength. 

In a similar study, visual grading and MOE estimated from 

the ultrasonic wave velocity were correlated to the bending 

strength. Kuklik and Kuklikova (2000) found that in the 

majority of cases, knottiness of the board was the decisive 

visual grading criteria. 

Sasaki and Hasegawa (2000) examined changes in shear 

velocity with applied stress in Japanese magnolia (Magnolia 

obovata Thunb.). Compressive loading caused a reduction in shear velocity; tensile loads caused increases. The 

changes in shear velocity were less than 1%. 

Gonçalves and Bartholomeu (2002) examined MOR and 

MOE for boards harvested at different heights within a 

Cupiúba (Goupia glabra) tree. MOR and MOE were highest 

in boards harvested near the base of the tree. The MOR and 

MOE were found to be weakly correlated; however, the 

MOE was found to be capable of reliably evaluating rigidity 

properties. 

In 2002, Machado created a profile of bending strength using boards of maritime pine. Ultrasonic signals were applied 

to the boards perpendicular to the grain. Acousto-ultrasonic 

parameters were extracted from the signals and used to 

estimate modulus of elasticity through correlated relationships established by the author in a previous work. Bending 

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

33

tests were then performed along the length of the same 

boards. The results of the bending tests were correlated to 

modulus of elasticity values estimated using the acoustoultrasonic parameters. The results showed that a lengthwise 

bending strength profile could be created using acoustoultrasonic parameters. 

While using timber grading, Sandoz and Benoit (2002) developed a relationship between the pair of parameters wave 

speed and transmitted energy and the properties of MOE 

and MOR. Empirical equations expressing the relationships 

were given. Energy damping of the wave was found to be 

directly dependent upon local singularities such as knots, 

decay, and grain angle. 

Plinke (2005) proposed a finger joint inspection method 

in which high powered ultrasonic waves would generate 

friction at the joint and could be observed using infrared 

cameras. The method would be effective at defect detection, 

but not strength estimation.

Terezo and others (2005) tested two different species of 

Brazilian woods, Peroba (Aspidosperma pirycollum) and 

Angelim (Hymenolobium petraeum), using two commercially available ultrasonic tools to obtain estimates of the wave 

velocity. The correlation between MOE and wave velocity 

was lower than for most similar studies. Conversely, the correlation between wave velocity and MOR was higher than 

most similar studies. 

Bartholomeu and Gonçalves (2007) examined the correlation between ultrasonic wave speed and MOE obtained from 

static bending. The six species were Eucalyptus citriodora, 

E. grandis, E. saligna, Cupiuba (Goupia glabra), Angelim 

araroba (Votaireopsis araroba), and Pinus elliottii. High 

correlation was found. 

Iñiguez and others (2007) related changes in ultrasonic 

wave velocity to specimen length. The studies were conducted on Scots pine. For each meter of wood traveled, the 

wave velocity decreased approximately 83 m/s. The test was 

conducted to propose velocity adjustments during timber 

assessment. 

Karlinasari and others (2007) examined the correlation 

between the dynamic modulus determined from ultrasound 

and the static modulus for Jeunjing (Paraserianthes 

falcataria).

Mechanical properties of three European species, radiata 

pine (P. radiata D. Don.), Scots pine (P. sylvestris L.), and 

Laricio pine (P. nigra Am. Ssp. salzmannii), were evaluated 

by Iñiguez and others (2009) using ultrasonic wave velocity 

perpendicular and parallel to the grain of the wood. Empirical relationships were constructed relating the wave speed to 

MOE and MOR; however, correlation values were low for 

ultrasonic values. 

Yin and others (2009) investigated the feasibility of using ultrasonic wave velocities to estimate compressive and 

tensile strength of structural lumber composed of Larch 

(Larix gmelini). The author determines that lumber strength 

can be estimated from ultrasonic wave velocity, but correlation between the values is low. 

Massak and others (2009) examined the influence of tree 

age on wave velocity and MOE. Beams were cut from 

P. elliottii trees of various ages ranging from 8 to 23 years. 

Both longitudinal velocity and MOE increased with age 

up to approximately 20 years. There were two groups of 

variation based upon age, 13 years and below, and 15 years 

and above. Rigidity properties would increase with age, but 

eventually become constant. 

In a 2011 study, Pires and others estimated the MOE of eight 

Brazilian species using stress wave, transverse vibration, 

and ultrasound and then correlated to MOE determined from 

static bending tests. The species were Pouteria guianensis, 

P. pachycharpa, Holopyxidium jarana, Vatairea sericea, 

Chrysophyllum venezuelanense, Astronium lecontei, 

Endopleura uchi, and Lecythis pisonis. Ultrasound and 

stress-wave inspection were similarly correlated with 

respect to MOE. None of the testing methods correlated to 

density. 

Rohanová and others (2011) studied Slovakian spruce 

(Picea abies) and found that ultrasonic-based strength 

estimates for this species were higher than those estimated 

using the European standard EN 408. 

Iñiguez-Gonzalez and others (2013) presented preliminary work for use of nondestructive techniques to evaluate 

properties of sawn lumber. The goal was to establish common procedures to be used. The species examined included 

several Spanish species: Scots pine, laricio pine, radiata 

pine, maritime pine (Pinus pinaster Ait.) and sweet chestnut 

(Castanea sativa Mill.). 

A 2013 study by Ferreira and others investigated the 

adequacy of Brazilian standards for grading wood using ultrasonic inspection by testing the predicted compressive and 

tensile stiffness and loading on Eucalyptus grandis boards. 

The results largely supported the standards on compressive 

loading and stiffness. The study raised concerns about tensile loading and stiffness as outlined by the standard. 

Hermoso and others (2013) examined a combined visual 

grading and ultrasonic grading technique using 116 Scots 

pine boards. Boards were accepted or rejected using three 

methods, visual grading, ultrasonic grading, and a combined 

method. Boards accepted using ultrasonic inspection had a 

higher modulus of elasticity and higher modulus of rupture 

than the rejected boards. The boards accepted using visual 

grading had higher MOE than the rejected boards, but the 

rejected boards had a higher modulus of rupture. Also, the 

mean MOE of the visually graded boards was higher than 

that of the ultrasonically graded boards. The combined 

visual–ultrasonic grading increased the MOE of the 

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

34

accepted boards over the ultrasonic method alone by 0.5%. 

and decreased the modulus of rupture by 1.3%. 

Another study (van Dijk and others 2013) examined the 

degree of mode conversion of ultrasonic waves traveling 

through wood. A 7-m beam composed of Cabreúva (Myrocarpus frondosus) was used as the medium to observe 

traveling waves. Three frequencies were used for the tests: 

25 kHz, 45 kHz, and 80 kHz. The receiving transducer was 

placed every 100 mm along the length of beam with its face 

parallel to the longitudinal axis. Two source transducer orientations were explored, direct, with the transducer directing 

wave energy along the longitudinal axis, and indirect, with 

the transducer directing wave energy perpendicular to the 

longitudinal axis. When the measurements were taken at a 

distance greater than five wavelengths, the velocities from 

both types of measurements were equivalent for all transducers. The degree of dispersion increased as the transducer 

frequency increased.

Defect Detection

In one early paper (Lee 1965), the possibility of using 

ultrasonic inspection as a safety measure was explored. 

Differences in wave speeds as a function of the wave angle 

to the grain orientation were examined. The possibility of 

detecting delamination as a function of amplitude and wave 

velocity was discussed. Energy reflection based upon the 

size of the reflecting defect was calculated. 

An early paper in ultrasonic inspection (McDonald 1978) 

put a board in a fluid tank. McDonald sent signals through 

the specimen and recorded the time of flight (TOF). A 

good correlation between the TOF and defect location was 

observed. Also, a strong relationship between grain direction 

and the velocity of sound was noted. 

Pellerin and others (1985) were the first to report on a 

systematic examination of the effect of biological attack on 

the acoustic properties of clear wood. They used small, clear 

Southern Pine specimens in a laboratory study designed to 

examine the effect of brown-rot decay fungi and termite attack on acoustic velocity and static strength. Time-of-flight 

measurements, parallel to the fiber axis, were made using 

a through transmission system on specimens after various 

exposure times. The researchers observed a considerable 

change in acoustic time of flight with exposure time. More 

importantly, they were able to make several conclusions. 

Changes in time of flight occurred well before measureable 

loss of either weight loss (density) or strength was observed. 

A significant correlation was observed between residual 

strength and acoustic time of flight. Time of flight measurements parallel to the fiber axis were not useful for monitoring changes in corresponding strength for termite attack 

because of their preferential consumption of the early wood 

sections of the specimens. 

Hamm and Lam (1987) used transit times of waves traveling 

longitudinally as a metric for locating compression wood 

in western hemlock. Moisture content, grain angle, thickness, knots, and wane complicated the transit time measurement. Transit time uniformity along the board was used to 

eliminate the effects of knots and wanes. Moisture content 

and thickness did not completely mask the presence of compression wood. Grain angle was a factor that still affected 

compression wood identification. 

In a 1987 study, Patton-Mallory and others used acoustoultrasonics to determine the presence of brown rot in Southern Pine. Coupling the sensors to the wood proved difficult. 

Hot melt adhesives produced the most repeatable results. 

Time centroid and peak time were the most repeatable 

waveform parameters. 

Hamm and Lum (1991) explored the feasibility of using ultrasound as an online tool to identify compression wood. A 

slope of grain indicator was also investigated using western 

hemlock, but its accuracy was insufficient for compression 

wood identification. 

DeGroot and others (1994, 1995, 1998) reported both energy storage and loss parameters for monitoring the deterioration of clear wood when exposed to natural populations of 

decay fungi and subterranean termites. 

Ross and others (1994) used a pulse echo test setup to 

measure speed of sound transmission and wave attenuation, 

parallel to the fiber axis, in small clear Southern Pine specimens in field exposure conditions. 

In studies in 1996 and 1997, Ross and others also developed empirical models that used both parameters capable of 

predicting residual compressive strength with a high level of 

accuracy. 

Ross and DeGroot (1998) reported on a test setup they 

developed to monitor deterioration in full-size lumber 

specimens that were exposed, above ground, to naturally 

occurring decay fungi. The setup was based on a through 

transmission concept, with rolling ultrasonic transducers 

used to transmit and receive the pulse, perpendicular to the 

fiber axis. Time of flight was measured with a commercially 

available timing unit. The specimens used in this study were 

completely free of natural defects. Consequently, changes in 

acoustic parameters after exposure were solely attributed to 

deterioration resulting from decay fungi. 

Niemz and Kucera (1998) examined the ability of ultrasound to detect defects using small holes drilled into Norway spruce specimens. Holes were varied in both size and 

orientation. Small defects were difficult to discern because 

of wood variability. The influence frequency upon of wave 

speed was also studied. This study modeled velocity of 

waves traveling at angles across the wood grain using the 

Hankison equation. 

Dolwin and others (2000) used both stress-wave and 

ultrasonic inspection to detect decay in cross sections of 

English oak (Quercus robur L.) and European beech (Fagus 

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

35

sylvatica L.). Cubes of wood were inoculated with whiterot, brown-rot, or soft-rot. The wave speeds were measured 

using ultrasonic and acoustic stress waves. The slowing effect that rot had upon waves was more noticeable in acoustic 

waves than in ultrasonic waves. 

Brashaw and others (2000) used ultrasound conjunction 

with gas chromatography mass spectroscopy to identify 

wetwood in red oak lumber. The study also identified a 

relationship between the level of wetwood and energy-based 

ultrasound parameters. 

Ross and others (2001) examined the relationship between 

time of flight (TOF) measurements made perpendicular to 

the fiber axis of large timbers that had been in service and 

their residual strength in compression (both parallel and 

perpendicular to fiber axis). Noting that the sample size they 

used was very small, they observed a strong relationship 

between TOF and compression strength. 

Kabir and Araman (2002) examined several parameters 

derived from ultrasonic signals recorded during inspection 

of wooden pallets. Signal amplitude was decreased in the 

presence of defects. The severity and types of the defects 

correlated to the degree of dispersion of the power spectrum. Energy loss parameters were more sensitive to defects 

than time of flight measurements. 

Schubert and others (2005) used resonant ultrasonic spectroscopy to determine the shear modulus of decayed and 

sound specimens of Norway spruce. The specimens were 

excited using ultrasonic transducers and displacement at 

the faces were measured using laser interferometers. The 

natural frequencies of the specimens were determined from 

the displacement response measurements. The reduction in 

shear modulus was six to ten times larger than the density 

loss from fungal decay. The shear velocity and damping 

were calculated at different stages of fungal rot. 

Hasenstab and others (2005) used echo technique to detect 

local defects. Measurements were taken transverse and parallel to the grain. The echo technique only required access to 

one side of the inspected specimen. 

The sensitivity of different MOE calculation methods to 

the presence of holes in a beam was explored in 2005 by 

Castellanos and others. Several 50-mm holes were drilled in 

beams of Cryptomeria japonica (Japanese Sugi). The MOE 

of the boards were then determined using four different 

methods. The four MOE calculation methods, ordered by increased sensitivity, were static bending, ultrasonic, acoustic 

stress wave, and longitudinal vibration. 

Schubert and others (2006) used modal analysis and ultrasonic inspection to determine the change in shear modulus 

of Norway spruce upon exposure to white-rot fungus (Heterobasidion annosium and Ganoderma lipsiense). During 

the 12 weeks of exposure, H. annosium induced a 10% and 

G. lipsiense a 50% reduction in shear modulus. 

Lin and others (2007) examined the degradation of railroad 

ties that had been taken out of service after 20 years. The 

goal of this research was to evaluate the possibility of reusing ties in the same or other applications. Hence, the ties 

were tested as is and after being cut down to lumber of two 

smaller sizes. The sound velocity was found to have the best 

correlation to the residual strength of the ties with it decreasing as the samples tested ranged from small lumber piece to 

larger lumber to tie. The dynamic modulus of elasticity was 

found to have a weaker correlation with residual strength 

and the values generally higher. 

Hyvärinen (2007) used air-coupled transducers to detect 

defects in Scots pine. Knots, cracks, and heartwood were 

identified. The signal attenuation was capable of detecting 

heartwood while the board was moving between 1 and 

3 m/s, opening the possibility of online grading. Air transducer sorting comported with visual sorting for 90% of the 

boards. 

Tomographic views of timber members were constructed by 

Riggio and Piazza (2011) using ultrasonic transducers. Heterogeneities that affect wood strength were mapped using 

ultrasound and were then related to external features using 

digital photogrammetry. Drilling resistance tests were also 

used to corroborate results. 

Examining the effect of fungal decay on Norway spruce, 

Reinprecht and Hibky (2011) demonstrated that sound 

velocity and dynamic modulus were suitable for revealing which fungal agent was responsible for the decay with 

known decreases in wood density. Yet, the study could not 

tell the difference between various strains of brown-rot 

fungus. 

Ritschel and others (2013) studied damage mechanisms 

within failing spruce specimens in virtually real time using a 

combination of acoustic emission and synchrotron radiation 

x-ray tomographic microscopy (SRXTM). Acoustic emission enabled sub-millisecond resolution when determining 

damage initiation. The SRXTM produced high-resolution 

visualizations of the inner wood cellular structure in three 

dimensions. The visualizations allowed observation of 

changes in the microscopic wood structure in both time and 

space that led to ultimate failure. 

White and others (2013) reviewed the ability of several nondestructive techniques to assess wood damaged by heat and 

fire. The study found that changes in time delay and wave 

velocity for charred yellow poplar specimens was minor, 

but the area under the power spectral density plot changed 

significantly. Several other non-ultrasonic NDE techniques 

were also discussed but were not included in this review.

Engineered Wood Products

Engineered wood products cover a variety of material including oriented strandboard (OSB), fiberboard, particleboard, plywood, glued-laminated (glulam) beams, and 

veneers.

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

36

Strength and Grading

An early review of ultrasonic evaluation of wood composites was given in Szabo (1978). This reference covered 

many aspects of ultrasonic inspection including anisotropy, 

coupling issues, wave types, attenuation, basic ultrasonic 

testing setups, transducer descriptions, and the advantages 

and disadvantages of higher frequency usage. The concepts 

presented were fundamental when ultrasound was used in 

inspection applications. 

In a 1978 study, Kunesh found visual grading of parallel 

laminated veneer (PLV) made from Douglas-fir to be unsatisfactory with grading for strength. Ultrasonic inspection 

was capable of grading the material at a rate such that it 

could be used on a production line. A similar study was conducted using laminated veneer lumber with similar results 

and conclusions (Sharp 1985). 

Petit and others (1991) examined the changes in ultrasonic 

behavior through laboratory aged structural flakeboard. The 

boards were tested before an aging process was applied. Ultrasonic wave velocity decreased as the angle diverged from 

parallel with the length of the boards. The aging processes 

were hygrothermal treatments and were performed in accordance with French standards NF 51-262 and NF 51-263. 

Ultrasonic wave velocity, modulus of elasticity, and modulus of rupture decreased after the aging process was applied. 

High correlation between wave velocity and MOE both 

prior to and after the aging process supported the hypothesis 

that the relationship could be used to develop nondestructive 

evaluation techniques. The correlation between wave velocity and modulus of rupture was not as high as wave velocity and MOE, but the potential to use the relationship in an 

evaluation technique still existed. 

Lemaster and Beall (1993) examined surface roughness of 

medium density fiberboard (MDF) using acoustic emission. 

Wave guides of various shapes were moved across surfaces 

of MDF that were previously sanded using various sized grit 

sandpaper. The motion and the waveguides caused measurable acoustic emission events. The technique was capable of 

detecting groves and snaking.

A 1996 study by Kruse and others used contact and noncontact methods to measure wave speed through MDF and 

particleboard. Surface sanding affected the ultrasonic waves, 

so the two groups were tested separately. Unsanded boards 

had lower velocities. Non-contact methods were better 

correlated than contact methods for both internal bond of 

sanded boards and also swelling of unsanded boards. Wave 

velocity was measured through both MDF and particleboards with thicknesses of 34 mm or less without problems 

caused by signal attenuation. 

Beall and Chen (1998) used ultrasonic waves and acoustoultrasonic techniques to monitor particleboard curing. Wave 

guides allowed signals to be taken from the boards during 

pressing, but without interfering with the process. Strength 

development, board thickness, resin content, and press temperature correlated to the root mean square (RMS) of the 

collected signals. 

In 1998, Tucker and others evaluated several ultrasonic frequencies for monitoring wood plastic composites. The study 

determined wave speed and attenuation from ultrasonic 

signals. Strong correlation existed between wave speed and 

MOE. Good correlation existed between MOE and MOR. 

High frequencies were significantly attenuated over short 

distances. 

The relationship between wave velocity through woodbased composite materials and several different parameters 

was explored by Bekhta and others (2000). Particleboard, 

OSB, and MDF were studied. Parameters examined included relative humidity, temperature, direction of measurements, and frequency. Empirical equations relating modulus 

of rupture, resonance frequency, wave velocity, MOE calculated from resonance frequency, MOE calculated from wave 

velocity, and MOE measured from static bending tests as 

well as the associated correlation values for each relationship were given.

Vun and others (2000) used contact and non-contact sensors 

to inspect OSB. Wave velocities were higher when contact 

sensors, rather than non-contact, were used. Internal voids 

were found to reduce MOE and dimensional stability. Wave 

velocity and attenuation were fitted to a third order polynomial to density. The percentage of board resin affected the 

velocity trends. 

Kruse (2000) explored use of various NDT technologies for 

process control of panel production. A good reference on 

several NDT technologies and their individual uses in panel 

production was provided. Ultrasonic velocity was useful in 

determining panel thickness, MOE, and internal bond for 

particleboard and MDF. The use of ultrasound to evaluate 

internal bond was thought to potentially reduce production 

cost between 2% and 8%. 

Vun and others (2002) characterized the horizontal density 

of OSB using several parameters including ultrasonic velocity. Other parameters examined were attenuation and RMS. 

A polynomial and a power model were constructed. The 

power model provided a higher correlation with the measured density values. 

A 2002 study by Moore and Bier found that veneer grading 

using both ultrasonic velocity and density produced lower 

variable grades than using ultrasonic velocity alone. Much 

of the discussion focused upon methods of adjusting calculations for veneers with higher MC so grading could be 

ultimately performed on green wood veneer. 

Tucker and others (2002) evaluated the elastic properties 

of natural fiber composite panels using antisymmetric plate 

waves excited by an ultrasonic tone burst. Flexural and 

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

37

transverse shear rigidity values were obtained from dispersion curves using fundamental plate wave propagation 

theory. Good agreement existed between the shear modulus 

calculated from transverse through-thickness tests and the 

plate wave tests. The effects of panel othrotropy on the 

shear modulus and rigidity calculations were discussed. The 

method was determined to be feasible and accurate for panels less than 6.4 mm thick. 

Najafi and Ebrahimi (2005) tested three different methods 

for predicting the longitudinal velocity in particleboard and 

fiberboard. Longitudinal wave velocity was found to change 

within particle and fiber boards because of constituent material aligning with manufacturing machinery during production. Hankinson and Jacoby equations were used to model 

the change in longitudinal velocity. Cubic and quadratic 

equations, using angle as the independent variable, were 

also examined. All three methods were effective at modeling 

the velocity changes to manufacturing angle.

Defect Detection

The success of ultrasonic inspection to detect voids led to 

a discussion of the feasibility of online production inspection of plywood and composite boards. Baker and Carlson 

(1978) found use of non-contact sensors as part of quality 

control had great potential to benefit both manufacturers and 

customers. 

Beall and Biernacki (1991) evaluated Douglas-fir glulam 

beams using acousto-ultrasonic techniques. To minimize 

variation in reference tests conducted on control zones, 

the time to peak amplitude was used as a parameter. The 

acousto-ultrasonic techniques were not directly sensitive to 

bond strength, but were sensitive to the presence of certain 

defects. 

Illman and others (2002) were the first to examine use of 

acoustic parameters for monitoring the deterioration of a 

commonly used wood structural composite, oriented strandboard (OSB). In a closely controlled laboratory experiment, they obtained similar results to those observed for 

clear wood. In a follow-up analysis of the data, Ross and 

others (2003) were successful at developing strong correlative models between acoustic parameters and the residual 

strength of deteriorated OSB.

Dill-Langer and others (2005) evaluated defects in adhesive 

boding between laminates of glulam beams using several 

ultrasonic parameters. Defects included lack of adhesive, 

adhesive curing prior to bonding, and epoxy block gluing. The parameters examined were time of flight (TOF), 

peak to peak amplitude, and amplitude of initial peak. The 

amplitude of the initial peak was found to be useful when 

detecting glue line defects. Both TOF and peak to peak amplitude showed large fluctuations and limited contrast. Three 

coupling conditions were also explored: dry, paste, and 

elastomer. Dry coupling produced the worst test results with 

low signal to noise ratio; paste coupling provided the best. 

Elastomer coupling had a higher signal to noise ratio than 

dry but did not alter the condition of the beam as did paste. 

In 2009, Bobadilla and others artificially aged particle- and 

fiberboards and then tested using several inspection methods 

including ultrasound. Strong correlation was found between 

ultrasonic wave velocity and changes in mechanical properties of the boards subjected to aging. The independent variables were used to estimate the natural logarithm of density 

rather than density itself, which might have contributed to 

the strength of the correlation. 

Sanabria and others (2009) identified delamination defects 

between two solid spruce boards bonded together with 

polyurethane adhesive by using ultrasonic inspection. The 

boards were 5-mm thick. The boards were bonded with adhesive, but portions of the bonding surfaces were left free 

of adhesive. Air-coupled transducers were used to perform 

through transmission measurements. The signal loss due 

to lack of glue bonding far exceeded the loss from normal 

wood heterogeneity spruce. 

Divos and others (2009) measured the depth of cracks 

within glulam beams using ultrasonic techniques. Transducers were places on either side of the crack within 20 to 

50 mm. The crack depth was estimated based upon TOF 

measurements and was accurate to within 10% of the true 

crack depth. Initial tests were carried out to estimate shear 

strength between lamina. Estimations of residual stresses 

within glulam beams were caused by the manufacturing process and changes to the climate condition. 

In a follow-up to the study conducted by Bobadilla and 

others (2009), the researchers artificially aged OSB panels 

and then tested the panels using several inspection methods 

including ultrasound. Strong correlation was found between 

ultrasonic wave velocity and changes in mechanical 

properties of the OSB panels. During the inspection, the 

transducers were inclined 45° to the plane of the panel 

surface. The feasibility of using the technique in the field 

was demonstrated.

In 2011b, Bucur performed a dual high and low frequency 

inspection on wood based composites. A dual high and 

low frequency inspection was carried out on wood based 

composites. Local defects were detected using high 

frequency ultrasonic techniques. Global damage detection 

was based upon low frequency vibrations. A finite element 

model of the analyzed structure was used to estimate modal 

shapes, frequencies, and damping. 

Sanabria and others (2011) examined the structural integrity 

of multilayered glued-laminated beams using air coupled 

transducers. Bonding defect between lamina were identified 

for specimens 520 mm (20.5 in.), demonstrating for the 

first time the feasibility of using air coupled transducers on 

lamina of such thickness.

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

38

Acoustic Emission

Acoustic emission is not strictly an ultrasonic method. The 

frequencies excited during an acoustic emission event can 

be within the acoustic or ultrasonic ranges, or both. The 

studies presented here involve researchers that have applied 

acoustic emission techniques within the ultrasonic range 

of frequencies to engineered wood products. Beall (1989) 

presented a literature review covering work in the area of 

acoustic emissions prior to 1989. 

Sato and Fushitani (1991) used acoustic emission to detect 

poor bonding in plywood. A three-point bending test allowed both MOE and acoustic emissions to be monitored 

in a single test. The number of AE events was found to be a 

better indicator of MOR than the number of knots. Regions 

of poor bonding were characterized by an increased number 

of acoustic emission events. 

Lemaster (1993) conducted a study in which the individual 

particles and flakes used to make engineered wood panels 

were characterized using acoustic emissions. Particles and 

flakes were dropped onto a plate and the waveform was 

recorded. Waveforms were analyzed in both the time and 

frequency domain. Size classes were best defined by the 

number of threshold crossings. Frequency centroid was used 

to describe the category of particles and flakes. 

A two part study was conducted by Adams and Morris 

in 1996. In the first part, Adams and Morris (1996) used 

acoustic emission in conjunction with fracture pullout 

to observe how changes in MC affected the fracture of 

OSB and MDF. Adams and Morris (1996) used acoustic 

emission in conjunction with fracture pullout to observe 

how changes in MC affected the fracture of OSB and MDF. 

A beam specimen was notched and then loaded in threepoint bending. The notch on the beam was in line with the 

center load and opened towards the opposite side. As the test 

continued, the specimen fractured and the acoustic emission 

events were counted. The speed of the tests strongly 

influenced the results. In the second portion of the study, 

Morris and Adams (1996) correlated acoustic emissions to 

crack propagation. OSB and MDF were again the materials 

examined. Acoustic emissions were used to identify 

different stages of fracture during a three point bending test. 

The specimens were notched boards as described above. 

Crack formation initiated with high numbers of acoustic 

emission events; crack propagation had fewer events than 

formation. High MC lowered the number of events making 

it difficult to identify phases for crack formation and 

propagation.

Beall (1996) explored duration of load behavior of OSB 

by loading specimens to 40%, 65%, and 80% of ultimate 

load and monitoring acoustic emission events. The number 

of events increased at a rate similar to the rate of specimen 

deformation until a constant load point was reached; at 

which point, the number of events decreased exponentially. 

The difficulty of typical ultrasonic techniques to detect 

tensile damage in wood led to an investigation into 

identification through acoustic emissions. Pierre (2000) 

found that acoustic emissions showed promise as a tool to 

monitor tensile damage. 

Vun and Beall (2002) demonstrated that acoustic emission 

was an effective tool in monitoring creep of OSB. The 

cumulative number of acoustic emission events correlated 

strongly to specimen deflection. The test also showed a 

potential future use of acoustic emission to evaluate fracture 

mechanisms and locating the source location. 

Locations of fractures within specimens of OSB, plywood, 

and MDF were later located by Niemz and others (2007) 

using acoustic emissions. Four sensors were placed around 

the specimen. The difference in arrival time of the events 

allowed the fracture location to be estimated parallel to 

the panel. Perpendicular location was not possible as 

perpendicular wave speed was only a third of the wave 

speed parallel to the panel. 

Ritschel and others (2011) performed tensile tests on LVL 

and monitored plywood samples using acoustic emission 

and digital image correlation. The rate and intensity of 

acoustic emission events changed with different strain 

behavior of the specimen, indicating that acoustic emissions 

were suitable for analyzing micro-mechanisms of damage 

growth.

El-Hajjar and Qamhia (2013) used acoustic waveforms 

produced by acoustic emissions during tensile testing of 

triaxially braided regenerated cellulose composites to 

identify failure modes. The study confirmed that acoustic 

emissions could be monitored to identify onset of internal 

damage.

Buildings and Structures

Many different ultrasonic inspection tools are commercially 

available. Ultrasonic inspection technology is well established, relatively low cost, and can be ruggedized for use 

outside ideal laboratory conditions. These features make 

ultrasound an attractive tool for use in situ. Palaia-Perez 

(1993) examined the time and frequency domain of ultrasonic signals through beams. Identifying conclusive patterns 

from the time domain signal was difficult; frequency domain signals provided useful information. Amplitude of the 

highest frequency decreases with the size of interior voids. 

Baldassino (1996) used several nondestructive techniques in 

conjunction with each other including hardness testing, steel 

pin penetration, drilling resistance, and ultrasonic inspection. Beams were removed from the structure and tested. 

Baldassino determined that multiple techniques were necessary to assess the material behavior. 

Anthony and others (1998) inspected timber piles from 

the Queens Boulevard Bridge in Queens, New York, using 

Chapter 3
Nondestructive Evaluation of Wood: Second Edition

39

radial ultrasonic inspection and micro-drilling techniques. 

Ultrasound was used to determine areas that needed to be 

inspected using a resistograph. The study was part of a 

comprehensive inspection using innovative techniques. 

Transducer coupling was a focus when Emerson and others 

(1998) inspected creosote-coated timbers. TOF and frequency were collected in a grid along each timber. Advanced 

decay was easily identified using TOF. Spectral analysis 

and peak frequency showed potential in identifying incipient decay. A dry flexible membrane was used as a couplant. 

Greater consistency of coupling force was needed. 

During shear wall testing, Beall and others (2005) found 

that few acoustic emission events occurred up to 40% of the 

maximum load. The absence of AE events indicates the deformation was reversible. AE events were more likely to be 

observed in the frame than the panel. 

A description of an ultrasonic through transmission setup for 

clear wood timbers was provided in Agrawal and Choudhari 

(2009).

Karlinasari and Bahtiar (2011) examined the differences between boards end-jointed using finger joints and those using 

scarf joints. The process of end-jointing did not significantly 

change ultrasonic wave velocity and dynamic MOE from 

those of the constituent boards. The correlations between 

dynamic MOE from ultrasound and static MOE or MOR 

were higher in finger-jointed boards than scarf-jointed. 

Teder and others (2011) examined the effect of sensor spacing on structural timber assessment. The study determined 

that decreasing the sensor spacing caused the inspection 

results to yield more localized value. A sensor distance of 

600 mm was found to be the best arrangement for determining physical and mechanical properties using either longitudinal or indirect measurements. This finding matched the 

literature reviewed upon the subject. A brief description of 

ultrasonic inspection was also provided.

Teder and Wang (2013) used several nondestructive techniques to examine 75-year old glulam arches extracted from 

a building during deconstruction. The arches were tested 

using stress-wave timing, ultrasonic wave propagation, 

resistance microdrilling, and visual inspection along cutting 

planes. This study found that wave propagation was a good 

indicator of moderate to large delaminations and internal 

decay.

Historical Structures and Artifacts

There are many historical structures and artifacts of cultural 

significance. The alteration of these structures and objects 

would be considered at best, undesirable and, at worst, a 

loss for a society or the world. Nondestructive testing is especially well suited for inspecting these places and things as 

the ending condition is unchanged, insuring their preservation for future generations.

Ceccotti and Togni (1996) used several nondestructive techniques to evaluate beams from a fifteenth century Florentine 

building under restoration. Techniques used included visual 

inspection, stress wave, free vibration, hardness testing, 

and ultrasound. Wave inspection was poor at detecting ring 

shakes, localized decay, and variations in MC. A multiparameter inspection had the best correlation with strength. 

The copper pins and the wood surrounding the pins from the 

USS Constitution were tested by Ross and others (1998) for 

signs of deterioration using ultrasound. 

Kandemir-Yucel and others (2005) examined timbers from 

the Aslanhane Mosque in Ankara, Turkey, using thermographic and ultrasonic inspection. Ultrasound measurements 

were taken perpendicular to grain of pillars to evaluate their 

soundness. Infrared was used in conjunction with ultrasound 

to determine the effects of MC. 

Lee and others (2009) evaluated an ancient Korean wood 

building, Daeseongjeon in Yeosan, Hyanggyo, using wave 

TOF measurements. Ultrasonic tomographic cross sections 

of supporting pillars were created. Coupling the sensors to 

the pillars was major difficulty during the inspection process 

due to poor surface conditions. 

In 2009, Arriaga and others proposed a methodology of testing ancient Spanish timbers composed of Scots pine and 

Laricio pine. Twenty-five timber pieces were extracted from 

an eighteenth century building and tested in the proposed 

method and mechanical testing. The correlation between the 

proposed method and mechanical testing was low for MOE 

and poor for MOR. 

Ross and Dundar (2012) inspected a 2,500-year-old Egyptian wooden inner coffin of Meretites using ultrasound. The 

bottom portion of the coffin was found to have deterioration. 

In 2011, Lee and others presented a portable device capable 

of inspecting rafters from an ancient Korean wooden building. Conventional TOF was ineffective for rafter inspection. 

Several ultrasonic parameters were examined including 

TOF, amplitude, energy, pulse length, and RMS voltage. A 

poor correlation relationship was developed to estimate deterioration depth using the TOF and pulse length parameters. 

Beikircher and others (2013) tested timber strength in buildings constructed over a wide range of dates 1250 AD to 

2011 AD using both nondestructive and destructive methods. The nondestructive methods included visual inspection, 

electrical resistance, and ultrasound; the destructive methods 

were drill resistance and fractometer. The goal was to compare aged wood with virgin wood. Temperature, MC, and 

natural variability of wood caused results to lack precision. 

Indirect ultrasound measurements were not reliable when 

estimating strength of the wood. 

Ultrasonic-Based Nondestructive Evaluation Methods for Wood
General Technical Report FPL–GTR–238

40 Chapter 3

Table 3.2—List of tree species studies and associated bibliographic reference 

Tree species Reference 

Agathis alba Karlinasari and others 2011 

Alan batu (Shorea albida) Duju and others 2000 

Alder Grimberg and others 2011 

Alerce (Fitzroya cupressoides) Baradit and Niemz 2011 

Angelim (Hymenolobium petraeum) Terezo and others 2005 

Angelim araroba (Votaireopsis araroba) Bartholomeu and Gonçalves 2007 

Annona duckei Inés and others 2011 

Apeiba membranaceae Inés and others 2011 

Apuleia leiocarpa Trinca and others 2009 

Inés and others 2011 

Aquilaria microcarpa Karlinasari 2013 

Aspidosperma polyneuro Trinca and others 2009 

Aspidosperma rigidum Inés and others 2011 

Aspidosperma schultesii Inés and others 2011 

Astronium lecontei Pires and others 2011 

Balfourodendron riedelianum Trinca and others 2009 

Bauhinia purpurea Karlinasari and others 2011 

Beech Diebold and others 2000 

Grimberg and others 2011 

Betula costata Wang and others 2013 

Bilat (Parashorea macrophlla) Duju and others 2000 

Black oak (Quercus kelloggii) Quarles and Zhou 1987 

Brosimum potabile Inés and others 2011 

Cabreúva (Myrocarpus frondosus) van Dijk and others 2013 

Caesalpinia echinata Trinca and others 2009 

Caryocar glabrum Inés and others 2011 

Cavanillesia umbellate Inés and others 2011 

Cedrelinga cateniformis Inés and others 2011 

Cedrelinga Cateniformis Ducke Yoza and Mallque 2011 

Cherry Grimberg and others 2011 

Cherry (Prunus sar gentii Rehd. subsp. jamasakura Ohwi) Kawamoto 1996 

Chinese fir (Cunninghamia lanceolata (lamb.) Hook) Yin and others 2005 

Chrysophyllum prieurii Inés and others 2011 

Chrysophyllum venezuelanense Pires and others 2011 

Clarisia racemosa Inés and others 2011 

Croton matourensis Inés and others 2011 

Cupiúba (Goupia glabra) Gonçalves and Bartholomeu 2002 

Gonçalves and Costa 2005 

Bartholomeu and Gonçalves 2007 

Cupiúba (Goupia glabra) Gonçalves and others 2011 

Dacroides nitens Inés and others 2011 

Diplotropis purpurea Inés and others 2011 

Dipteryx micrantha Inés and others 2011 

Dipteryx odorata Trinca and others 2009 

Douglas-fir (Pseudotsuga menziesiii) Kunesh 1978 

Lemaster and others 1993 

Anthony and Phillips 1991 

Beall and Biernacki 1991 

Anthony and Phillips 1993 

Diebold and others 2000 

Ballarin and others 2002 

Seeling 2002 

Eastern cottonwood (Populus deltoids) Brancheriau and others 2011 

Endopleura uchi Pires and others 2011 

English oak (Quercus robur L.) Dolwin and others 2000 

Eucalipto (Eucalyptus saligna) Gonçalves and others 2011 

Eucalyptus citriodora Bartholomeu and Gonçalves 2007 

Gonçalves and others 2009 

Bertoldo and others 2013 

Freitas and others 2013 

Eucalyptus globulus Yang and others 2007 

Eucalyptus grandis Bartholomeu and Gonçalves 2007 

Pedroso and others 2011, 2013 

Ferreira and others 2013 

Eucalyptus pellita Bertoldo and others 2013 

Eucalyptus saligna Bartholomeu and Gonçalves 2007 

Freitas and others 2013 

European beech (Fagus sylvatica) Dolwin and others 2000 
Nondestructive Evaluation of Wood: Second Edition

41

Table 3.2—List of tree species studies and associated bibliographic reference (cont.) 

Tree species Reference 

Agathis alba Karlinasari and others 2011 

Alan batu (Shorea albida) Duju and others 2000 

Alder Grimberg and others 2011 

Alerce (Fitzroya cupressoides) Baradit and Niemz 2011 

Angelim (Hymenolobium petraeum) Terezo and others 2005 

Angelim araroba (Votaireopsis araroba) Bartholomeu and Gonçalves 2007 

Annona duckei Inés and others 2011 

Apeiba membranaceae Inés and others 2011 

Apuleia leiocarpa Trinca and others 2009 

Inés and others 2011 

Aquilaria microcarpa Karlinasari 2013 

Aspidosperma polyneuro Trinca and others 2009 

Aspidosperma rigidum Inés and others 2011 

Aspidosperma schultesii Inés and others 2011 

Astronium lecontei Pires and others 2011 

Balfourodendron riedelianum Trinca and others 2009 

Bauhinia purpurea Karlinasari and others 2011 

Beech Diebold and others 2000 

Grimberg and others 2011 

Betula costata Wang and others 2013 

Bilat (Parashorea macrophlla) Duju and others 2000 

Black oak (Quercus kelloggii) Quarles and Zhou 1987 

Brosimum potabile Inés and others 2011 

Cabreúva (Myrocarpus frondosus) van Dijk and others 2013 

Caesalpinia echinata Trinca and others 2009 

Caryocar glabrum Inés and others 2011 

Cavanillesia umbellate Inés and others 2011 

Cedrelinga cateniformis Inés and others 2011 

Cedrelinga Cateniformis Ducke Yoza and Mallque 2011 

Cherry Grimberg and others 2011 

Cherry (Prunus sar gentii Rehd. subsp. jamasakura Ohwi) Kawamoto 1996 

Chinese fir (Cunninghamia lanceolata (lamb.) Hook) Yin and others 2005 

Chrysophyllum prieurii Inés and others 2011 

Chrysophyllum venezuelanense Pires and others 2011 

Clarisia racemosa Inés and others 2011 

Croton matourensis Inés and others 2011 

Cupiúba (Goupia glabra) Gonçalves and Bartholomeu 2002 

Gonçalves and Costa 2005 

Bartholomeu and Gonçalves 2007 

Cupiúba (Goupia glabra) Gonçalves and others 2011 

Dacroides nitens Inés and others 2011 

Diplotropis purpurea Inés and others 2011 

Dipteryx micrantha Inés and others 2011 

Dipteryx odorata Trinca and others 2009 

Douglas-fir (Pseudotsuga menziesiii) Kunesh 1978 

Lemaster and others 1993 

Anthony and Phillips 1991 

Beall and Biernacki 1991 

Anthony and Phillips 1993 

Diebold and others 2000 

Ballarin and others 2002 

Seeling 2002 

Eastern cottonwood (Populus deltoids) Brancheriau and others 2011 

Endopleura uchi Pires and others 2011 

English oak (Quercus robur L.) Dolwin and others 2000 

Eucalipto (Eucalyptus saligna) Gonçalves and others 2011 

Eucalyptus citriodora Bartholomeu and Gonçalves 2007 

Gonçalves and others 2009 

Bertoldo and others 2013 

Freitas and others 2013 

Ultrasonic-Based Nondestructive Evaluation Methods for Wood

Eastern cottonwood (Populus deltoids) Brancheriau and others 2011 

Endopleura uchi Pires and others 2011 

English oak (Quercus robur L.) Dolwin and others 2000 

Eucalipto (Eucalyptus saligna) Gonçalves and others 2011 

Eucalyptus citriodora Bartholomeu and Gonçalves 2007 

Gonçalves and others 2009 

Bertoldo and others 2013 

Freitas and others 2013 

Eucalyptus globulus Yang and others 2007 

Eucalyptus grandis Bartholomeu and Gonçalves 2007 

Pedroso and others 2011, 2013 

Ferreira and others 2013 

Eucalyptus pellita Bertoldo and others 2013 

Eucalyptus saligna Bartholomeu and Gonçalves 2007 

Freitas and others 2013 

European beech (Fagus sylvatica) Dolwin and others 2000 

Ficus Americana Inés and others 2011 

Fraxinus mandshurica Song and others 2011 

Gallesia integrifólia Trinca and others 2009 

Garapeira (Apulleia leiocarpa) Gonçalves and others 2011 

Guarea kuntiana Inés and others 2011 

Holopyxidium jarana Pires and others 2011 

Imbuia (Ocotea porosa) Gonçalves and Costa 2005 

Jacaranda copaia Inés and others 2011 

Jackfruit (Artocarpus heterophyllus Lamk.) Karlinasari and others 2009 

Japanese cedar (Cryptomeria japonica D. Don) Soma and others 2000 

Japanese magnolia (Magnolia obovata Thunb.) Sasaki and Hasegawa 2000 

Japanese Sugi (Cryptomeria japonica) Castellanos and others 2005 

Jeunjing (Paraserianthes falcataria) Karlinasari and others 2007 

Keruing utap (Dipterocarpus rigidus) Duju and others 2000 

Kruing (Dipterocarpus spp.) Tjondro and Soryoatmono 2007 

Larch Diebold and others 2000 

Larch (Larix gmelini) Yin and others 2009 

Laricio pine (Pinus nigra Arn. ssp. salzmannii) Arriaga and others 2009 

Iñiguez and others 2009 

Iñiguez-Gonzalez and others 2013 

Laurel (Laurelia sempervirens) Baradit and Niemz 2011 

Lecythis pisonis Pires and others 2011 

Lenga (Nothofagus pumilio) Baradit and Niemz 2011 

Licania elata Inés and others 2011 

Loblolly pine Huang 2000 

Loblolly pine (Pinus taeda) Oliveira and others 2002 

Fassola and others 2011 

Madrone (Arbutus menziesii). Hamstad and others 1993 

Mangium (Acacia mangium Willd.) Karlinasari and others 2009 

Manio (Podocarpus nubigena) Baradit and Niemz 2011 

Maple (Acer spp.) Hamstad and others 1993 

Maritime pine (Pinus pinaster Ait.) Machado 2002 

Iñiguez-Gonzalez and others 2013 

Matisia bracteolosa Inés and others 2011 

Meranti (Shorea spp.) Karlinasari and Bahtiar 2011 

Mezilaurus Trinca and others 2009 

Mimusops elengi Karlinasari and others 2011 

Myroxylon Balsamum Trinca and others 2009 

Naucleopsis glabra Inés and others 2011 

Nectandra Trinca and others 2009 

Oak Ross and others 1998 

Diebold and others 2000 

Ocotea fragantisima Inés and others 2011 

Olivillo (Aextoxicon punctatum) Baradit and Niemz 2011 

Oriental beech (Fagus orientalis) Najafi and others 2007, 2009 

P. pachycharpa Pires and others 2011 

Pequía (Aspidosperma desmanthum) Gonçalves and others 2011 

Peroba (Aspidosperma pirycollum) Terezo and others 2005 

Pine Diebold and others 2000 

Miettienen and others 2005 

Grimberg and others 2011 

Pinho do Parana (Araucaria angustifolia) Gonçalves and Costa 2005 

Populus simonii Song and others 2011 

Pouteria guianensis Pires and others 2011 

Pterocarpus indicus Karlinasari and others 2011 

Radiata pine (Pinus radiata D. Don) Booker and others 1996, 2000 

Andrews 2002 

Moore and Bier 2002 

Kang and Booker 2002 

Iñiguez and others 2009 

IñiGld h2013 
General Technical Report FPL–GTR–238

42

Table 3.2—List of tree species studies and associated bibliographic reference (cont.) 

Tree species Reference 

Agathis alba Karlinasari and others 2011 

Alan batu (Shorea albida) Duju and others 2000 

Alder Grimberg and others 2011 

Alerce (Fitzroya cupressoides) Baradit and Niemz 2011 

Angelim (Hymenolobium petraeum) Terezo and others 2005 

Angelim araroba (Votaireopsis araroba) Bartholomeu and Gonçalves 2007 

Annona duckei Inés and others 2011 

Apeiba membranaceae Inés and others 2011 

Apuleia leiocarpa Trinca and others 2009 

Inés and others 2011 

Aquilaria microcarpa Karlinasari 2013 

Aspidosperma polyneuro Trinca and others 2009 

Aspidosperma rigidum Inés and others 2011 

Aspidosperma schultesii Inés and others 2011 

Astronium lecontei Pires and others 2011 

Balfourodendron riedelianum Trinca and others 2009 

Bauhinia purpurea Karlinasari and others 2011 

Beech Diebold and others 2000 

Grimberg and others 2011 

Betula costata Wang and others 2013 

Bilat (Parashorea macrophlla) Duju and others 2000 

Black oak (Quercus kelloggii) Quarles and Zhou 1987 

Brosimum potabile Inés and others 2011 

Cabreúva (Myrocarpus frondosus) van Dijk and others 2013 

Caesalpinia echinata Trinca and others 2009 

Caryocar glabrum Inés and others 2011 

Cavanillesia umbellate Inés and others 2011 

Cedrelinga cateniformis Inés and others 2011 

Cedrelinga Cateniformis Ducke Yoza and Mallque 2011 

Cherry Grimberg and others 2011 

Cherry (Prunus sar gentii Rehd. subsp. jamasakura Ohwi) Kawamoto 1996 

Chinese fir (Cunninghamia lanceolata (lamb.) Hook) Yin and others 2005 

Chrysophyllum prieurii Inés and others 2011 

Chrysophyllum venezuelanense Pires and others 2011 

Clarisia racemosa Inés and others 2011 

Croton matourensis Inés and others 2011 

Cupiúba (Goupia glabra) Gonçalves and Bartholomeu 2002 

Gonçalves and Costa 2005 

Bartholomeu and Gonçalves 2007 

Cupiúba (Goupia glabra) Gonçalves and others 2011 

Dacroides nitens Inés and others 2011 

Diplotropis purpurea Inés and others 2011 

Dipteryx micrantha Inés and others 2011 

Dipteryx odorata Trinca and others 2009 

Douglas-fir (Pseudotsuga menziesiii) Kunesh 1978 

Lemaster and others 1993 

Anthony and Phillips 1991 

Beall and Biernacki 1991 

Anthony and Phillips 1993 

Diebold and others 2000 

Ballarin and others 2002 

Seeling 2002 

Eastern cottonwood (Populus deltoids) Brancheriau and others 2011 

Endopleura uchi Pires and others 2011 

English oak (Quercus robur L.) Dolwin and others 2000 

Eucalipto (Eucalyptus saligna) Gonçalves and others 2011 

Eucalyptus citriodora Bartholomeu and Gonçalves 2007 

Gonçalves and others 2009 

Bertoldo and others 2013 

Freitas and others 2013 

Chapter 3

P. pachycharpa Pires and others 2011 

Pequía (Aspidosperma desmanthum) Gonçalves and others 2011 

Peroba (Aspidosperma pirycollum) Terezo and others 2005 

Pine Diebold and others 2000 

Miettienen and others 2005 

Grimberg and others 2011 

Pinho do Parana (Araucaria angustifolia) Gonçalves and Costa 2005 

Populus simonii Song and others 2011 

Pouteria guianensis Pires and others 2011 

Pterocarpus indicus Karlinasari and others 2011 

Radiata pine (Pinus radiata D. Don) Booker and others 1996, 2000 

Andrews 2002 

Moore and Bier 2002 

Kang and Booker 2002 

Iñiguez and others 2009 

Iñiguez-Gonzalez and others 2013 

Ramin (Gonystylus bancanus) Duju and others 2000 

Red oak Schafer and others 1998; Brashaw and others 2000 

Red pine (Pinus densiflora) Kawamoto 1993,1996 

Kim and others 2007; Oh and others 2013 

Red pine (Pinus resinosa) Gao and others 2011, 2013 

Redwood (Sequoia sempervirens) Hamstad and others 1993 

Ballarin and others 2002 

Seeling and others 2002 

Resak durian (Cotylelobium burcki) Duju and others 2000 

Romanian beech Curtu and others 1996 

Salix matsudana Koidz Song and others 2011 

Sandalwood (Santalum album) Turpening 2011 

Schefflera morototoni (Sacha cetico) Inés and others 2011 

Scots pine (Pinus sylvestris L.) Abbott and Elcock 1987 

Feeney and others 1996 

Kánnár 2000 

Iñiguez and others 2007 

Hyvärinen 2007 

Arriaga and others 2009 

Iñiguez and others 2009 

Hermoso and others 2013 

Iñiguez-Gonzalez and others 2013 

Llana and others 2013 

Noya 2013 

Sessile oak (Quercus petrea) Dundar and others 2013 

Silver fir (Abies alba Mill.) Ceccotti and Tongi 1996 

Simarouba amara Inés and others 2011 

Slash pine (Pinus elliottii) Bartholomeu and Gonçalves 2007 

Massak and others 2009 

Pedroso and others 2011 

Southern pine Patton-Mallory and others 1987 

Spruce Sandoz 1991 

Feeney and others 1996 

Diebold and others 2000 

Hauffe and Mahler 2000 

Kuklík and Kuklíkova 2000 

Pierre 2000 

Spruce (Picea abies) Niemz and Kucera 1998 

Dill-Langer et al 2002 

Seeling and others 2002 

Rosner and Wimmer 2005 

Schubert and others 2005 

Sanabria and others 2009 

Rohanová and others 2011 

Rosner 2011 

Sanabria and others 2011 

van der Beek and Tiitta 2011 

Sterculia frondosa Inés and others 2011 

Sweet chestnut (Castanea sativa Mill.) Dundar and others 2013 

Iñiguez-Gonzalez and others 2013 

Vásquez and others 2013 

Swietenia sp. Karlinasari and others 2011 

Sycamore Grimberg and others 2011 

Sycamore fig (Ficus sycomorus) Dundar and Ross 2011 

Tabebuia serratifolia Inés and others 2011 

Tepa (Laureliopsis philipiana) Baradit and Niemz 2011 

Tetragastris panamensis Inés and others 2011 

Toona ciliata Pedroso and others 2011 

Ulmus pumila Song and others 2011 

Unonopsis floribunda Inés and others 2011 
Nondestructive Evaluation of Wood: Second Edition

43

Noya (2013) examined two Scots pine structural members 

of the seventeenth century roof of the library of the College 

of San Pablo in Granada, Spain, using ultrasound, MC measurements, and fungal cultures. Ultrasound measurements 

were taken perpendicular to the grain of the timbers. Data 

from the inspection led to the conclusion that high decay 

was present.

Concluding Remarks

Ultrasonic inspection of wood has evolved over a half a 

century of research and development. In this chapter, a 

comprehensive literature review of the use of ultrasound in 

wood inspection was presented and information regarding 

basic ultrasonic inspection techniques and analyses were described. Table 3.2 at the end of this chapter contains a list of 

over one hundred species of wood that have been inspected 

using ultrasound.

Strength grading, determination of elastic constants, and 

evaluation of MC effects are a few of the fields to which 

ultrasonic inspection have been successfully applied. The 

most widespread application of ultrasonic inspection with 

wood is arguably defect detection. There is an ongoing need 

to detect and assess defects within standing trees, poles, 

lumber, structures, and engineered wood products. Increased 

sensitivity and more accurate approximations of remaining 

wood strength aid inspectors in the evaluating the utility and 

safety of wood structures. Wood is already the most common building material in the world, but with the increased 

reliability that comes with advanced ultrasonic inspection 

techniques, its use can only grow.

Literature Cited

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context. In: Proceedings, 6th Symposium, Nondestructive 

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Adams, J.; Morris, V. 1996. Acoustic emission and fracture 

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Table 3.2—List of tree species studies and associated bibliographic reference (cont.) 

Tree species Reference 

Agathis alba Karlinasari and others 2011 

Alan batu (Shorea albida) Duju and others 2000 

Alder Grimberg and others 2011 

Alerce (Fitzroya cupressoides) Baradit and Niemz 2011 

Angelim (Hymenolobium petraeum) Terezo and others 2005 

Angelim araroba (Votaireopsis araroba) Bartholomeu and Gonçalves 2007 

Annona duckei Inés and others 2011 

Apeiba membranaceae Inés and others 2011 

Apuleia leiocarpa Trinca and others 2009 

Inés and others 2011 

Aquilaria microcarpa Karlinasari 2013 

Aspidosperma polyneuro Trinca and others 2009 

Aspidosperma rigidum Inés and others 2011 

Aspidosperma schultesii Inés and others 2011 

Astronium lecontei Pires and others 2011 

Balfourodendron riedelianum Trinca and others 2009 

Bauhinia purpurea Karlinasari and others 2011 

Beech Diebold and others 2000 

Grimberg and others 2011 

Betula costata Wang and others 2013 

Bilat (Parashorea macrophlla) Duju and others 2000 

Black oak (Quercus kelloggii) Quarles and Zhou 1987 

Brosimum potabile Inés and others 2011 

Cabreúva (Myrocarpus frondosus) van Dijk and others 2013 

Caesalpinia echinata Trinca and others 2009 

Caryocar glabrum Inés and others 2011 

Cavanillesia umbellate Inés and others 2011 

Cedrelinga cateniformis Inés and others 2011 

Cedrelinga Cateniformis Ducke Yoza and Mallque 2011 

Cherry Grimberg and others 2011 

Cherry (Prunus sar gentii Rehd. subsp. jamasakura Ohwi) Kawamoto 1996 

Chinese fir (Cunninghamia lanceolata (lamb.) Hook) Yin and others 2005 

Chrysophyllum prieurii Inés and others 2011 

Chrysophyllum venezuelanense Pires and others 2011 

Clarisia racemosa Inés and others 2011 

Croton matourensis Inés and others 2011 

Cupiúba (Goupia glabra) Gonçalves and Bartholomeu 2002 

Gonçalves and Costa 2005 

Bartholomeu and Gonçalves 2007 

Cupiúba (Goupia glabra) Gonçalves and others 2011 

Dacroides nitens Inés and others 2011 

Diplotropis purpurea Inés and others 2011 

Dipteryx micrantha Inés and others 2011 

Dipteryx odorata Trinca and others 2009 

Douglas-fir (Pseudotsuga menziesiii) Kunesh 1978 

Lemaster and others 1993 

Anthony and Phillips 1991 

Beall and Biernacki 1991 

Anthony and Phillips 1993 

Diebold and others 2000 

Ballarin and others 2002 

Seeling 2002 

Eastern cottonwood (Populus deltoids) Brancheriau and others 2011 

Endopleura uchi Pires and others 2011 

English oak (Quercus robur L.) Dolwin and others 2000 

Eucalipto (Eucalyptus saligna) Gonçalves and others 2011 

Eucalyptus citriodora Bartholomeu and Gonçalves 2007 

Gonçalves and others 2009 

Bertoldo and others 2013 

Freitas and others 2013 

Ultrasonic-Based Nondestructive Evaluation Methods for Wood

Rosner 2011 

Sanabria and others 2011 

van der Beek and Tiitta 2011 

Sterculia frondosa Inés and others 2011 

Sweet chestnut (Castanea sativa Mill.) Dundar and others 2013 

Iñiguez-Gonzalez and others 2013 

Vásquez and others 2013 

Swietenia sp. Karlinasari and others 2011 

Sycamore Grimberg and others 2011 

Sycamore fig (Ficus sycomorus) Dundar and Ross 2011 

Tabebuia serratifolia Inés and others 2011 

Tepa (Laureliopsis philipiana) Baradit and Niemz 2011 

Tetragastris panamensis Inés and others 2011 

Toona ciliata Pedroso and others 2011 

Ulmus pumila Song and others 2011 

Unonopsis floribunda Inés and others 2011 

Vatairea sericea Pires and others 2011 

Vochysia lomatifolia Inés and others 2011 

Western hemlock Hamm and Lam 1987, 1991 

White fir (Abies concolor) Berdnt 2002 

Xylopia nitida Inés and others 2011 

Yellow pine Groom 1991 
General Technical Report FPL–GTR–238

44

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Ultrasonic-Based Nondestructive Evaluation Methods for Wood

53

Chapter 4

Proof Loading

Roy F. Pellerin, Professor Emeritus

Washington State University, Pullman, Washington

Robert J. Ross, Supervisory Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

This chapter provides a brief discussion of the topic of proof 

loading, including some of the earliest research published 

on the topic. The concept of proof loading is presented, and 

some original equipment development efforts. Much of the 

early work was conducted at Washington State University 

(Pellerin and Strickler 1971, 1972, 1977; Strickler et al. 

1970; Strickler and Pellerin 1971, 1973, 1976). Subsequent 

to the foundation work, several excellent studies have been 

conducted on the topic. A listing of the publications that 

present these findings is included under the heading “Additional Sources of Information.”

Concept

Structural end jointing of lumber has become a widespread 

practice, and high-strength end joints now permit the design of structures with long wood members that would not 

otherwise be considered feasible. Nevertheless, engineers 

and architects are often hampered in designing with wood 

because of the lack of complete quality assurance.

If the structural integrity of an efficiently designed building 

were to rely upon any one member, the engineer would need 

to know without question that the member would withstand 

a minimum known level of stress indefinitely.

Although present lumber stress grades assigned nondestructively by machine stress rating (MSR) have proven to 

be significantly more reliable than visually graded lumber 

grades, MSR systems now in commercial use are insensitive 

to the quality of end joints—except in the case of extremely 

weak joints. Other methods of nondestructive evaluation 

(NDE) of lumber have also proven to be unresponsive to 

end-joint properties. These methods include transverse vibration, longitudinal stress wave, and microwave systems. 

Consequently, the remaining method that appears to offer 

potential for complete quality assurance for end joints is a 

proof load.

The principle of a proof-load system is that each end joint or 

complete member is stressed to the maximum of its design 

limitation, times a factor, before the member is accepted 

for a critical-stress application. Pieces having characteristics, natural or otherwise, that preclude them from actually 

sustaining the intended working stress are fractured by 

the proof load and as a result are rejected. Proof loading, 

in a sense, serves to identify pieces of wood with superior 

strength properties that may then be used for higher design 

purposes. Consequently, in terms of structural integrity, 

much greater confidence can be placed in members that successfully withstand a proof load than in members that have 

not been subjected to proof loading.

As a practical example, the proof-load principle proved beneficial during fabrication of long critical-stress members for 

experimental houses. Finger joints in members intended as 

bottom chords in plywood-web I-beams were proof loaded 

before the beams were fabricated. Two finger joints broke 

under proof loading. They were shown later to have been 

poorly bonded, probably because of malfunction of the 

finger-joint machine. From the standpoint of all concerned, 

including the builder and homeowner, it was better to have 

proof loaded all members and broken some than to install 

poorly joined members in critical stress locations in the 

house.

As a result of experiences such as this, proof loading of 

end joints was embraced as an area for research, despite 

aversion to the principle in some quarters. This aversion is 

understandable—it stems from fear among some wood engineers that a proof load might cause incipient damage that 

would lead to early failure of members in service. Therefore, one purpose of research was to determine whether or 

not a weakening of end-jointed members would occur as a 

result of proof loading. A second purpose was to initiate an 

evaluation of practical methods of proof loading structural 

end-jointed lumber.

Proof-Loading Considerations

Two stress modes for proof loading of end-jointed dimension lumber are considered in this chapter: tension parallel 

to grain and flatwise bending.

In any practical proof-load system, the possibility of breaking or otherwise damaging or downgrading an excessive 

amount of material must be avoided. This possibility could 

be minimized by using only MSR lumber. The small amount 

of material that would normally break under proof loading 

could be salvaged by end-joining undamaged portions. 
General Technical Report FPL–GTR–238

54

Furthermore, only critical-design members would require 

proof loading in practice. Familiar examples of critical 

members are bottom chords in trusses and box beams and 

outer tensile plies of laminated beams.

Tension Proof Loading

In the first attempt to research tension proof loading, researchers reasoned that the proof-load factor must be 2.1 

(because the primary stress in the critical outside lamination 

of a beam was in tension) and that the minimum ultimate 

strength of the beam had to be 2.1 times the design stress.

The resultant strengths of beams fabricated from laminations that were proof loaded to a tensile stress of 2.1 times 

design stress are shown in Figure 4.1. These results made 

it apparent that the proof-load factor could be lowered to 

near 1 and still result in adequate beam strengths. Table 4.1 

shows how a single proof-load value could be used to evaluate many proof-load factors. For example, proof loading to 

approximately 3,000 lb/in2 would result in a proof load factor of 1.4 for a design value of 2,200 lb/in2

. After arriving at 

a proof-load factor of 1.4, another series of beams was fabricated. The resultant stress values (Fig. 4.2) show that the 

beams maintained the minimum allowable level.

The first commercial use of this tension proof-loading concept was in 1974 by Hood Industries in Laural, Mississippi. 

The company realized the need for such a concept because 

of customer complaints about joint failures. Since adopting 

the tension proof-loading concept, they claim to have had no 

complaints and in fact have used the concept to great marketing advantage. The second commercial installation (of a 

machine developed by Metriguard, Inc., Pullman, Washington) was by St. Regis in Libby, Montana.

The Hood Industries installation was out of line so that 

joints may adequately cure prior to proof loading, whereas 

the St. Regis in-line installation stresses the joints to the 

desired proof-load level after approximately 10 s out of the 

radiofrequency (RF) tunnel.

A portable model of a tension proof-loading machine was 

developed for the American Institute of Timber Construction (AITC). This machine was air transportable in three 

packages. AITC used this machine to qualify their member 

plants for minimum tension strength end joints.

Subsequently, Suddarth (1985) of Purdue University reported that strength performance of I-beams could be improved 

by a factor of 1.4 if built with tension proof loaded lumber.

Bending Proof Loading

Although results of research on tension proof loading demonstrated that significant improvements in allowable design 

properties of laminated beams can be obtained from proof 

loading and that commercial machines have performed adequately, the laminating industry, through AITC, requested 

research on a bending proof-load concept.

Figure 4.1—Properties of laminated beams fabricated from 

laminations proof loaded to 2.1 times design stress.

Figure 4.2—Properties of laminated beams fabricated from 

laminations proof loaded to 1.4 times design stress.

The goals of a bending proof-load concept were to

•	 improve the reliability of glued-laminated timbers by 

eliminating the occasional low-strength end joint,

•	 eliminate the need for spacing end joints within the 

beam when approved proof-loading procedures are followed, and

Chapter 4
Nondestructive Evaluation of Wood: Second Edition

55

•	 make it easier for glued-laminated timber to comply 

with reliability based procedures when the need arises. 

(In Canada, this is known as the limit states design 

procedure.)

The immediate objective of this research was to develop 

data and criteria, so that proof loading by bending could be 

incorporated into a laminating plant’s production and quality 

control systems.

To achieve these goals and objectives, it was necessary to 

have information on the effects of proof loading on lumber, 

fully cured finger joints, hot finger joints, bending strength 

of fully cured joints that had been proof loaded while hot, 

tensile strength of fully cured joints that had been proof 

loaded while hot, and various configurations of finger joints 

(including horizontal and vertical).

Table 4.2 shows average ultimate values for lumber stressed 

in bending, tension, and tension after being proof loaded in 

bending. The resulting proportion of ultimate tensile stress 

to ultimate bending stress for lumber is 60%. Typical data 

for finger-jointed lumber are summarized in Table 4.3. The 

resulting proportion of ultimate tensile stress to ultimate 

bending stress for lumber containing finger joints is also 

60%.

The objective of the second phase of the bending study was 

to determine the strength ratio of partially cured end joints 

to fully cured end joints. The procedure for this phase included the selection of a random sample of visually graded 

lumber for use in laminated beams designated as 301–

24 Douglas-fir lumber from sources in southwest Oregon to 

northwest Washington. This lumber was used to make test 

specimens containing either a horizontal or a vertical finger 

joint. Sixty matched pairs of specimens of each finger-joint 

orientation were subjected to two different procedures. In 

the first, the specimens were assembled, cured by RF energy, and allowed to fully cure. In the second, specimens 

were assembled, cured by RF, and tested to failure in bending within 10 s after the RF generator had shut off. The fully 

cured specimens were then tested to failure in bending at 

the same rate of loading used for the partially cured joints. 

Table 4.1—Single proof-load value (lb/in2) used to evaluate many 

proof-load factors

Proof-load factor

f 1.0 1.1 1.2 1.3 1.4

2,000 2,000 2,200 2,400 2,600 2,800

2,200 2,200 2,420 2,640 2,860 3,080 

2,400 2,400 2,640 2,880 3,120 3,360

2,600 2,600 2,860 3,120 3,380 3,640

2,800 2,800 3,080 3,360 3,640 3,920

3,000 3,000 3,300 3,600 3,900 4,200

3,300 3,300 3,630 3,960 4,280 4,620

Table 4.2—Average ultimate values for lumber stressed in 

bending, tension, and tension after being proof loaded in 

bending

Mode of test

Average 

ultimate stress

(lb/in2

)

Bending 9,066

Tension 5,413

Tension after 6,000-lb/in2 bending proof load 5,403

Table 4.3—Average ultimate values for finger-jointed 

lumber stress in bending, tension, and tension after

being proof loaded in bending

Mode of test

Average 

ultimate stress 

(lb/in2

)

Bending 9,556

Tension 5,724

Tension after 6,000-lb/in2 bending proof load 5,927

Proof Loading
General Technical Report FPL–GTR–238

56

Results are summarized in Table 4.4. Ratios of hot to cold 

strengths for both horizontal and vertical joints are reported 

in Table 4.5.

The objective of the third phase was to determine if bending proof loading of the hot joint affects final cold strength 

of the joints. Three more groups, 60 specimens each, of the 

vertical finger joints were made. Two groups were proof 

loaded immediately out of the RF energy and then allowed 

to fully cure. The third group was assembled and allowed 

to fully cure without proof loading. Tensile strengths were 

determined for the group allowed to fully cure without proof 

loading and for one of the groups that had been proof loaded 

while hot.

Finally, a series of test beams were fabricated with the remaining group of laminations that had been proof loaded in 

bending for finger-joint quality assurance. The beams were 

layed-up so that finger joints in the three outermost tension 

laminations were stacked. The resulting beam properties are 

reported in Table 4.6.

Based on the results of this study and the commitment of 

AITC to proof loading, Mann-Russell Electronics (Tacoma, 

Washington) developed a bending proof loading machine 

for commercial application.

Literature Cited

Pellerin, R.F.; Strickler, M.D. 1971. Tension proof loading 

of lam stock for laminated beams. Forest Products Journal. 

21(5): 50–55.

Pellerin, R.F.; Strickler, M.D. 1972. Proof loading of tension 

laminations for large glued-laminated beams. Forest Products Journal. 22(10): 24–30. 

Pellerin, R.F.; Strickler, M.D. 1977. S. pine beams from 

E-rated and proof loaded lumber. Proceedings Paper 12656. 

Journal of the Structural Division, American Society of 

Chemical Engineers. 103(ST1): 270–274.

Strickler, M.D.; Pellerin, R.F. 1971. Tension proof loading 

of finger joints for laminated beams. Forest Products Journal. 21(6): 19–24. 

Strickler, M.D.; Pellerin, R.F. 1973. Rate of loading effect 

on the tensile strength of wood parallel to grain. Forest 

Products Journal. 23(10): 34–36.

Strickler, M.D.; Pellerin, R.F. 1976. Tension proof loading 

for Southern pine beams. Proceedings Paper 11978. Journal 

of the Structural Division, American Society of Chemical 

Engineers. 102(ST3): 645–657.

Table 4.4—Hot and cold ultimate bending stresses of both vertical 

and horizontal finger joints and time to fail hot joints after removal

from RF energy source

Type of joint

Joint 

condition

Average bending stress 

(lb/in2

) 

Time to failure

(s)

Vertical Hot 

Cold

5,145 

9,672

7.27 

—

Horizontal Hot 

Cold

4,408 

8,484

6.65 

—

Table 4.5—Proportions of hot to cold ultimate

bending stresses for both horizontal and 

vertical joints

Type of joint Proportion (%)

Vertical 53

Horizontal 52

Table 4.6—Properties of beams fabricated from proof-loaded 

laminations and with stacked joints in the three outermost 

tension laminations

Proof load 

Beam series

No. of 

laminations

Stress level 

 (lb/in2

)

Average ultimate 

 stress (lb/in2)

1.8–1 2 2,640 5,625

1.8–2 3 2,640 6,580

2.2–1 2 3,640 6,540

2.2–2 2 3,920 7,010

Chapter 4
Nondestructive Evaluation of Wood: Second Edition

57

Strickler, M.D.; Pellerin, R.F.; Talbott, J.W. 1970. Experiments in proof loading structural end-jointed lumber. Forest 

Products Journal. 20(2): 29–35. 

Suddarth, S.K. 1985. Improved strength performance of 

I-beams built with proof tested lumber. In: Proceedings, 5th 

Symposium on Nondestructive Testing of Wood; 1985 September 9–11; Pullman, WA. Pullman, WA: Washington State 

University: 549–559.

Additional Sources of Information 

Chen, Y.; Barrett, J.D.; Lam, F. 2009. Mechanical properties 

of Canadian coastal Douglas-fir and hem-fir. Forest Products 

Journal. 59(6):44–54.

Chen, Y.; Lam, F; Barrett, J.D. 2006. Bending strength and 

modulus of elasticity of BC coastal timbers. Proceedings, 

9th World Conference on Timber Engineering, 9th World 

Conference on Timber Engineering, August 6–10, 2006. 

Volume 1: 544–548.

Enjily, V.; Fewell, A.R. 1992. The extent of damage in British grown Sitka spruce timber caused by proof loading. 

Journal of the Institute of Wood Science. 12(5): 279–284.

Evans, J.W.; Johnson, R.A.; Green, D.W. 1984. Estimating 

the correlation between variables under destructive testing, or how to break the same board twice. Technometrics. 

26(3): 285–290.

Galligan, W.L.; Johnson, R.A.; Taylor, J.R. 1979. Examination of the concomitant properties of lumber. Proceedings, 

Metal Plate Wood Truss Conference, FPRS Proceedings. 

79–28. Forest Products Society, Madison, WI. 65–70.

Gerhards, C.C. 1977. Time-related effects of loads on 

strength of wood. Proceedings, Environmental Degradation 

of Engineering Materials. 613–623. 

Henery, R.J.; Ahmed, E. 1992. Using the moment generating function to estimate the correlation coefficient by the 

method of proof loading. American Journal of Mathematical 

and Management Sciences. 12(4): 301–319.

Lackner, R. 1990. Strength and grading of construction timber in small cross-sections. Hozforschung und Holzverwertung. 42(3): 43–48.

Lam, F.; Abayakoon, S.; Svensson, S.; Gyamfi, C. Influence 

of proof loading on the reliability of members. Holz als 

Roh-und Werkstoff. 61(6): 432–438.

McLain, T.E.; Woeste, F.E. 1988. Proof test damage evaluation with southern pine lumber. Forest Products Journal. 

38(5): 31–32.

Marin, L.A.; Woeste, F.E. 1981. Reverse proof loading as

 a means of quality control in lumber manufacturing. 

American Society of Agricultural Engineers, Transactions. 

24(5): 1273–1277, 1281. 

Takeda, T.; Hashizume, T. 2000. Effective sampling method 

for estimating bending strength distribution of Japanese 

larch square-sawn timber. Journal of Wood Science. 46(5): 

350–356.

Umble, E.J.; Seaman, J.W.; Martz, H.F. 1999. A distribution-free Bayesian approach for determining the joint probability of failure of materials subject to multiple proof loads. 

Technometrics. 41(3): 183–191.

Woeste, F.E.; DeBonis, A.L.; McLain, T.E.; Perumpral, J.V. 

1984. Bending proof load as a quality control for compression parallel-to-grain in lumber. Transactions of the American Society of Agricultural Engineers. 27(6): 1859–1861.

Woeste, F.E.; Green, D.W.; Tarbell, K.A. 1984. Proof loading of lumber for quality control. American Society of 

Agricultural Engineers, Microfiche collection, Winter 1984, 

Paper no. 84-4509. 

Woeste, F.E.; Green, D.W.; Tarbell, K.A.; Marin, L.A. 1987. 

Proof loading to assure lumber strength. Wood and Fiber 

Science. 19(3): 283–297.

Ziethen, R. 2006. The reliability of proof-loading as a 

strength grading technique. Proceedings, 9th World Conference on Timber Engineering, Volume 1, 152–159.

Ziethen, R. 2008. Reliability of proof-loaded Norway 

spruce. Holz als Roh-und Werkstoff. 66(6): 401–408.

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strength grading—“output control” as a method for production control. Proceedings, 11th World Conference on Timber 

Engineering, Volume 1: 763–769.

Proof Loading

59

Chapter 5

Sounding, Probing, Moisture Content, 

and Resistance Drilling Techniques

Brian K. Brashaw, Program Manager

Forest Products Marketing Unit, USDA Forest Service, Madison, Wisconsin

Simple mechanical tests are frequently used for in-service 

inspection of wood elements in timber structures. For example, hammer sounding and probing are used in combination with visual inspection to conduct an initial assessment 

of the condition of a member. The underlying premise for 

such tests is that degraded wood is relatively soft and might 

sound hollow, with low resistance to penetration. 

This chapter was originally published in 2014 as chapter 2 

of the Wood and Timber Condition Assessment Manual, 

Second Edition, a Forest Products Laboratory General 

Technical Report:

White, Robert H.; Ross, Robert J. (Eds.). 2014. Wood 

and timber condition assessment manual: second 

edition. General Technical Report FPL–GTR–234. 

Madison, WI: U.S. Department of Agriculture, 

Forest Service, Forest Products Laboratory. 93 p.

Sounding and Probing 

One of the most commonly used techniques for detecting 

deterioration is to hit the surface of a member with a hammer or other object. Based on the sound quality or surface 

condition, an inspector can identify areas of concern for 

further investigation using advanced tools like a stress 

wave timer or resistance microdrill. Deteriorated areas typically have a hollow or dull sound that may indicate internal 

decay. A pick hammer commonly used by geologists is 

recommended for use in timber structures because it allows 

inspectors to combine the use of sound and the pick end to 

probe the element (Fig. 5.1). 

Probing with a moderately pointed tool, such as an awl or 

knife, locates decay near the wood surface as indicated by 

excessive softness or a lack of resistance to probe penetration and the breakage pattern of the splinters. A brash, or 

brittle break indicates decayed wood, whereas a splintered 

break indicates sound wood. Although probing is a simple 

inspection method, experience is required to interpret results. Care must be taken to differentiate between decay and 

water-softened wood, which may be sound but somewhat 

softer than dry wood. Assessing damage in soft-textured 

woods, such as western redcedar, is sometimes difficult. 

Figure 5.2 shows an awl probe inserted into a split to assess 

decay that is visible on the railing end.

Figure 5.1—A hammer pick is an effective tool for initial 

assessments of timber bridge elements.

Figure 5.2—An awl is used to assess depth and presence of 

decay in a horizontal split.

Probes can also be used to assess the depth of splits and 

checks in timber members. Flat bladed probes, such as 

pocket knives or feeler gauges, are recommended for use 

in this process. This is also important to understanding the 

effect of checks and cracks in other advanced techniques 

such as stress wave inspection. Figure 5.3 shows the use of 

probes to assess the depth of checks and cracks in timber 

bridge elements.
General Technical Report FPL–GTR–238

60

Screw withdrawal testing has been used as an indicator of 

biological deterioration in timber members. Additional research has correlated screw withdrawal resistance to physical and mechanical properties such as density, modulus of 

elasticity, modulus of rupture, and shear modulus. These 

tests are typically conducted by extracting a screw inserted 

at an angle perpendicular to the surface of the timber specimen. Although these tests are dependent on local material 

condition, information from both deteriorated and good 

material locations can be helpful during wood structure 

evaluations. 

A quantitative test based on the premise that underlies 

mechanical probing tests (relative softness of degraded 

wood and consequent low resistance to probe penetration) 

was developed by Talbot (1982). His test differed from the 

probing-type test in that instead of evaluating penetration 

resistance of a probe, it examined withdrawal resistance 

of a threaded probe, similar to a wood screw, inserted into 

a member. Talbot believed that a correlative relationship 

between withdrawal resistance and residual strength should 

exist and would be relatively easy to determine. He conducted an experiment using several small Douglas-fir beams 

in various stages of degradation as a result of exposure to 

decay fungi. Prior to testing the wood to failure in bending, 

probe withdrawal resistance was measured at the neutral 

axis of the beams. Bending strength and corresponding 

probe resistance values were then compared. The results revealed a relationship between withdrawal resistance and residual strength (Fig. 5.4). Talbot used this test in conjunction 

with stress wave techniques to assess the extent of damage 

to solid-sawn timbers in the football stadium at Washington 

State University.

A modification to Talbot’s technique was developed for use 

in evaluating in-service strength of fire-retardant-treated 

(FRT) plywood in the early 1990s. A portable screw withdrawal system was utilized to inspect FRT plywood and 

identify substantial degrade. An extensive research effort 

was devoted to the use of Talbot’s test for assessing residual 

strength of in-service panel products treated with fire retardant. Winandy et al. (1998) designed and conducted tests 

on a large sample of plywood specimens that were treated 

with fire-retardant chemicals and subsequently exposed to 

elevated temperatures. Screw withdrawal loads were determined for each specimen prior to static bending tests. Correlative relationships were then established between screw 

withdrawal strength and static bending properties (Fig. 5.5).

Recent evaluations have been completed by other researchers and commercial companies (Cai et al. 2002, Fakopp 

2014). Figure 5.6 shows one commercial screw withdrawal 

resistance unit.

Moisture Content Inspection

Moisture meters can be used effectively in inspecting timber 

elements. The presence of moisture is required for decay 

to occur in timber. Typically, moisture conditions in timber 

of less than 20% will not allow decay to occur; as moisture 

increases above 20%, the potential for decay to occur 

increases.

Serious decay occurs only when the moisture content of the 

wood is above 30%. This occurs when dry wood is exposed 

to direct wetting through rain, moisture infiltration, or contact with ground water or bodies of water. Wood decay fungi 

will not affect wood that is fully saturated with water and 

without oxygen. Timber members (such as bridge piling) 

should be carefully inspected near the water line because 

rivers and streams have varying water levels throughout 

the year and from year to year. Figure 5.7 shows the use of 

Figure 5.3—Probes are used to assess depth of cracks, 

checks, and through splits in timber bridge elements.

Figure 5.4—Relationship between withdrawal resistance 

and residual strength. MOR, modulus of rupture. 

Chapter 5
Nondestructive Evaluation of Wood: Second Edition

61

moisture meters with long insulated pins (up to 3 in. 

(75 mm) long) to assess moisture content of timber abutment caps. Pin-style moisture meters measure electrical 

resistance between two metal pins that are driven into the 

member and use this measurement to determine moisture 

content. 

Drilling

Drilling and coring are the most common methods used 

to detect internal deterioration in wood members. Both 

techniques are used to detect the presence of voids and to 

determine the thickness of the residual shell when voids 

are present. Drilling utilizes an electric power drill or handcrank drill equipped with a 9.5- to 19-mm- (3/8- to 3/4-in.-) 

diameter bit. Power drilling is faster, but hand drilling allows the inspector to monitor drilling resistance and may 

be more beneficial in detecting pockets of deterioration. In 

general, the inspector drills into the member in question, 

noting zones where drilling becomes easier and observing 

drill shavings for evidence of decay. The presence of common wood defects, such as knots, resin pockets, and abnormal grain, should be anticipated while drilling and should 

not be confused with decay. If decay is detected, remedial 

treatments, such as copper naphthenate, can be added to 

the wood through the inspection hole to help retard further 

decay. The inspection hole is probed with a bent wire or a 

thickness gauge to measure shell thickness. Because these 

holes are typically 3/8 to 3/4 in. diameter, they should be 

filled with a wood dowel section that has been soaked in a 

preservative.

Coring

Coring with an increment borer (often used for determining 

the age of a tree) also provides information on the presence 

of decay pockets and other voids. The resultant solid wood 

core can be carefully examined for evidence of decay. In 

addition, the core can be used to obtain an accurate measure 

of the depth of preservative penetration and retention. Figure 5.8 shows an increment core tool and the extracted core. 

The core can also be used to determine the wood species.

Figure 5.5—Relationship of screw-withdrawal force to 

bending strength for 12-mm- (1/2-in.-) thick plywood. 

MAP, mono-ammonium phosphate; PA, phosphoric 

acid; KD, dry-kiln drying temperature; and MOR, modulus of rupture. 

Figure 5.6—Screw withdrawal resistance meter that can 

be used to assess local wood quality.

Figure 5.7—A pin-style moisture meter is used to 

determine moisture content of timber elements.

Sounding, Probing, Moisture Content, and Resistance Drilling Techniques
General Technical Report FPL–GTR–238

62

To prevent moisture and insect entry, a bored-out core hole 

should be filled with a wood plug treated with copper napthenate.

Resistance Drilling

Another commercially developed drilling technique is the 

resistance drill system. Developed in the late 1980s, this 

system was originally developed for use by arborists and 

tree care professionals to assess tree rings, evaluate the condition of urban trees, and locate voids and decay. This technology is now being utilized to identify and quantify decay, 

voids, and termite galleries in wood beams, columns, poles, 

and piles. This technique is now the preferred drilling and 

coring technique for timber elements. Figure 5.9 shows a 

resistance microdrill being used to assess the level of decay 

in a timber. 

Several machine types are available from different manufacturers. They operate under the same general principle of 

measuring electrical power consumption of a needle rotation 

motor. This value is proportional to mechanical torque at the 

needle and primarily depends on wood density (Rinn 2013). 

The purpose of the equipment is to identify areas in timber 

elements that have low density, indicating decay or 

deterioration. 

Resistance drill equipment measures the resistance of 

wood members to a 0.6-in.- (1.5-mm-) diameter drill bit 

with a 0.18-in. (3.0-mm) head. This flat tipped drill bit 

travels through the member at a defined movement rate and 

generates information that allows an inspector to determine 

the exact location and extent of any damaged area. Figure 

5.10 shows several drill bit ends that are used in resistance 

drills. Although the unit is usually drilled into a member in 

a direction perpendicular to the surface, it is also possible to 

drill into members at an angle (Fig. 5.11).

Figure 5.8—An increment core can be used to conduct 

inspections of timber bridge elements. This image shows 

an extracted core from an in-service timber pile ready for 

examination.

Figure 5.9—A resistance microdrill used for inspection 

of a historic Civilian Conservation Corps log cabin.

Figure 5.10—Flat-tipped resistance drill bits used to 

inspect timber materials.

Figure 5.11—Drilling can take place at an angle to assess 

the area below ground line.

Chapter 5
Nondestructive Evaluation of Wood: Second Edition

63

Resistance drills collect data electronically and can also 

product a chart or printout showing relative resistance over 

the drilling path. Modern tools are also promoting the ability 

to view the data on a tablet computer or hand-held mobile 

phone. Areas of sound wood have various levels of resistance, depending on the density of the species; voids show 

no resistance. The inspector can determine areas of low, 

mild, and high levels of decay with this tool, and quantify 

the level of decay in the cross section. Figure 5.12 shows a 

timber abutment cap being assessed with a resistance microdrill and the resulting chart image, which shows minimal 

drilling resistance and indicates that the majority of the cap 

is decayed. Figure 5.13 shows a commercial model with an 

electronic display that allows allow data to be reviewed in 

the field and then further processed using a computer in the 

office. All holes should be filled after drilling, especially if 

no decay is present. For the microdrill, this can be accomplished by injecting a small amount of silicone sealant or 

marine adhesive into the small opening (Fig. 5.14).

Interpreting Drilling Data Charts

Charts or printouts should be reviewed in the field and notes 

taken to ensure understanding of the testing location. Notes 

should be taken on a graphical data chart. Care should be 

exercised to ensure that low density profiles from intact 

but soft wood (such as conifers) not be misinterpreted as 

decay. The very center of softwood species near the pith 

will have low resistance and lack the defined growth rings 

visible in the outer sections. It is important to understand the 

type of wood that is being drilled. Sound wood from many 

hardwood species may have high levels of resistance (over 

50%), whereas sound wood from softwood conifers may 

have low levels of resistance (in the range of 15% to 50+%, 

depending on its inherent density). It is important to evaluate the levels of decay across the full dimension, as some 

species have low resistance values but are not decayed. Further, each piece of commercial equipment provides different 

scales and may indicate different resistance levels. Table 5.1 

shows a general assessment rating index that can provide 

support for the bridge inspector in evaluating resistance data 

collected during testing. Example electronic drilling charts 

for a southern yellow pine pile and a Douglas-fir pile cap 

are shown in Figures 5.15 and 5.16, respectively. Note that 

while the fundamental concept utilized by these pieces of 

equipment is essentially the same, data analysis and subsequent display of the data can vary significantly, depending 

on equipment manufacturer.

Figure 5.12—Resistance microdrilling showing significant 

decay in the bridge pile cap. The inlay shows the paper 

chart readout from a commercial drilling unit.

Figure 5.13—Electronic display on a resistance drill.

Figure 5.14—Silicone is used to fill the small drilling hole.

Table 5.1—General assessment of resistance drilling 

data for Douglas-fir and southern yellow pine bridge 

members

Drilling 

resistance 

(%) Decay level Comments

0 Severe Decay resulting in an internal 

void

5–15 Moderate Often adjacent to the internal void 

areas

20+ Low to none Sound material often has 

resistance that is consistent across 

the full width

Note: These data must be carefully interpreted because of differences 

between commercial equipment.

Sounding, Probing, Moisture Content, and Resistance Drilling Techniques
General Technical Report FPL–GTR–238

64

Figure 5.15—Electronic view of a southern yellow pine timber piling showing a decay pocket between 

8 and 10 in. of the drilling profile.

Figure 5.16—Electronic resistance chart of a Douglas-fir pile cap showing a large crack between 

180 and 200 mm along the drilling path.

Chapter 5
Nondestructive Evaluation of Wood: Second Edition

65

Commercial Equipment

Several companies produce equipment that is suitable for 

inspecting timber structures based on these concepts. Additional details for these companies and their equipment 

follow.

Increment Borers

Forestry Suppliers Inc.

Jackson, MS 39284-8397 USA 

Telephone: (800) 647-5368

Website: www.forestry-suppliers.com

Ben Meadows Company 

Janesville, WI USA 53547–5277

Telephone: (608) 743–8001

Fax: (608) 743–8007 

Website: www.benmeadows.com

Resistance Microdrills

IML-RESI PD- and F-Series

IML North America, LLC

Moultonborough, NH 03254 USA

Telephone: (603) 253–4600

Website: www.iml-na.com

Resistograph 4- and 5-Series

RINNTECH, Inc.

St. Charles, IL 60174, USA

Telephone: (630) 377–2477

Website: www.rinntech.de

Digital microProbe

Sibtec Scientific

Sibert Technology Limited

2a Merrow Business Centre, Guildford

Surrey GU4 7WA England

Telephone: +44 1483 440 724

Fax: +44 1483 440 727 

Website: www.sibtec.com

Screw Withdrawal Resistance Meter

FAKOPP Enterprise

Agfalva, Hungary

Telephone: +36 99 33 00 99

Website: www.fakopp.com 

Literature Cited

Cai, Z.; Hunt, M.; Ross, R.J.; Soltis, L.A. 2002. Screw 

withdrawal—a means to evaluate densities of in-situ wood 

members. In: Proceedings, 13th International Symposium 

on Nondestructive Testing of Wood. Madison, WI: Forest 

Products Society: 277–281.

Fakopp. 2014. User’s guide: screw withdrawal resistance 

meter. Agfalva, Hungary: Fakopp Enterprise. http://www.

fakopp.com/site/downloads/Screw_Withdrawal.pdf. 

(Accessed April 24, 2014)

Rinn, F. 2013. From damage maps to condition inventories: 

a proven concept for documentation of results of inspection 

of timber bridges and other timber structures. International 

Conference on Timber Bridges. Las Vegas, NV. http://www.

woodcenter.org/docs/ICTB2013/technical/papers/ID_139_

Rinn.pdf

Talbot, J. 1982. Unpublished research. Pullman, WA: Wood 

Materials and Engineering Laboratory, Washington State 

University.

Winandy, J.E.; Lebow, P.K.; Nelson, W. 1998. Predicting 

bending strength of fire-retardant-treated plywood from 

screw withdrawal tests. Res. Pap. FPL–RP–568. Madison, 

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

Products Laboratory.

Sounding, Probing, Moisture Content, and Resistance Drilling Techniques

67

Chapter 6

A Review of the Piezoelectric 

Effect in Wood

Robert J. Ross, Supervisory Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

Jiangming Kan, Associate Professor

Beijing Forestry University, School of Technology, Beijing, China

Xiping Wang, Research Forest Products Technologist

Forest Products Laboratory, Madison, Wisconsin

Julie Blankenburg, Supervisory Librarian

National Forest Service Library, Forest Products Laboratory, Madison, Wisconsin

Janet I. Stockhausen, Patent Advisor

Forest Products Laboratory, Madison, Wisconsin

Roy F. Pellerin, Professor Emeritus

Washington State University, Pullman, Washington

Piezoelectricity is the charge that accumulates in certain solid materials (notably crystals, certain ceramics, and biological matter such as bone and various proteins) in response to 

applied mechanical stress. Piezoelectricity means electricity 

resulting from pressure and is the direct result of the piezoelectric effect.

Although wood is a complex biological material, it has been 

shown experimentally that wood exhibits a distinguishable 

piezoelectric effect. We conducted a worldwide literature review to examine the piezoelectric effect in wood. The goals 

of our review were to (1) examine the worldwide literature 

on the piezoelectric effect in wood and (2) summarize results of the main findings reported in the literature. The objective of this chapter is to present the results of our review.

This chapter was originally published in 2012 as a Forest 

Products Laboratory General Technical Report:

Ross, Robert J.; Kan, Jiangming; Wang, Xiping; Blankenburg, Julie; Stockhausen, Janet I.; Pellerin, Roy F. 

2012. Wood and wood-based materials as sensors—

a review of the piezoelectric effect in wood. General 

Technical Report FPL–GTR–212. Madison, WI: U.S. 

Department of Agriculture, Forest Service, Forest Products Laboratory. 9 p.

To review the state-of-the-art in the piezoelectric effect in 

wood, an extensive literature search on the piezoelectric 

effect in wood was conducted using CAB Abstracts. CAB 

Abstracts is an applied life sciences bibliographic database 

emphasizing agricultural literature that is international in 

scope. The database covers international issues in agricultural, forestry, and allied disciplines in the life sciences from 

150 countries in 50 languages. It includes English abstracts 

for most articles. Our review of abstracts covered the years 

from 1939 to 2010 and consisted of three searches of abstracts using three different sets of word descriptors. The 

first search required that the word “wood” was used and the 

word “piezo” could be anywhere in the records; this search 

yielded 12 records. Our second search of the abstracts used 

“wood” in the descriptor field and phrase “electrical properties” anywhere in the records; 334 records were discovered, 

of which 196 were selected for applicability. The third 

search we conducted used “wood” in the descriptor field 

and the truncation “piezo” anywhere in the records. Based 

on these results and several additional sources, 31 technical 

documents and 4 patents were selected for intensive review.

Technical Documents Reviewed

in a Chronological Order

1.	 Shubnikov 1946 

2.	 Bazhenov 1950 

3.	 Fukada 1955

4.	 Fukada and others 1957 

5.	 Bazhenov 1961 

6.	 Galligan and Bertholf 1963 

7.	 Fukada, E. 1965 

8.	 Galligan and Courteau 1965

9.	 Kytmanov 1967 

10.	 Lin, R.T. 1967 

11.	 Fukada 1968 

12.	 Hirai and others 1968a 

13.	 Hirai and others 1968b 

14.	 Hirai and others 1970 
General Technical Report FPL–GTR–238

68

15.	 Hirai and others 1972 

16.	 Maeda and others 1977

17.	 Hirai and Yamaguchi 1979 

18.	 Kellog 1981 

19.	 Pizzi and Eaton 1984 

20.	 Knuffel and Pizzi 1986 

21.	 Fei and Zeng 1987 

22.	 Knuffel 1988 

23.	 Hirai and others 1992 

24.	 Suzuki and others 1992 

25.	 Hirai and others 1993 

26.	 Nakai and Takemura 1993 

27.	 Suzuki and Hirai 1995 

28.	 Nakai and others 1998 

29.	 Smittakorn and Heyliger 2001 

30.	 Suzuki and others 2003 

31.	 Nakai and others 2005 

Patents Selected for Review

1.	 Best 1935 

2.	 Sanders 2001

3.	 Lammer 2006 

4.	 Churchill and Arms 2010 

Research Summary

Table 6.1 presents a summary of several of the significant 

findings in chronological order from the technical papers 

we reviewed. Note that a piezoelectric effect in wood was 

first hypothesized, and later discovered, by Russian scientists in the 1940s–1950s. Their work was initiated in an 

effort to find an appropriate trigger mechanism for military 

equipment, specifically missiles. Since then, research has 

been conducted to explore the relationships between fundamental wood characteristics and the piezoelectric effect 

observed.

Discussion

Fundamental Concepts

Piezoelectricity is the charge that accumulates in certain solid materials (notably crystals, certain ceramics, and biological matter such as bone and various proteins) in response to 

applied mechanical stress. Piezoelectricity means electricity 

resulting from pressure and is the direct result of the piezoelectric effect.

The piezoelectric effect is understood as the linear electromechanical interaction between mechanical and electrical 

state in crystalline materials. Piezoelectric effect is a 

reversible process in that materials exhibiting direct piezoelectric effect (the internal generation of electrical charge 

resulting from an applied mechanical force) also exhibit the 

reverse piezoelectric effect (internal generation of a mechanical strain resulting from an applied electrical field). 

For example, lead zirconate titanate crystals will generate 

measurable piezoelectricity when their static structure is 

deformed by about 0.1% of the original dimension. Conversely, those same crystals will change about 0.1% of their 

static dimension when an external electric field is applied to 

the material.

Piezoelectricity is found in useful applications such as the 

production and detection of sound, generation of high voltages, electronic frequency generation, microbalances, and 

ultrafine focusing of optical assemblies.

The electrical character of a piezoelectric material must be 

that of a dielectric wherein charge displacement far outweighs conduction. Thus, the material behaves according 

to the relationship: C = Q/V, where C is the capacitance 

(farads); Q the charge (coulombs); and V, the potential difference (volts).

At the molecular level, a further requirement is placed on 

the piezoelectric material; there must be planes of molecular 

symmetry and within these planes the molecular constituents must be oriented in such a manner that the electrical 

charge centers are not symmetrically located. Monocrystals 

are representative of materials that meet these requirements.

When a piezoelectric crystal is strained, the charge centers 

are displaced relative to one another, causing a net charge 

to occur on the crystal surface. The dielectric nature of the 

crystal, obeying the capacitance relationship, permits the 

charge to appear as a voltage. This voltage is the electrical 

evidence of the piezoelectric effect.

Table 6.1—Significant findings in a chronological order 

Year Author Reported findings 

1946 Shubnikov Discovery of piezo-effect in 

wood 

1950 Bazhenov and Konstantinova First reported experiments 

on piezoelectricity in wood 

1955 Fukada Inverse piezoelectric effect 

in wood 

1963 Galligan and Bertholf Use of piezoelectric textures 

to observe stress wave behavior

in wood 

1970 Hirai and others Effects of tree growth, wood 

quality, degree of crystallinity,

and micellar orientation 

1984 Pizzi and Eaton Correlation between molecular

forces in cellulose I crystal 

and piezoelectric effect 

1993 Nakai and Takemura Species, grain orientation 

effects 

1998 Nakai and others Relationship to static 

and vibration properties 

2005 Nakai and others Relationship to crystal lattice 

strain and tension stress 

of individual wood fibers 

Chapter 6
Nondestructive Evaluation of Wood: Second Edition

69

The fundamental equations that describe the relationship 

between mechanical stress and electrical charge are the 

following:

where a stress S is given to a substance, a polarization P is 

produced.

At the same time, an electric field E is also caused by the 

polarization of the substance. The coefficient d is called 

the piezoelectric modulus and η the electric susceptibility. 

The converse effect is shown by the second equation. A 

mechanical strain γ is produced by an applied electric field 

E and is accompanied by a stress S. The coefficient d for the 

converse effect is the same as that for the direct effect. If 

the condition is made that E = 0, then, by an experimental 

procedure, the modulus d can be determined as a ratio of 

polarization P to stress S. Thus relations between electrical 

polarization and mechanical stress are generally given the 

following equations:

Where Px,Py, Pz represent the polarizations in xx, yy, and 

zz-directions, Syz, Szx, and Sxy represent the shear stress in yz, 

zx, and xy planes, respectively. The piezoelectric modulus dij

relates each component of the polarization to each component of stress. In general, there are 18 components of dij

and they are represented by a piezoelectric tensor as 

follows: 

By examination of the geometrical relationship between 

applied stress and the resulting polarization in wood, the 

piezoelectric tensor for wood has been determined as 

follows:

The modulus d14 means that shear stress in the yz plane 

produces polarization in the x-direction, and the modulus 

d25 means that shear stress in the zx plane produces polarization in the y-direction. Experimentally, their magnitudes are 

nearly the same and their sign is opposite. This fact makes 

evident that the piezoelectric effect is symmetrical about the 

z-axis.

Characteristics of piezoelectricity considered above apply 

directly to monocrystalline materials. Piezoelectricity of 

wood cannot be discussed easily in this context; while the 

same fundamental relations are believed applicable, it is 

necessary to consider the extremely heterogeneous nature of 

wood. Shubnikov (1946) noted this fact in some of the first 

reported work on piezoelectricity of wood; he proposed 

the concept of “piezoelectric texture” to represent a 

system consisting of many crystalline particles oriented 

unidirectionally.

The piezoelectric effect in wood may be observed as illustrated in Figure 6.1. The z-axis represents the fiber direction 

in wood. If a shearing stress is applied as indicated by arrows, an electrical polarization takes place in the direction 

perpendicular to the plane of the stress. The sign of the 

value of polarization is reversed when the direction of shear 

is reversed.

Rectangular coordinates are assigned to the wood structure 

with the z, x, and y axes representing the longitudinal, 

radial, and tangential directions in a tree trunk, respectively 

(Fig. 6.2).

It is known that cellulose is crystallized to a fairly large 

extent and that the unit cell of cellulose crystal belongs to 

monoclinic symmetry C2. The piezoelectric tensor for a 

crystal is determined by the symmetry of a crystal lattice 

(Fukada 1968).

The tensor for a crystal with the symmetry C2 is

where the zz-axis is taken in the direction of the longitudinal 

axis of the molecules in the crystal. Eight components of the 

piezoelectric modulus should be finite.

The structure of wood composed of cellulose fiber is very 

complicated. Assume that the fiber is composed of many 

numbers of cellulose crystallites, orientated in the same 

direction, is the fiber axis, and that such fibers are regularly 

orientated parallel to the trunk axis. Figure 6.3 illustrates the 

P = dS + ηE

γ = JS + dE

Px = d11Sx + d12Sy + d13Sz + d14Sxy + d15Syz + d16Szx

Py= d21Sx + d22Sy + d23Sz + d24Sxy + d25Syz + d26Szx

Pz= d31Sx + d32Sy + d33Sz + d34Sxy + d35Syz + d36Szx

d11 d12 d13 d14 d15 d16

d21 d22 d23 d24 d25 d26

d31 d32 d33 d34 d35 d36

0 0 0 d14 0 0 

0 0 0 0 d25 0

0 0 0 0 0 0 

z z

Figure 6.1—General scheme to produce piezoelectric 

polarization in wood (Fukada 1968). (Graphic used by 

permission of Washington State University, Pullman.)

 0 0 0 d14 d15 0 

 0 0 0 d24 d25 0

d31 d32 d33 0 0 d36

A Review of the Piezoelectric Effect in Wood
General Technical Report FPL–GTR–238

70

uniaxial orientation of cellulose crystallites. The positive 

end of the zz-axis of each crystallite is distributed at random 

in the axis of symmetry, that is, with the same probability 

for two opposite directions. The xx-axis of each crystallite is 

distributed at random and uniformly in the plane perpendicular to the axis of symmetry. The piezoelectric modulus for 

such an assembled system of crystallites can be calculated 

by taking an average of the moduli of the crystallites. Then 

it turns out that only d14 and d25 are finite for the system and 

that the other moduli become zero due to cancellation of the 

effect. The values of d14 and d25 of the system are proportional to the mean value of d14 and d25 in the single crystal 

of cellulose. The coefficient of proportion is dependent on 

density, crystallinity, and degree of orientation.

The piezoelectric tensor for such an assembly of unidirectionally orientated crystallites is

This tensor form is identical to that experimentally determined for wood.

Cellulose fibrils twist spirally with a certain angle to the 

longitudinal axis of the cell. However, if the average is 

taken for the layers in which fibrils describe a spiral form 

in alternative directions, the form of the resultant tensor of 

piezoelectric modulus is the same as derived above.

Since polymeric substances possess a viscoelastic property, it is anticipated that when stress is applied, electrical 

polarization does not appear instantly but arises gradually 

with time. Therefore, the piezoelectric modulus is treated as 

a complex quantity and determines the phase lag between 

stress and polarization as well as the absolute value of the 

modulus.

Figure 6.4 represents stress and polarization in a vector 

diagram. The polarization is delayed behind the stress by an 

angle δd. The component of polarization in phase with the 

stress represents the real part of modulus d’ and the component of polarization 90o out of phase with the stress of the 

imaginary part of modulus d”. The ratio of d” to d’ may be 

expressed as the tangent of δd. These relationships are very 

similar to those encountered with the complex mechanical 

compliance and the complex dielectric constant.

Baseline Studies

Table 6.2 provides a summary of the species used in several 

reported studies. Note that a wide range of species has been 

used in these studies, and all have exhibited a piezoelectric 

effect. Reported moisture content values of the specimens 

used in the studies varied considerably; from a relatively dry 

state (below 10%) to over 70%. The specimens used were 

relatively small, with any dimension not exceeding 60 mm. 

Galligan and Courteau (1965), Knuffel (1988), and Knuffel 

z

x

y

Figure 6.2—Rectangular coordinates 

assigned to wood (Fukada 1968). 

(Graphic used by permission of 

Washington State University, Pullman.)

z

z

x

x

Figure 6.3—Uniaxial and non-polar 

orientation of crystallites (Fukada 

1968). (Graphic used by permission 

of the Journal of Wood Science and 

Technology.)

Stress

Polarization

δd

d '

d "

Figure 6.4—Vector representation of stress 

S and polarization P (Fukada 1968). (Graphic 

used by permission of the Journal of Wood 

Science and Technology.)

0 0 0 d14 0 0 

0 0 0 0 –d14 0

0 0 0 0 0 0 

Chapter 6
Nondestructive Evaluation of Wood: Second Edition

71

Table 6.2—A list of wood species investigated for piezoelectric effect in previous 

studies 

Reference Species 

Fei and Zeng 1987 Magnolia grandiflora Linn 

Tilia amurensis Rupr 

Taxodium ascendens Brongn 

Pinus massoniana Lamb 

Cunninghamia lanceolata Hook 

Fukada and others 1957 (10 old timbers from 8 years to 1,300 years) 

Galligan and Courteau 1965 Douglas-fir 

Hirai and others 1968 Tsuga (Tsuga sieboldii Carr.) 

Shioji (Fraxinus mandshurica Rupr.) 

Shirakaba (Betula platyphylla SUKATCHEV. var.; japonica HARA.) 

Hônoki (Magnolia obovata THUNB) 

Taiwanhinoki (Chamaecyparis taiwanensis MASAM. et SUZUKI) 

Kiri (Paulownia tomentosa STEUD.) 

Hinoki (Chamaecyparis obtuse ENDL.) 

Sugi (Cryptomeria japonica D. DON) 

Konara (Quercus serrata MURRAY.) 

Akamatsu I (Pinus densiflora SIEB. et ZUCC.) 

Akamatsu II 

Douglas-fir (Pseudotsuga taxifolia BRITT.) 

Makanba 

Hirai and others 1970 Sugi (summerwood and springwood) 

Hirai and others 1972 Hinoki tree (Chamaecyparis obtuse SIEB. et ZUCC) 

Hirai and Yamaguchi 1979 Hinoki 

Knuffel and Pizzi 1986 Pinus patula

Knuffel 1988 Pinus patula 

P. taeda 

P. elliottii 

Maeda and others 1977 Japanese cedar

Nakai and Takemura 1993 Beisugi (Thuja plicata Donn) 

Hinoki (Chamaecyparis obtuse ((S. and Z.)) Endl.) 

Beitsuga (Tsuga heterophylla ((Raf.)) Sarg.) 

Beimatsu (Pseudotsuga menziesii ((Mirb.)) Franco) 

Buna (Fagus crenata Bl.) 

Nakai and others 1998 Sitka spruce (Picea sitchensis Carr.)

Nakai and others 2005 Japanese cypress (Chamaecyparis obtuse Endl.)

Suzuki and others 1992 Hinoki (Chamaecyparis obtuse ((S. and Z.)) Endl.) 

Beimatsu (Pseudotsuga menziesii ((Mirb.)) Franco) 

Beihiba (Chamaecyparis nootkatensis ((D. Don)) Spach) 

Agathis (Agathis sp.) 

Igem (Podocarpus imbricatus Bl.) 

Momi (Abies firm S. and Z.) 

White fir (Abies alba Mill.) 

Spruce (Picea pungens Engelm) 

Shinanoki (Tilia japonica Simk) 

Katsura (Cercidiphyllum japonicum S. and Z.) 

Buna (Fagus crenata Bl.) 

Lauan (Pentacme contorta Merr. and Rolfe) 

Nato (Palaquium sp.) 

Matoa (Pometia pinnata Forst.) 

Sugar maple (Acer saccharum Marsh.) 

Suzuki and Hirai 1995 Chamaecyparis botusa Endlicher 

Larix leptolepis Gordon 

Magnolia ovobata Thunberg

A Review of the Piezoelectric Effect in Wood
General Technical Report FPL–GTR–238

72

and Pizzi (1986) were exceptions—they used lumber size 

specimens in their experiments.

Most reported work used test setups that resulted in a uniform compressive stress being applied to the specimen, orienting each specimen so that the angle between growth rings 

and the application of load was approximately 45o. The 

electric charge generated was detected by electrodes that 

consisted of conductive paint, glued-on metal foil, pins, or 

small metal buttons placed against a specimen’s surface.

Based on early experimentation by Bazhenov (1961), Fukuda (1955, 1965), and Hirai and others (1970), the magnitude of the piezoelectric modulus of wood is approximately 

1/20 of that of a quartz crystal. Bazhenov (1961) and Hirai 

and others (1970) found that the values of the piezoelectric 

modulus, d14, increased gradually from the pith to the bark 

of a tree. They also reported that the values of the piezoelectric modulus for springwood and summerwood, for the same 

year’s growth, were nearly equal.

Fukada and others (1957) found that the piezoelectric 

moduli increased with increasing density. Bazhenov (1961) 

found that the piezoelectric modulus d25 increased and that 

of d14 decreased with increasing density in pines. Hirai and 

others (1968a) confirmed that the d25 piezoelectric modulus 

increased with increasing density, but they show no data 

for d14.

Bazhenov (1961) found that the piezoelectric modulus is 

related to temperature, and it increases as temperature increases. Maeda and others (1977) found that d′ of the piezoelectric constant of the Japanese cedar at 74% of moisture 

content increased with the increasing temperature for the 

piezoelectric constant determined at 10 Hz.

Smittakorn and Heyliger (2001) developed a theoretical 

model for the steady-state and transient behavior of adaptive 

wood composite plates composed of layers of wood and other piezoelectric materials to simultaneously study the effects 

of mechanical, electrical, temperature, and moisture fields. 

They considered the theoretical model as a means of studying any laminated wood plate where the elastic, temperature, 

moisture, and electric fields influence the overall structural 

response. Their results of studying a representative example 

provided an indication of the level of response of adaptive 

wood composites, although no experimental verification had 

been conducted in their investigation.

Origin of the Piezoelectric Effect in Wood

Fukada (1955) and Bazhenov (1961) both hypothesized that 

the piezoelectric effect observed in wood originates in crystalline cellulose regions of the wood cell wall and that its 

intensity is dependent upon the degree of crystallinity. Hirai 

and others (1970) furthered that hypothesis, postulating that 

the magnitude of the piezoelectric modulus of wood depend 

upon degree of crystallinity and orientation of cellulose 

crystals in the cell wall.

Using conformational analysis, Pizzi and Eaton (1984) 

concluded that van der Waal forces were responsible for the 

piezoelectric effect in wood. They concluded that the electrical charge most likely develops in response to an imposed 

shear force that results in laminar lateral–longitudinal deformations in the five-strand unit of the crystalline cellulose I 

molecule found in the microfibrils of wood. They also concluded that electrostatic and hydrogen bond interactions do 

not contribute to the piezoelectric effect.

Hirai and others (1968b, 1972) have shown that the piezoelectric modulus can be increased by increasing the crystallinity of the cellulose by treatment with gamma rays, 

exposure to high temperature for extended periods, liquid 

ammonia, ethlyenediamine, or sodium hydroxide. Fukada 

and others (1957) found that aging wood increased its crystallinity and its piezoelectric modulus. Based on his experimental results, he also postulated that fungal decomposition 

decreased both crystallinity and piezoelectric modulus.

Nakai and others (2005) found that the first and second 

peaks in the piezoelectric voltage appeared almost simultaneously with the peak of the ratio of crystal lattice strain 

to surface strain (Fig. 6.5). They also noted that the 

piezoelectric response decreased because of the effect of 

microscopic cracks in their specimens.

Figure 6.5—Relationships between the ratios of crystal 

lattice strain to surface strain (triangles), piezoelectric 

voltage (squares), and tension stress. Note: values of 

tension stress are shown as a percentage of ultimate 

tensile stress (Nakai and others 2005). (Graphic used 

by permission of the Journal of Wood Science and 

Technology.)

Chapter 6
Nondestructive Evaluation of Wood: Second Edition

73

Piezoelectric Effect and the Properties of 

Wood and Wood Structural Members

Nakai and others (1998) measured the piezoelectricity of 

kiln-dried Sitka spruce specimens and simultaneously recorded scanning electron microscope images in real time 

to observe the deformation process of wood. Results of 

their experiments showed that there were two types of microscopic destruction in the specimens. With the first type, 

although a small uprush around the boundary of the annual 

ring was observed, the specimens were broken only by 

shearing fracture in the 45o direction. With the second type, 

the specimens were finally broken by shearing fracture after 

repeated buckling. They found that the piezoelectric voltage 

increased almost linearly in the elastic region, preceded to 

the maximal point, and then decreased gradually, and a clear 

peak appeared in the bucking and shearing fracture.

Nakai and Takemura (1993) measured the piezoelectricity of 

air-dried specimens (from five species) under time-varying 

load to investigate the possible relationship between piezoelectricity and the fracture of wood. A time-varying load 

was applied at a constant rate, accompanying a preliminary 

load and a sinusoidal load with a frequency of 20 Hz. They 

found that the greatest voltage of the piezoelectric signals as 

reported in a previous paper was in the case of a grain angle 

of 45o, and the voltages of the piezoelectric signals depended on the magnitude of the load, species, and grain angle. 

The results of their experiments showed that the piezoelectricity–time curves can be classified into three types (Type 

A, B, and C). Each curve consists of an initial rising part, a 

gradually increasing part, a subsequent decreasing part, and 

finally, a rapid rising and falling (Type A and B) or merely 

falling part (Type C), where the second part of the Type B 

is much flatter compared with that of the Type A. They also 

found that decreasing piezoelectricity against an increasing 

load was another characteristic behavior in the plastic region 

before a sudden fracture of a specimen.

Fukada and others (1957) found that the relation between 

the dynamic Young’s modulus and the piezoelectric constant 

of the old timbers was linear (Fig. 6.6). Nakai and others 

(1998) reported a similar linear relation in the kiln-dried Sitka spruce with the exact relationship between the dynamic 

Young’s modulus and the piezoelectric constant as

Hirai and others (1968a) found that the piezoelectric effect 

varied with the angle between the direction of the stress 

and the direction of the fiber axis and that maximum piezoelectric polarization was obtained when the direction of the 

stresses were at angles of 45o and 135o with the direction of 

the fiber axis (Fig. 6.7). 

Knuffel and Pizzi (1986) measured the piezoelectric effect 

in Pinus patula structural timber beams. They found that the 

piezoelectric signal usually began smaller, increased to maximum after about five cycles, and then began to attenuate 

to zero. Also, the piezoelectric response started to develop 

almost simultaneously with the arrival of the stress wave 

and reached its first peak within 0.0001 s. They observed 

that the first peak of the piezoelectric signals might be either 

positive or negative, which are uncontaminated by resonance. They also found that the piezoelectric effect in the 

wet boards was still found to be very strong. But because of 

the conductive conditions, the electrical signal originating at 

the beginning of the board propagated faster than the stress 

wave, and at 20% moisture content, the piezoelectric effect 

began to coincide with the arrival of the stress wave.

Knuffel (1988) investigated the effect of the natural defects 

on the piezoelectric effect in structural timber. There were 

Ec (×103 kgf/cm2) = 1.18 ( Pp

ρ × Lp ) + 3.15

Dynamic Young’s modulus (×1010 dyne/cm2)

Piezoelectric constant

0 5 10 15 

1

2

3

×10–9 c.g.s.e.s.u 

J2

M6

P2

I

1

O2

L4

N2

R H5 2

K2

Figure 6.6—The relation between dynamic Young’s 

modulus and the piezoelectric constants of old timbers 

(Fukada and others 1957). (Graphic used by permission 

of Oyo Buturi.)

Figure 6.7—Anisotropy of piezoelectric modulus 

(Hirai and others 1968). (Graphic used by permission 

of Washington State University, Pullman.) Piezoelectric modulus, d×10–9 c.g.s.e.s.u

Θ: Angle (deg) between directions of tree 

and fiber axis

0 45 90 135 180

Θ

1

0.5

0

–0.5

–1

A Review of the Piezoelectric Effect in Wood
General Technical Report FPL–GTR–238

74

three findings from their investigation. Firstly, the piezoelectric first wavepeak values showed a definite and very 

sensitive increase in amplitude in the vicinity of knots and 

cross-grain (Fig. 6.8). Second, the piezoelectric response 

was far more sensitive to the defects than to MOE. At last, 

the piezoelectric effect was directly related to strain concentrations in the anatomical structure.

Summary

1.	Research has been conducted on the piezoelectric 

effect in wood and wood materials, starting as early 

as the 1940s.

2.	A number of wood species have been shown to exhibit 

a piezoelectric effect.

3.	The magnitude of the piezoelectric modulus of wood is 

approximately 1/20 of that of a quartz crystal.

4.	Several studies have been conducted to identify the 

origin of the piezoelectric effect in wood.

5.	Studies have been conducted to explore its potential for 

evaluating the structural properties of wood structural 

members.

Literature Cited

Bazhenov, V.A. 1961. Piezoelectric properties of wood. 

New York: Consultants Bureau. 176 p.

Bazhenov, V.A.; Konstantinova, V.P. 1950. Piezoelectric 

properties of wood. Doklady Akod. Nauk SSSR 71(2). 

Chemical Abstracts. 45, 2747. 

Best, F.M., inventor. 1935 (August 13). Method of and apparatus for utilizing energy of a vibratory nature. U.S. Patent 

2,010,806.

Churchill, D.L.; Arms, S.W., inventors. 2010 (April 6). Slotted beam piezoelectric composite. U.S. Patent 7,692,365 B2.

Fei, Y.Y.; Zeng, S.X. 1987. Piezoelectric effect in wood. 

Journal of Nanjing Forestry University. 3: 100–104.

Fukada, E. 1955. Piezoelectricity of wood. Journal of the 

Physical Society of Japan. 10(2): 149–154.

Fukada, E. 1965. Piezoelectric effect in wood and other 

crystalline polymers. In: Galligan, W.L., ed. Proceedings, 

second symposium on nondestructive testing of wood. April, 

1965. Spokane, WA: Washington State University. 143–170.

Fukada, E. 1968. Piezoelectricity as a fundamental property 

of wood. Wood Science and Technology. 2: 299–307.

Fukada, E.; Yasuda, S.; Kohara, J.; Okamoto, H. 1957. The 

dynamic Young’s modulus and the piezoelectric constant of 

old timbers. Oyo Butsuri [Journal of Applied Physics, 

Japan]. 26: 25–28. 

Galligan, W.L.; Bertholf, L.D. 1963. Piezoelectric effect in 

wood. Forest Products Journal. 12(12): 517–524.

Galligan, W.L.; Courteau, R.W. 1965. Measurement of 

elasticity of lumber with longitudinal stress waves and the 

piezoelectric effect in wood. In: Galligan, W.L., ed. Proceedings, second symposium on nondestructive testing of 

wood. April, 1965. Spokane, WA: Washington State 

University. 223–224.

Hirai, N.; Asano, I. Sobue, N., Naito, H. 1970. Studies 

on piezoelectric effect of wood. III. Tree growth and variations of piezoelectric modulus. Mokuzai Gakkaishi. 

16(7): 310–318. 

Hirai, N.; Date, M.; Fukada, E. 1968a. Studies on piezoelectric effect in wood. I. Mokuzai Gakkaishi. 14(5): 247–251.

Figure 6.8—Piezoelectric response in structural timber (Knuffel 1988). (Graphic used by permission 

of Holzforschung.)

Chapter 6
Nondestructive Evaluation of Wood: Second Edition

75

Hirai, N.; Date, M.; Fukada, E. 1968b. Studies on piezoelectric effect in wood. II. Modification of crystal structures. 

Mokuzai Gakkaishi. 14(5): 252–256.

Hirai, N.; Hayamura, S.; Suzuki, Y.; Miyajima, K.; Matsuyama, S. 1992. Piezoelectric relaxation of wood in high 

temperature region. Mokuzai Gakkaishi—Journal of the 

Japan Wood Research Society. 38(9): 820–824.

Hirai, N.; Morita, M.; Suzuki, Y. 1993. Electrical properties 

of cyanoethylated wood meal and cyanoethylated cellulose. 

Mokuzai Gakkaishi. 35(5): 603–609.

Hirai, N.; Sobue, N.; Asano, I. 1972. Studies on piezoelectric effect of wood. IV. Effects of heat treatment on cellulose 

crystallites and piezoelectric effect of wood. Mokuzai 

Gakkaishi. 18(11): 535–542.

Hirai, N.; Yamaguchi, A. 1979. Studies on piezoelectric effect of wood. VI. Effect of moisture content on piezoelectric 

dispersion of wood. Mokuzai Gakkaishi. 25(1): 1–6.

Kellog, R.M. 1981. Physical properties of wood. In: Wangaard, F.F., ed. Wood: its structure and properties. Clark C. 

Heritage Memorial Series on Wood. University Park, PA: 

The Pennsylvania State University. 195–223. Vol. 1.

Knuffel, W. 1988. The piezoelectric effect in structural lumber. Part II. The influence of natural defects. Holzforschung. 

42(4): 247–252.

Knuffel, W.; Pizzi, A. 1986. The piezoelectric effect in 

structural lumber. Holzforschung. 40(3): 157–162.

Kytmanov, A.V. 1967. Piezo-electric properties and shrinkage of compressed Larch wood. Drevesina, ee zascita i plastiki. 72–77. Moscow, USSR.

Lammer, H.J., inventor. 2006 (August 29). Flexible piezoelectric films. U.S. Patent 7,098,578 B2.

Lin, R.T. 1967. Review of the electrical properties of wood 

and cellulose. Forest Products Journal. 17(7): 54–66.

Maeda, H.; Tsuda, H.; Fukuda, E. 1977. Electrical and mechanical properties of bamboo and wood with various hydrations. Reports on Progress in Polymer Physics in Japan. 

20: 739–742.

Nakai, T.; Igushi, N.; Ando, K. 1998. Piezoelectric behavior 

of wood under combined compression and vibration stress 

I: relation between piezoelectric voltage and microscopic 

deformation of Sitka spruce (Picea sitchensis Carr.). Journal 

of Wood Science. 44: 28–34.

Nakai, T.; Takemura, T. 1993. Piezoelectric behaviors of 

wood during compression tests. Mokuzai Gakkaishi. 

39(3): 265–270.

Nakai, T.; Yamamoto, H.; Nakao, T.; Hamatake, M. 2005. 

Mechanical behavior of the crystalline region of wood and 

the piezoelectric response of wood in tension tests. Wood 

Science and Technology. 39: 163–168.

Pizzi, A.; Eaton, N. 1984. Correlation between molecular 

forces in the cellulose I crystal and the piezoelectric effect in 

wood. Holzforschung und Holzverwertung. 36(1): 12–14.

Sanders, J.F., inventor. 2001 (January 16). Composites 

under self compression. U.S. Patent 6,174,595 B1.

Shubnikov, A.V. 1946. Piezoelectric textures. Moskova, 

Russia: Izvestiya Akademii Nauk, Seriya Biologicheskaya 

[Proceedings of the Academy of Sciences, Biological 

Series]. 84 p. In Russian.

Smittakorn, W.; Heyliger, P.R. 2001. An adaptive wood 

composite: theory. Wood and Fiber Science. 33(4): 595–608.

Suzuki, Y.; Hirai, N. 1995. Piezoelectric relaxation of wood. 

II. Application of two-phase system model to piezoelectric 

relaxation in low temperature region. Mokuzai Gakkaishi. 

42(3): 271–280.

Suzuki, Y.; Hirai, N.; Ikeda, M. 1992. Piezoelectric relaxation of wood. I. Effect of wood species and fine structure 

on piezoelectric relaxation. Mokuzai Gakkaishi. 38(1): 

20–28.

Suzuki, Y.; Kataoka, Y.; Matsui, H. 2003. Electrical properties of composite cyanoethylated pullulan film. Journal of 

Wood Science. 49(1): 100–103.

A Review of the Piezoelectric Effect in Wood

77

Chapter 7

Nondestructive Testing in the Urban Forest

R. Bruce Allison, Adjunct Professor

University of Wisconsin–Madison, Madison, Wisconsin; and Allison Tree, LLC, Verona, Wisconsin

Xiping Wang, Research Forest Products Technologist

Forest Products Laboratory, Madison, Wisconsin

Trees within an urban community provide measurable 

aesthetic, social, ecological and economic benefits. When 

growing normally and stably, they contribute to making 

a city more livable and comfortable for its inhabitants. 

However, as large physical structures in close proximity 

to people and property, their failure can cause harm. The 

science of tree stability analysis uses both biological and 

engineering principles in determining a tree’s structural 

soundness and predicting the probability of failure. Nondestructive testing methods of locating and quantifying wood 

decay and defect are used to measure the physical condition 

of trees within the urban forest to promote public safety. 

These methods are of special value to the urban forest managers and arborists responsible for the general safety of city 

residents, roadway transportation, and utility corridors. This 

chapter will discuss the commonly used methods of visual 

inspection, acoustic testing, and microdrill resistance with 

two case studies presented to illustrate how they are used 

in combination plus how to best interpret data collected in 

evaluating the nature and threat of discovered defects. 

Tree Decay and Visual Inspection

Tree decay is an essential process that links tree growth to 

soil fertility, wildlife habitat, and many other critical functions in all forests. However, decay in roots, trunk, and 

limbs can be a major problem for tree managers. Concealed 

internal decay presents a special problem in managing urban trees for public safety. Knowledge and special tools to 

measure the presence of internal decay are needed by tree 

managers responsible for safety in parks and recreational 

areas, populated residential and commercial neighborhoods, 

and along utility and transportation corridors. Trees can 

reach great height, mass, and age. Wood decay and defect 

can cause structural failure damaging property, endangering 

lives, and affecting the efficiency of transportation and energy distribution. Urban forest managers need to understand 

how decay fungi interact with the tree and how and why 

visible external characteristics of the tree can reveal internal 

decay conditions. 

Decay fungi enter standing trees through wounds or breaks 

in the bark. Wounds can result from a variety of incidents 

including storms, pruning, or root cutting. Once the decay 

fungus breaches the bark, it enters the sapwood and can 

eventually affect the heartwood. 

A variety of fungi species affect woody plants and they can 

progress over time in succession, one species preparing the 

way for the invasion of another. They live in a tree’s structure, and its reaction to the presence of the fungal parasite 

influences how the fungus advances within the tree. Decay 

fungi are parasites. Unable to produce their own carbohydrates for energy, they digest the carbohydrates within 

the tree’s structure. The drawings in Figure 7.1, provided 

courtesy of the United States Forest Service, illustrate how 

decay fungi interact with trees (Shigo 1979).

Trees “wall off” or compartmentalize wounded areas to resist the spread of decay, confining it to the wood present at 

the time of wounding. The concept of compartmentalization 

was developed by Dr. Alex Shigo as part of his research for 

the U.S. Forest Service on the biology of tree decay (Shigo 

1979, 1984). Following his retirement from the Forest Service in 1985, he continued to write and teach about tree 

biology until his death in 2006.

Compartmentalization produces chemical and anatomical 

boundaries that vary in effectiveness to resist the spread of 

decay. The ceiling and floor and the interior wall are usually 

less effective than the exterior wall formed by new growth 

of xylem tissue. Thus decay tends to move in columns and 

back to the interior. Understanding how decay advances 

within a tree allows the tree manager to visualize the shape 

of internal decay, estimate its point of origin, and predict the 

direction of movement.

The tree manager can use visual inspection of a tree for 

observing external indicators of internal decay and defect. 

Seams on the bark, for example, can indicate internal cracks 

and decay. Other external indicators of decay include open 

cavities. Fungal fruiting bodies are a sign of the presence of 

wood decay fungi. They present the opportunity to identify 

the species of fungus and thereby know its significance and 

probable course of advancement within the tree. Examples 

of useful wood decay identification books include Wood 

Decay Fungi by Christopher Luley (Luley 2005) and Manual of Wood Decay in Trees by Karlheinz Weber and Claus 

Mattheck (Weber and Mattheck 2003).
General Technical Report FPL–GTR–238

78

The visual inspection of a tree’s “body language” is another 

way to estimate the presence of internal decay. For example 

a tree with an unusually expanded base sometimes called 

“bottle butt” could indicate the presence of extensive lower 

trunk decay. Trees are self-optimizing organisms highly 

reactive to the environmental stress around them. They are 

under constant mechanical stresses from holding their own 

weight, from leans or horizontal branching or dynamic loads 

from wind or ice. From Newtonian physics it is understood 

that a horizontal force against the tree will produce an equal 

and opposite resultant force tending to uproot the tree unless 

there is a reciprocal force to maintain stability. Wind loads 

in the crown are distributed down the trunk to the anchoring 

root system as mechanical stresses. The reciprocal force is 

the weight and friction of the soil against the root system resisting the shear force. Without it, tree trunks would be like 

the masts of sailing ships pushed along by wind forces.

Internal decay in branches or trunks reduces the area for 

those mechanical stresses to pass on the way to the root 

system. Living parts of trees can respond to those areas 

of concentrated mechanical stresses by laying down additional wood. This process is called thigmomorphogenesis. 

It explains the bottle shape or bulges that are sometimes 

evident on trunks with internal decay. Evidence of this selfoptimizing growth reaction to environmental mechanical 

stress is observed in cross sections of horizontal branches 

or leaning trunks that have produced wider annual rings on 

one side compensating for the extra gravitation loading. In 

angiosperms, the extra wood growth occurs on the tension 

side of the lean acting as a guy line holding back the trunk 

or limb. In gymnosperms, the extra wood growth occurs on 

the compression side acting as prop to hold up the trunk or 

limb. Examples of book references discussing this body language of trees and the visible anatomical indicators of decay 

are The Body Language of Trees by Claus Mattheck (1996) 

and Evaluating Tree Defects by Ed Hayes (2001).

In addition to these visual methods of internal decay detection, tree managers have used other traditional tools including increment borers and sounding mallets (Fig.7.2). Increment borers allow the visual examination of a core sample 

taken from the trunk using inexpensive, simple tools. But 

they have the disadvantage of leaving a significant wound at 

each location sampled. Small-diameter drill bits on a battery 

powered hand drill can also be used to “feel” for changes in 

resistance and look for wound-initiated discoloration in the 

shavings, but no core sample is produced and results are not 

graphically recorded. 

Sounding mallets allow a trained ear to listen for the telltale 

drum sounds of internal cavities. They are limited in determining the extent or location and do not produce a record of 

the sound wave characteristics. 

Electronic tools are available to vastly improve upon these 

traditional forestry methods. The increment borer and 

sounding mallet have evolved into sophisticated electronic 

tools of micro-drill resistance and acoustic measurements. 

Figure 7.1—Wounds start the processes that 

could lead to discolored and decayed wood. 

The classical concept and the expanded concept both recognize wounds as the starting 

point for the processes. Trunk wounds can be 

caused by a variety of agents: insects, birds, 

small and large animals, wind, ice, snow, temperature extremes, chemicals, and people and 

some of their activities (Shigo 1979).

Figure 7.2—Traditional tools for inspecting trees—

Increment borer and sounding mallet. 

Chapter 7
Nondestructive Evaluation of Wood: Second Edition

79

Acoustic Measuring Devices

Single-Path Stress Wave Timing

Simple stress wave measuring devices are used to record 

the time it takes for an impact-induced stress wave to travel 

through the wood sample. When used for tree inspection, 

this nondestructive testing procedure is often called singlepath stress wave timing measurement. A stress wave is created with a strike to the tree. An accelerometer mounted at 

the strike point senses the impact and sends a start signal 

to the measuring device to start a timer. A second accelerometer attached to the opposite side of the tree senses the 

leading edge of the stress wave and sends a stop signal to 

the timer. The time between the start signal and stop signal 

is the time-of-flight (TOF) of the wave propagating through 

the tree trunk. The TOF data are usually measured in microseconds. Transmission time per unit length, such as microseconds per meter and velocity, such as meters per second, 

are calculated using the known distance between the sensors 

and the TOF.

The first instruments using acoustics to determine forest 

product quality were developed by Metriguard, Inc., of 

Pullman, Washington, in the early 1960s to evaluate particleboards (Pellerin and Ross 2002). In the 1990s, foresters 

began using longitudinal stress wave testing on both cut logs 

and standing timber for log selection and grading based on 

material strength. The first report of the use of stress wave 

testing to determine internal degradation of living trees was 

by Claus Mattheck and Klaus Bethge in 1993 (Mattheck and 

Bethge 1993).

Stress wave propagation in wood is related to the physical 

and mechanical properties of the wood. Stress waves travel 

faster in defect-free wood. Areas of cracks, cavities, or 

decay-induced low density will require longer times for the 

stress wave to arrive at the opposite timing sensor. By measuring TOF or wave transmission time through a tree stem 

in the radial direction, the internal condition of the tree can 

be determined. 

The presence of deterioration from decay can greatly affect 

TOF in wood. TOF for decayed wood are much greater than 

that for non-decayed wood. For example, TOF for nondegraded Douglas-fir is approximately 800 μs/m (244 μs/ft), 

whereas severely degraded members exhibit values as high 

as 3,200 μs/m (975 μs/ft) or greater (Wang et al. 2004). A 

study conducted by Pellerin and others (1985) demonstrated 

that a 30% increase in stress wave TOF implies a 50% loss 

in strength. A 50% increase indicates severely decayed 

wood. 

The speed of stress wave propagating perpendicular to grain 

is also affected by tree species. Mattheck and Bethge (1993) 

measured speed of sound in different species of healthy 

trees using a commercially available stress wave timing 

unit. The speed was determined by dividing the transit distance (tree diameter) by the TOF measured. Table 7.1 shows 

both radial stress wave velocity and TOF on a per length 

basis for 5 softwood species and 14 hardwood species. 

Nondestructive Testing in the Urban Forest

Table 7.1—Radial stress wave velocity and time-of-flight measured in 

healthy trees (Mattheck and Bethge 1993) 

Species 

Radial stress wave velocity Time-of-flight per unit length

 m/s ft/s μs/m μs/ft 

Hardwoods 

Ash 1162–1379 3810–4520 725–861 221–262 

Birch 967–1150 3170–3770 870–1034 265–315 

Black locust 934–1463 3060–4800 684–1071 208–326 

Black poplar 869–1057 2850–3470 946–1151 299–351 

Horse chestnut 837–1557 2860–5110 642–1145 196–349 

Lime 940–1183 3080–3880 845–1064 258–324 

Maple 1006–1600 3300–5250 625–994 191–303 

Oak 1382–1610 4530–5280 621–724 189–221 

Pine poplar 967–1144 3170–3750 874–1034 266–315 

Plane 950–1033 3120–3390 968–1053 295–321 

Red beech 1206–1412 3960–4630 708–829 216–253 

Silver poplar 821–1108 2690–3640 903–1218 275–371 

Sweet chestnut 1215–1375 3990–4510 727–823 222–251 

Willow 912–1333 2990–4370 750–1096 229–334 

Softwoods 

Douglas-fir 905–1323 2970–4340 756–1105 230–337 

Fir 910–166 2990–3830 858–1099 261–335 

Larch 1023–1338 3360–4390 747–978 228–298 

Pine 1066–1146 3500–3760 873–938 266–286 

Spruce 931–1085 3050–3560 922–1074 281–327 
General Technical Report FPL–GTR–238

80

Generally, stress waves travel faster in hardwood trees than 

in softwood trees. To account for species difference, Divos 

and Szalai (2002) provided some baseline reference velocities for different species for field tree inspection (Table 7.2). 

This reference velocity can be used to evaluate the actual 

measured wave velocity and assess the internal condition of 

the tree inspected. 

Figure 7.3 shows the use of a stress wave timer. When measuring TOF across a tree trunk, an inspector should make 

sure that the start and stop sensor probes are aligned in a 

horizontal line across the diameter of the trunk. The spike of 

a sensor probe should penetrate the bark and reach the sapwood so that the probe is securely attached to the trunk. Use 

a small hammer that is provided along with the stress wave 

timing device to tap the start sensor probe and obtain several 

consistent TOF readings.

Acoustic Tomography

In recent years, tomography techniques that were developed 

for geophysical engineering and medical applications have 

been evaluated for their applicability in tree inspection. 

Investigations on urban trees showed great success using 

acoustic tomography to detect internal decay hidden from 

view within the trunks (Nicolotti et al. 2003, Gilbert and 

Smiley 2004, Wang and Allison 2008, Wang et al. 2009). 

Acoustic tomography employs multiple sensors (usually 8 

to 32) to measure stress wave transmission time at multiple 

directions. The distance between the sensors is measured 

with a caliper scale. An impact-induced stress wave is generated by striking each of the sensor recording points with 

the hammer. A computer records the time-of-flight of the 

stress wave to each of the other recording points. Knowing 

the distance and the TOF data, a velocity can be calculated 

by the computer. Computer projection software using stress 

wave data from these multi-path measurements creates an 

image of the distribution of relative acoustic wave velocity 

in the cross section. 

Figure 7.4 shows the acoustic tomograms obtained from a 

black cherry tree at 50-, 100-, and 150-cm heights versus 

corresponding photographic images and 2D hardness maps 

of the cross sections (Wang et al. 2009). The tomogram in 

this example is displayed in four colors representing varying 

recorded velocities. Velocities from lower to higher values 

are displayed as blue, violet, green and brown. The acoustic 

shadows in the tomograms are in good agreement with the 

physical conditions revealed by visual examination and 2D 

hardness mapping of the cross-sections. This map of the 

acoustic characteristics of the sample cross section, with accurate interpretation, can determine the size and location of 

internal trunk decay and defect. 

The acoustic tomogram is not a spatially precise map of the 

internal structure but only a representation of the wood’s 

Figure 7.3—A Fakopp Microsecond Timer (Fakopp Bt., 

Agfalva, Hungary) was used to inspect a large red oak 

tree at the University of Wisconsin–Madison campus 

through a single-path stress wave timing measurement.

Chapter 7

Table 7.2—Reference stress wave velocity 

and time-of-flight in healthy trees (Divos 

and Szalai 2002) 

Species 

Radial stress 

wave velocity 

Time-of-flight 

per unit length

m/s ft/s μs/m μs/ft 

Beech 1670 5479 599 183 

Black fir 1480 4856 676 206 

Larch 1490 4888 671 205 

Linden 1690 5545 592 180 

Maple 1690 5545 592 180 

Oak 1620 5315 617 188 

Poplar 1140 3740 877 267 

Scotch fir 1470 4823 680 207 

Silver fir 1360 4462 735 224 

Spruce 1410 4626 709 216 
Nondestructive Evaluation of Wood: Second Edition

81

acoustic characteristics as determined by the perceived 

velocities of the stress waves moving between the sensors. 

The shape of a tomogram will vary depending upon the 

type of decay and defect within the trees. Areas of low velocity could be a result of a decay hollow, snake crack, or 

ring crack. When internal cracks are present in a tree trunk, 

acoustic tomography tends to overestimate the size of the 

defect. The acoustic shadows are usually in the form of wide 

band and its size is significantly greater than the true defect 

area as indicated in Figure 7.5. 

Resistography

The Resistograph (RINNTECH, Heidelberg, Germany) is a 

portable electric drill with a long, thin metal drill bit. As the 

micro drill bit (1.5-mm diameter in shaft and 3-mm wide in 

tip) moves through the wood in a linear path, the penetration resistance along its path is measured and recorded. 

The denser the wood, the greater the resistance is. Hollows, 

cracks, and decayed wood with lower density are detected 

by the reduced resistance to the drill as it progresses through 

Figure 7.4—Acoustic tomograms of a black cherry tree compared with the photographic images 

and 2D hardness maps of the cross-sections at different elevations (Wang et al. 2009). 

Nondestructive Testing in the Urban Forest
General Technical Report FPL–GTR–238

82

the wood sample. The pattern of change in density measurements is recorded on scaled graph paper or in a digital representation display. 

The Resistograph technique was developed in Germany 

in the 1980s as a portable tool for tree ring analysis. The 

first prototype of the resistance-based drilling machine 

was developed by Kamm and Voss in 1984 employing a 

spring-loaded recording mechanism (Rinn 2012). The resonance effects of the spring mechanism were later found to 

lead inaccurate readings and profiles. A study conducted at 

Hohenheim University (Stuttgart, Germany) and the Environmental Physics Institute Heidelberg University made a 

breakthrough in the resistance drilling concept and proved 

that electronic regulation and electronic recording of motor 

power consumption is able to achieve much more reliable 

and reproducible resistance profiles than the spring-loaded 

mechanism (Rinn 1988). Further experiments demonstrated 

that when the needle’s tip was flat and twice the diameter of 

the shaft, the measured electrical power consumption (EPC) 

was proportional to the mechanical torque at the needle 

and it mainly depended on the local density at the point of 

contact of the drill bit (Rinn 1989, 1990). After thousands 

of tests, a shaft diameter of 1.5 mm and a 3-mm-wide tip 

was found to be a good compromise between minimizing 

damage and maximizing information in the profiles (Rinn 

1989, 1990). With this breakthrough and new electronic improvements, the Resistograph tool can now simultaneously 

measure, display, and record the relative resistance profile 

through direct measurement of the EPC of a direct-current, 

needle-rotation motor while a drill bit is driven into the tree. 

More research on urban tree decay detection has shown that 

Resistograph technique is effective in detecting and defining 

the extent of internal decay, including early stages of decay, 

if the drilling device is oriented so that its path goes through 

the decay zone (Wang et al. 2005). However, orienting the 

drill through the decay is difficult to guarantee. Considering 

the orientation limitation, Resistograph technique is best 

suited to confirm and determine the extent of decay in trees 

after suspect trees are identified by other inspection methods 

such as visual assessment, stress wave timing, or acoustic 

tomography. Figure 7.6 shows the field use of the Resistograph tool for inspecting an American elm on Forest Products Laboratory property in Madison, Wisconsin. 

Tree Decay Inspection Procedure

Based on previous studies and tree inspection practice, 

Wang and Allison (2008) have developed an effective tree 

decay inspection procedure that uses a combination of visual assessment, acoustic tools, and micro-drill. The steps of 

the procedure are the following:

1. Simple screening test using a combination of visual 

inspection and single-path acoustic wave testing

2. Multi-sensor acoustic tomography to identify the 

location and approximate magnitude of defects

3. Micro-drill resistance tool to confirm defects or 

differentiate between decayed wood and internal cracks

Figure 7.5—Acoustic tomogram and the cross-section image of a 

black cherry tree with internal crack (Wang et al. 2009). 

Figure 7.6—Dr. R. Bruce Allison used rope and saddle 

techniques to climb an American elm to position him 

along the trunk crack for Resistograph testing. The goal 

of the testing was to determine by sampling if there was 

extensive heartwood decay associated with the crack that 

could compromise the holding strength of the remaining 

trunk wound.

Chapter 7
Nondestructive Evaluation of Wood: Second Edition

83

Two case studies, one in an urban park and the other in a 

forest camp ground, illustrate how this tree inspection 

procedure allows the tree manager to maximize the understanding of a tree’s concealed, internal condition.

Case Study 1: Urban Park 

A tree stability study was conducted on the 153 trees in the 

heavily used urban park surrounding the Capitol in Madison, Wisconsin (Allison 2005). The park managers were 

concerned about tree stability after some tree failures. The 

Forest Products Laboratory assisted in designing a nondestructive evaluation procedure for detecting internal decay. 

It started with a visual inspection of all directly and indirectly observable defects. 

Next a simple, quick screening test using a single-path 

acoustic tool was conducted on all of the larger trees’ lower 

trunks. The results were compared to the anticipated stress 

wave velocities in comparable tree species and were tabulated by Wang et al. (2004). 

These tables offer a range of values only. There are no uniform reference standards. Stress wave velocities of solid 

wood can vary due to moisture levels and tree-growing conditions; therefore conducting a self-reference test on a part 

of the tree or comparable neighboring tree with known solid 

wood creates the preferred reference standard. In this study, 

if the sampled trunk cross section had a velocity reduction 

greater than 25% of the solid wood reference standard it was 

judged to have failed the screening test.

Multi-path acoustic tests were then conducted on those select trees with problems indicated by the visual and singlepath acoustic tests (Fig. 7.7a). The multi-path acoustic tests 

generated tomograms requiring interpretation to identify the 

type, size and location of internal trunk defects. In the case 

of this 100-year old red oak (Quercus rubra) tests at cross 

section elevations of 10 cm, 100 cm, and 200 cm indicated 

decay and defect most severe at the root collar level. Tomography software is able to combine the three tomograms 

creating a 3D representation of the study area on the trunk 

(Fig. 7.7b). Resistograph test were then performed to verify 

the exact type and extent of the suspected defects and decay 

indicated on tomograms. The value of tomograms in those 

cases was that they identified the best location for the drilling, significantly reducing the number of drills required 

and enhancing the value of the data acquired. Resistograph 

testing indicated snake cracking and a large area of heartwood decay at the root collar area of the red oak.

Figure 7.7—Case study in the Capitol Square, Madison, Wisconsin. (a) Decay detection 

in red oak trees using a combination of visual inspection, acoustic testing, and resistance microdrilling; (b) 3D acoustic tomogram generated by the computer software 

through combining three tomograms of three different elevations. 

Nondestructive Testing in the Urban Forest

(a)

(b)
General Technical Report FPL–GTR–238

84

Having carefully progressed using the procedure in gathering and interpreting the data, confidence was established to 

recommend the removal of this historic and very public tree. 

Measurements of the defect, knowledge of the way decay 

interacts with the wood, and understanding the physical 

requirements for mechanical tree stability led to the conclusion that this tree had a high probability of failure under 

loading. Tree risk assessment considered both the likelihood 

of tree failure and the severity of consequences to people, 

property, or disruption of activity (Dunster et al. 2013). In 

this frequently occupied public park, large tree failure could 

be catastrophic. A 300-page report was submitted to the park 

managers detailing the cases of internal decay and defect 

revealed using these combined nondestructive testing methods (Allison 2005). Select tree removal and pruning were 

conducted based on the report’s conclusions. Just weeks 

before the last two trees were to be removed, a violent storm 

with unusually high velocity winds passed through the capitol park causing these two remaining trees to fail. Of the 

remaining 143 trees in the park, the only trees to fail were 

these two that had been identified as high risk using the procedure. Seldom does nature cooperate so fully in validating 

a procedure for testing tree stability.

Case Study 2: Forest Campground 

Another example of the successful use of combined visual, 

acoustic, and micro-drill decay detection procedure occurred 

at a recreational ropes course located at the U.S. Forest Service’s Camp Nesbit in Upper Michigan (Allison et al. 2008). 

The recreational course consists of steel cable and ropes attached to a cluster of twelve 60-ft-tall red pines (Pinus resinosa). Groups of school children regularly use the course, 

mounting the high suspended walkways and swinging from 

ropes. Undetected internal decay within any of the trees has 

the potential for disastrous consequences. A team of scientists from the U.S. Forest Service Forest Products Laboratory, Michigan Technological University, and the University 

of Minnesota Duluth were asked to nondestructively evaluate the trees for evidence of internal decay and instability. 

First a visual inspection was conducted looking for cavities, cracks, seams, bulges, decay fruiting bodies, carpenter 

ants, or other evidence of decay activity. Then single-path 

stress wave tests were conducted on the lower trunks, and 

with the aid of an aerial lift, at select locations in the upper 

crown (Fig. 7.8). To establish a standard for stress wave velocities of solid wood in this particular stand, we conducted 

stress wave tests on red pines known to be without decay or 

defect. When velocities were at least 25% slower than this 

standard, the sampled tree failed the screening test.

At those locations where visual inspection and failed acoustic screening test indicated a problem, multi-path acoustic 

tests were conducted. Tomograms from those tests sites 

were interpreted to determine an estimate of the type, size, 

and location of internal decay and defects. Using the acoustic map of the sampled cross section to guide the entry point 

and direction of the micro-drill, additional crucial data were 

gathered while minimizing the number of drill holes.

The use of the less time-consuming visual and single-path 

acoustic tool as screening tests reduced the use of the multisensor tomography test. And by using the tomography to 

guide the number and direction of the micro-drill, fewer drill 

holes were needed and more meaningful information was 

gathered. Single-path stress wave testing in two directions 

takes less than 5 min, each micro-drill test about the same. 

Multi-sensor tomograms with set up and measurements requires around 30 min per cross section tested.

Only one of the 12 tested trees was determined to have significant internal decay and was removed from the course. 

It is important to keep in mind that the mere presence of 

decay, even extensive decay, is just part of the story in determining whether a tree should be removed. Decay is a critical 

factor in tree stability because a loss of wood strength occurs as the percentage of decayed stem increases. Researchers have arrived at various formulas to evaluate the strength 

loss associated with wood decay (Table 7.3). Although useful to consider the possibility of failure, they should not be 

used as a threshold for tree removal without first considering 

the actual strength loading factors on the particular tree. 

Figure 7.8—Case study at the U.S. Forest Service Nesbit 

Lake Camp, Sidnaw, Michigan—Visual and nondestructive 

evaluation of red pines supporting a ropes course. 

Chapter 7
Nondestructive Evaluation of Wood: Second Edition

85

These case studies illustrate that a combination of nondestructive tests, following a procedure of visual inspection 

then screening with quick single path stress wave tests, then 

progressing to more complex multi-path acoustic testing as 

needed with the use of a few directed micro-drill tests to assist in interpretation, offers tree managers an efficient method to measure hidden internal decay and defects in standing 

trees. Other less frequently used NDT techniques include 

ground penetrating radar (GPR) (Miller and Doolittle 1990; 

Nicolotti et al. 2003; Butnor et al. 2009), static pull testing 

(Peltola et al. 2000; Kane and Clouston 2008), electrical impedance (Gocke et al. 2008), and, on an experimental bases, 

portable x-ray CT scanning (Allison 2011). 

Literature Cited

Allison, R.B. 2005. Capitol park tree structural stability 

study. Report submitted to Wisconsin Department of Administration Division of Building & Police. Allison Tree Care, 

Inc. Fitchburg, WI. 308 p.

Allison, R.B. 2011. Acoustic tomography and microdrill 

resistance density measurements compared to x-ray CT scan 

on white birch (Betula papyrifera). In: Proceedings of the 

17th International Nondestructive Testing and Evaluation of 

Wood Symposium, September14–16, 2011. Sopron, Hungary: University of West Hungary, p. 191–197.

Allison, R.B.; Wang, X.; Ross, R.J. 2008. Visual and nondestructive evaluation of red pines supporting a ropes course 

in the USFS Nesbit Lake Camp, Sidnaw, Michigan. In: 

Proceedings of the 15tht International Symposium on 

Nondestructive Testing of Wood. September 10–12, 2007, 

Duluth, MN. Madison, WI: Forest Products Society. 

p. 43–48. 

Butnor, J.R.; Pruyn, M.L.; Shaw, D.C.; Harmon, M.E; 

Mucciardi, A.N.; Ryan, M.G.. 2009. Detecting defects in 

conifers with ground penetrating radar: applications and 

challenges. Forest Pathology. 39: 309–322.

Divos, F.; Szalai, L. 2002. Tree evaluation by acoustic tomography. In: Proceedings of the 13th International Nondestructive Testing of Wood Symposium, August 19–21, 2002. 

Berkeley, CA. Madison, WI: Forest Products Society. 

p. 251–256.

Dunster, J.A.; Smiley, E.T.; Matheny, N.; Lilly, S. 2013. 

Tree risk assessment manual. Champaign, Illinois: 

International Society of Arboriculture. 

Gilbert, E.A.; Smiley, E.T. 2004. Picus sonic tomography 

for the quantification of decay in white oak (Quercus alba) 

and hickory (Carya spp.). Journal of Arboriculture. 

30: 277–280. 

Gocke, L.; Rust, S.; Weihs U.; Gunther, T.; Rucker, C. 2008. 

Combining sonic and electrical impedance tomography for 

non-destructive testing of trees. In: Proceedings of the 15th 

International Symposium on Nondestructive Testing of 

Wood. September 10–12, 2007, Duluth, MN. Forest Products Society, Madison, Wisconsin. p. 31–42.

Hayes, E. 2001. Evaluating tree defects: a field guide. Safetrees. ISBN-10: 0971412804. 30 p.

Kane, B; Clouston, P. 2008. Tree pulling tests of large shade 

trees in the Genus Acer. Arboriculture and Urban Forestry. 

34(2): 101–109. 

Luley, C.J. 2005. Wood decay fungi: common to urban living trees in the Northeast and Central United States. Naples, 

NY: Urban Forestry, LLC. 58 p. 

Nondestructive Testing in the Urban Forest

Table 7.3—Common methods to evaluate “strength loss” associated with wood decay 

(Harris et al. 2004) 

Source Formulaa

 Threshold for action Comments 

Coder 1989 ( ) 100 4 4 d D × 20–44% caution 

>50% hazard 

Based on engineering formula for bending 

stress in a cylinder. Thresholds based on 

experience. 

Wagener 1963 ( ) 100 3 3 d D × 33% Adjusted formula to account for discontinuities 

in trunks. Applied only to conifers without 

aggravating defects. 

Smiley and 

Fraedrich 1992 ( ) 100 3

3 3 3

×

+ −

D

d r D d 33% Modification of Wagener to account for cavity 

openings. Surveyed 54 fallen and 31 standing 

trees. Using 33% strength loss as a threshold for 

action, 50% of fallen and 13% of standing trees 

would have been removed. 

Matteck, Gerhardt, 

and Breloer 1992 

t R <0.30 Based on buckling failure in cylinders. 

Measured t/R for fallen and standing trees. 

Using a t/R of 0.3 as a threshold for action, 95% 

of the fallen trees and 35% of the standing trees 

would have been removed. Based on data, no 

conclusions can be made on threshold for 

trees >50 cm (20 in.) in diameter. 

a

Abbreviations: d—diameter of decayed wood; D—diameter of trunk; r—size of cavity opening/circumference 

of trunk; t—width of sound wood; R—radius of trunk. 
General Technical Report FPL–GTR–238

86

Mattheck C. 1996. Body language of trees: a handbook for 

failure analysis. Research for Amenity Trees. London: Stationery Office Books (TSO). ISBN-10: 0117530670. 260 p.

Mattheck, C.G.; Bethge, K.A. 1993. Detection of decay in 

trees with the Metriguard Stress Wave Timer. Journal of Arboriculture. 19(6): 374–378.

Miller, W.F.; Doolittle, J.A. 1990. The application of 

ground-penetrating radar to detection of internal defect in 

standing trees. In: Proceedings of the 7th International Nondestructive Testing of Wood Symposium, September 27–29, 

1990. Pullman, Washington: Washington State University. 

p. 263–274.

Nicolotti, G.; Socco, L.V.; Martinis, R.; Godio, A.; 

Sambuelli, L. 2003. Application and comparison of three 

tomographic techniques for the detection of decay in trees. 

Journal of Arboriculture. 29: 66–78.

Pellerin, R.F.; Ross, R.J. eds. 2002. Nondestructive evaluation of wood. Madison, WI: Forest Products Society. 

Pellerin, R.F.; DeGroot, R.C.; Esenther, G.R. 1985. Nondestructive stress wave measurements of decay and termite 

attack in experimental wood units. In: Proceedings, 5th International Symposium on Nondestructive Testing of Wood, 

September 9–11, 1985, Pullman, WA. Pullman, WA: Washington State University. p. 319–353.

Peltola, H.; Kellomaki, S.; Hassinen, A.; Granader, M. 2000. 

Mechanical stability of Scots pine, Norway spruce, and 

birch: An analysis of tree-pulling experiments in Finland. 

Forest Ecology and Management. 135:143–153.

Rinn, F. 1988. A new method for measuring tree-ring density parameters. Physics diploma thesis, Institute for Environmental Physics, Heidelberg University. 85 p.

Rinn, F. 1989. A new drilling method for wood inspection. 

Holz Zentralblatt. 34: 529–530.

Rinn, F. 1990. Device for material testing, especially wood, 

by drill resistance measurements. German Patent 4122494.

Rinn, F. 2012. Basics of typical resistance-drilling profiles. 

Western Arborist. Winter: 30–36. 

Shigo, A.L. 1979. Tree decay: An expanded concept. United 

States Department of Agriculture Forest Service Information 

Bulletin Number 419. Washington, DC.

Shigo, A.L. 1984. Compartmentalization: A conceptual 

framework for understanding how trees grow and defend 

themselves. Annual Review of Phytopathology. 22 (1): 

189–214. 

Wang, X.; Divos, F.; Pilon, C.; Brashaw, B.K.; Ross, R.J.; 

Pellerin, R.F. 2004. Assessment of decay in standing timber 

using stress wave timing nondestructive evaluation tools–

A guide for use and interpretation. Gen. Tech. Rep. 

FPL–GTR–147. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 12 p. 

Wang, X.; Wiedenbeck, J.; Ross, R.J.; Forsman, J.W.; 

Erickson, J.R.; Pilon, C.; Brashaw, B.K. 2005. Nondestructive evaluation of incipient decay in hardwood logs. Gen. 

Tech. Rep. FPL–GTR–162. Madison, WI: U.S. Department 

of Agriculture, Forest Service, Forest Products Laboratory. 

11 p. 

Wang, X.; Allison, R.B. 2008. Decay detection in red oak 

trees using a combination of visual inspection, acoustic testing, and resistance microdrilling. Arboriculture & Urban 

Forestry. 34(1): 1–4.

Wang, X.; Wiedenbeck, J.; Liang, S. 2009. Acoustic tomography for decay detection in black cherry trees. Wood and 

Fiber Science. 41(2): 127–137.

Weber, K.; Mattheck C. 2003. Manual of wood decays in 

trees. Gloucestershire, UK: Arboricultural Association. 

127 p.

Additional Source of Information

Harris, R.W.; Clark, J.R.; Matheny, N.P. 2004. Arboriculture: Integrated management of landscape trees, shrubs, and 

vines. Pearson Education, Inc., Upper Saddle River, New 

Jersey.

Chapter 7
Nondestructive Evaluation of Wood: Second Edition

87

Chapter 8

Acoustic Assessment of Wood Quality 

in Trees and Logs

Xiping Wang, Research Forest Products Technologist

Forest Products Laboratory, Madison, Wisconsin

Peter Carter, Chief Executive Officer

Fibre-gen, Auckland, New Zealand

Assessing the quality of raw wood materials has become a 

crucial issue in the operational value chain as forestry and 

the wood processing industry are increasingly under economic pressure to maximize extracted value. A significant 

effort has been devoted toward developing robust nondestructive evaluation (NDE) technologies capable of predicting the intrinsic wood properties of individual trees, stems, 

and logs and assessing the value of stands and forests. Such 

technologies can help foresters make wise management 

decisions and grow higher quality wood and can lead to 

greater profitability for the forest industry.

Acoustic technologies have been well established as material evaluation tools in the past several decades, and their use 

has become widely accepted in the forest products industry 

for on-line quality control and products grading (Schad et 

al. 1995; Pellerin and Ross 2002; Ross et al. 2004). Recent 

research developments on acoustic sensing technology offer further opportunities for wood manufacturers and forest owners to evaluate raw wood materials (standing trees, 

stems, and logs) for general wood quality and intrinsic wood 

properties. This provides strategic information that can help 

make economic and environmental management decisions 

on treatments for individual trees and forest stands, improve 

thinning and harvesting operations, and efficiently allocate 

timber resources for optimal utilization. For example, the 

information could be used to sort and grade trees and logs 

according to their suitability for structural applications and 

for a range of fiber properties of interest to paper makers. 

Another example is to determine the relationships between 

environmental conditions, silvicultural treatments, and wood 

fiber properties so that the most effective treatment can be 

selected for desired fiber quality in future plantations. 

Today, the precision of acoustic technology has been improved to the point where tree quality and intrinsic wood 

properties can be predicted and correlated to the performance of final products. With continuous advancements and 

refinements, this technology could assist in managing wood 

quality, assessing forest value, and improving timber quality 

of future plantations.

Background

Traditionally, the quality of trees, stems, and logs has been 

assessed through simple physical measurements (height/

length, diameter, taper, and sweep) and human visual observation of surface characteristics (size and distribution 

of knots, wounds, and other defects). Assignment to one of 

several possible grades is based on simple, broad, allowable 

ranges for physical features. Although these grades may be 

sufficient where appearance is the primary consideration, 

the adequacy of visual grades for applications involving 

stiffness and strength is questionable because no measure of 

these properties is actually obtained. A concern over reliability and the broad conservative design values associated with 

visual grades for structural applications led to the development of machine strength rating (MSR) technology for lumber, which uses a pre-established relationship between stiffness and bending strength to define a set of strength-based 

lumber grades (FPS 1997; Galligan 2002). This provides a 

more refined and flexible approach than visual grading for 

identifying and sorting lumber into stress grades used in 

products such as structural framing, glued-laminated timber 

(glulam), and engineered trusses. 

With the development and rapid growth of new engineered 

wood products such as laminated veneer lumber (LVL), Ibeams, and I-joists, there has been a parallel growth in nondestructive testing for stiffness and strength of lumber and 

veneer used as components of these products. In addition, 

concerns with design values of structural lumber graded 

with visual methods are creating demand for stiffness verification of visually graded lumber. These trends have renewed 

interest of mills in nondestructive testing and evaluation 

methods. Mills seeking to capture a price premium by 

producing nondestructively tested lumber and veneer find 

that it is very expensive to process logs or purchase timber 

stands that have low yields of product with the stiffness and 

strength levels desired by their customers. Consequently, researchers have developed technology for applying acoustic 

methods to measure stiffness of logs and trees and improve 

sorting and matching with desired levels of lumber or 
General Technical Report FPL–GTR–238

88

veneer stiffness (Aratake et al. 1992; Aratake and Arima 

1994; Wang 1999; Harris and Andrews 1999; Huang 2000; 

Addis et al. 2000a,b; Wang et al. 2001, 2002; Harris et al. 

2003; Andrews 2003). The research has led to the development and introduction of a series of acoustic tools that allow 

rapid assessment of wood resource quality at early stages of 

the operational value chain.

Fundamentals of Acoustic Wave 

Propagation in Wood

When stress is applied suddenly to the surface of wood, the 

disturbance that is generated travels through the wood as 

stress waves. In general, three types of waves are initiated 

by such an impact: (1) longitudinal wave (compressive or 

P-wave), (2) shear wave (S-wave), and (3) surface wave 

(Rayleigh wave) (Fig. 8.1). A longitudinal wave corresponds to the oscillation of particles along the direction of 

wave propagation such that particle velocity is parallel to 

wave velocity. In a shear wave, the motion of the particles 

conveying the wave is perpendicular to the direction of the 

propagation of the wave itself. A Rayleigh (surface) wave 

is usually restricted to the region adjacent to the surface; 

particles move both up and down and back and forth, tracing 

elliptical paths. Although most energy resulting from an impact is carried by shear and surface waves, the longitudinal 

wave travels the fastest and is the easiest to detect in field 

applications (Meyers 1994). Consequently, the longitudinal 

wave is by far the most commonly used wave for material 

property characterization.

One-Dimensional Wave Equation

A basic understanding of the relationship between wood 

properties and longitudinal wave velocity (hereafter referred 

to as wave velocity) can be acquired from fundamental 

wave theory. In a long, slender, isotropic material, strain and 

inertia in the transverse direction can be neglected and longitudinal waves propagate in a plane waveform (wave front) 

(Fig. 8.2). In this case, the wave velocity is assumed to be 

independent of Poisson’s ratio and is given by the following 

equation (hereafter referred to as a one-dimensional wave 

equation):

(8.1)

where C0 is longitudinal wave velocity, E is longitudinal 

modulus of elasticity, and ρ is mass density of material. 

Three-Dimensional Wave Equation

In an infinite or unbounded isotropic elastic medium, a triaxial state of stress is present. The wave front of the longitudinal wave propagating through such a medium is no longer 

a plane. The wave propagation is governed by the following three-dimensional longitudinal wave equation (Meyers 

1994):

 

(8.2)

where C is longitudinal wave velocity in unbounded medium and v is Poisson’s ratio of the material. To differentiate 

from the longitudinal wave velocity in a slender rod, we will 

use the term “dilatational wave” for unbounded medium. 

The wave velocity is dependent on density and two elastic 

parameters, modulus of elasticity (MOE) and Poisson’s 

ratio (v). 

Wave Propagation in Logs and Standing 

Trees

The direct application of fundamental wave equations in 

wood, particularly in standing trees and logs, has been complicated by the fact that wood is neither homogeneous nor 

isotropic. Wood properties in trees and logs vary from pith 

to bark as wood transforms from juvenile wood to mature 

wood. Properties also change from butt to top within a tree 

and differ between trees. Genetics, soil conditions, and environmental factors all affect wood characteristics in both 

microscopic and macrostructure levels. 

In spite of these natural variations, studies have shown that 

the one-dimensional wave equation is adequate to characterize the wave propagation behavior in logs that are in a long, 

slender form (Wang et al. 2004; Wang 2013). The modulus of elasticity of the logs predicted by this fundamental 

equation generally has a high accuracy. Consequently, log 

grading or sorting using acoustic wave technology has been 

very effective and widely adopted in the forest and wood 

processing industries. 

For standing trees, the acoustic measurement approach is 

completely different from that in logs. Because there is no 

Figure 8.1—Types of stress waves in semi-infinite elastic material.

Figure 8.2—Longitudinal waves traveling in a long, slender material as compression and tension waves.

Chapter 8

ρ

E C0 = 

( )( ) ν ν ρ

ν E C

1 1 2

1

+ −

− = 

t

S C

Δ T = 

CL = 2 f0L 

ρ

E C0 = 

( )( ) ν ν ρ

ν E C

1 1 2

1

+ −

− = 

t

S C

Δ T = 

CL = 2 f0L 
Nondestructive Evaluation of Wood: Second Edition

89

access to an end surface (in contrast to a log) in a standing 

tree, acoustic waves have to be introduced from the side 

surface of the trunk, which results in a nonuniaxial stress 

state in the stem. The one-dimensional wave equation is 

therefore no longer valid for trees. If the dilatational wave is 

considered for acoustic measurement in standing trees, Poisson’s ratio (v) of wood is needed to describe the relationship 

between wave velocity and modulus of elasticity, as shown 

in Eq. (8.2). Dilatational wave velocity is generally higher 

than C0 (Eq. (8.1)) (Meyers 1994; Wang et al. 2007). As 

Poisson’s ratio increases, the deviation of dilatational wave 

velocity from C0 gets larger. For instance, the ratio of dilatational wave velocity to C0 is 1.16 for v = 0.30. The velocity 

ratio becomes 1.46 as v increases to 0.40. 

The Poisson’s ratio of green wood is not explicitly known. 

Bodig and Goodman (1973) and other investigators obtained 

Poisson’s ratios through plate or compression testing for dry 

wood. Poisson’s ratio appears to change with species and 

material sources. However, statistical analysis by Bodig and 

Goodman (1973) indicated that Poisson’s ratios do not seem 

to vary with density or other anatomical characteristics of 

wood in any recognizable fashion. Therefore, an average 

value of 0.37 (vLR) has been suggested for both softwoods 

and hardwoods (Bodig and Goodman 1973; Bodig and 

Jayne 1982). This could translate into a dilatational wave 

velocity that is 1.33 times that of the one-dimensional longitudinal wave velocity, which is apparently in agreement 

with previous experimental results (Andrews 2003; Wang et 

al. 2001). 

Acoustic Measurements in Trees 

and Logs

The use of longitudinal acoustic wave techniques for wood 

quality assessment is based on accurate measurement of 

propagation velocity of a stress wave generated by a mechanical impact. The success of any field application of this 

technique is directly related to understanding stress-wave 

behavior in wood materials and the physical and geometrical 

characteristics of wood itself. Wood, in the form of trees and 

logs, tends to have variable external and boundary conditions that create technical challenges for measuring acoustic 

velocities. This is particularly true in trees, where a stress 

wave has to be initiated from the surface of the trunk, and 

acoustic sensors need to be attached to the trunk through 

spikes (probes). 

Trees—Time-of-Flight-Based Approach

A typical approach for measuring acoustic velocity in trees 

involves inserting two sensor probes (transmit probe and 

receiver probe) into the sapwood and introducing acoustic 

energy into the tree through a hammer impact on the transmit probe. The time-of-flight (TOF) is the time taken for the 

stress wave to travel from the transmit probe to the receiver 

probe. Acoustic velocity is subsequently calculated from the 

span between the two sensor probes and the TOF measure 

using 

(8.3)

where CT is tree acoustic velocity (m/s), S is distance between the two probes (sensors) (m), and Δt is TOF. 

During field acoustic measurement, the probes are inserted 

into the tree trunk (probes are inserted through bark and 

cambium to extend into the sapwood) and aligned within a 

vertical plane on the same face (Fig. 8.3). The lower probe 

is placed about 40 to 60 cm above the ground. The span between the probes is determined from a practical standpoint, 

typically in the range 1.0–1.2 m; the probes need to be positioned at a comfortable height for the person who takes the 

measurements. 

Logs—Resonance-Based Approach 

Acoustic velocities in logs and long stems of known length 

are typically measured using a resonance-based approach. 

In log acoustic measurement, an acoustic sensor is mounted 

or held on one end of a log (Fig. 8.4). A stress wave is initiated by a mechanical impact on the same end, and the stress 

waveforms are subsequently recorded by an electronic unit. 

This acoustic approach is based on the observation of hundreds of acoustic pulses resonating longitudinally in a log 

and provides a weighted average acoustic velocity. Most 

resonance-based acoustic tools have a built-in fast Fourier 

transformation (FFT) program that can analyze and output 

the natural frequencies of the acoustic signals. Log acoustic 

velocity is then determined from 

(8.4)

where CL is acoustic velocity of logs (m/s), f0 is fundamental 

natural frequency of an acoustic wave signal (Hz), and L is 

log length (end-to-end) (m). 

The resonance-based acoustic method is a well-established 

nondestructive evaluation (NDE) technique for measuring 

long, slender wood members such as logs, poles, and timber 

(Harris et al. 2003; Andrews 2003; Wang et al. 2004). The 

inherent accuracy and robustness of this method provide a 

significant advantage over TOF measurement in applications 

such as log measurement. In contrast to the TOF approach, 

the resonance method stimulates many, possibly hundreds, 

of acoustic pulse reverberations in a log, resulting in a very 

accurate and repeatable velocity measurement. Because of 

this accuracy, the acoustic velocity of logs obtained by the 

resonance-based measurement has served as a standard to 

validate the TOF measurement in standing trees (Wang et al. 

2001; Andrews 2003; Carter et al. 2005). 

Assessing Wood Quality of Logs

It is well recognized that the variation in wood and fiber 

properties is enormous within any pile of logs that has been 

Acoustic Assessment of Wood Quality in Trees and Logs

( )( ) ν ν ρ

ν E C

1 1 2

1

+ −

− = 

t

S C

Δ T = 

CL = 2 f0L 

ρ

E C0 = 

( )( ) ν ν ρ

ν E C

1 1 2

1

+ −

− = 

t

S C

Δ T = 

CL = 2 f0L 
General Technical Report FPL–GTR–238

90

visually sorted for similar grade. The same is true for logs 

from trees of the same age and from the same forest stand 

(Huang et al. 2003). Dyck (2002) reinforced this view by 

stating “All logs are different even if they are clonal and 

even if they come from the same tree.” As an example, 

Figure 8.5 shows the acoustic velocity of a large sample of 

similar logs from two geographically distinct radiata pine 

forests in New Zealand, demonstrating the large variability 

in intrinsic wood properties of the logs (Andrews 2000). 

Clearly, there are major commercial benefits to be gained 

by assessing wood properties at the log level and optimizing the use of resource through appropriate log sorting and 

processing. 

The ability to improve log sorting with resonance-based 

acoustic methods has been well recognized in the forest 

products industry (Walker and Nakada 1999; Harris et al. 

2003; Huang et al. 2003; Carter and Lausberg 2003; Wang 

et al. 2002, 2004). This technology is based on the observation of hundreds of acoustic pulses resonating longitudinally 

in a log and provides an acoustic velocity dependent on the 

weighted average wood properties and moisture status of 

the log. Because the MOE of the log is simply equal to the 

density times the acoustic velocity squared, the technology 

is basically measuring fiber properties that influence macro 

properties such as stiffness, strength, and stability. The 

challenge is to interpret what the log is “saying” and translate this information into meaningful values (Dyck 2002). 

Sorting Logs for Lumber Quality

Research has shown that log acoustic measures can be used 

to predict the strength and stiffness of structural lumber that 

would be produced from a log (Aratake et al. 1992; Aratake 

and Arima 1994; Ross et al. 1997; Iijima et al. 1997; Wang 

1999; Wang and Ross 2000). In the early 1990s, scientists in 

Japan did some pioneering research exploring the possibility 

of using the natural frequency of longitudinal compression 

waves in a log to predict the strength and stiffness of the 

structural timbers (Aratake et al. 1992; Aratake and Arima 

1994). They succeeded in identifying close relationships 

Figure 8.3—Acoustic measurement on standing trees.

Figure 8.4—Resonance acoustic measurement on 

a log.

Chapter 8
Nondestructive Evaluation of Wood: Second Edition

91

between the fourth resonant frequency of the logs and MOE 

and modulus of rupture (MOR) of the scaffolding boards 

and square timbers cut from the log. Years later, a trial 

study in the United States also revealed a good correlation 

between acoustic-wave-predicted MOE and mean lumber 

MOE (Ross et al. 1997). This research opened the way for 

acoustic technology to be applied in mills for sorting logs 

and stems for structural quality. 

To validate the usefulness of the resonance acoustic method 

for a practical log sorting process, Wang and Ross (2000) 

conducted a mill study and examined the effect of log 

acoustic sorting on lumber stiffness and lumber E-grades. 

After acoustically testing 107 red maple logs, they sorted 

the logs into four classes according to acoustic velocity. 

Figure 8.6 illustrates the average lumber MOE for each 

log class, with a significant differentiation and clear trend 

between the log acoustic classes. They further compared 

log acoustic classes to lumber E-grades and found a good 

relationship between them. Logs that have a high acoustic 

velocity contain higher proportions of high-grade lumber. A 

study in New Zealand revealed similar results when presorting untested logs of radiata pine into three acoustic classes 

(Addis et al. 1997). The logs with the highest acoustic 

velocity (the top 30%) produced timber that was 90% stiffer 

than that from the group with the lowest velocity (the bottom 30%). 

In a practical log-sorting process, the industry can achieve 

benefits by developing a sorting strategy based on the log 

sources and desired end products. Currently, companies 

implementing acoustic sorting strategies measure only the 

velocity of acoustic waves and segregate logs into velocity 

groups using predetermined cut-off velocity values. Appropriate cut-off acoustic velocity values can be determined 

for either selecting the highest quality logs for superior 

structural applications or isolating the low grade logs for 

nonstructural uses. 

Figure 8.7 demonstrates the increasing yield of structural 

grades of lumber with increasing acoustic velocity of logs 

processed, as measured at two New Zealand radiata pine 

sawmills. Assuming a price differential of NZ$200/m3

(value estimated in 2004) between structural and nonstructural lumber, an increase in average log acoustic velocity of 

0.1 km/s produces an increase in structural lumber yield of 

about 5%. This translates into a gain of about NZ$6/m3 on 

log volume or about NZ$1.8 million for a mill processing 

300,000 m3 of logs per year. 

Sorting Logs for Veneer Quality

Log acoustic measurement has also been successfully 

used to assess the quality of veneer obtained from logs. Figure 8.8 illustrates the relationship between acoustic velocity 

for Southern Pine log batches (10 percentile groups from log 

sample) and the average ultrasound propagation time (UPT) 

Figure 8.5—The acoustic velocity (km/s) of a large sample 

of similar logs from two geographically distinct radiata 

pine forests in New Zealand, demonstrating the large variability in the intrinsic wood properties of the logs 

(Andrews 2000).

8

9

10

11

12

13

14

G1 G2 G3 G4

Average M

O

E of lumber (G

Pa)

Log acoustic grade

Figure 8.6—Relationship between log acoustic grade 

and average MOE of lumber (Wang and Ross 2000).

40

45

50

55

60

65

70

75

80

85

90

2.8 3.0 3.2 3.4 3.6 3.8 4.0

Structural grade (%)

Log acoustic velocity (km/s)

Mill A

Mill B

Figure 8.7—Relationship between log acoustic velocity 

and yield of structural grades of lumber for two New 

Zealand radiata pine sawmills (Carter et al. 2005).

0

20

40

60

80

100

120

2.52.62.72.82.9

3

3.13.23.33.43.53.63.73.83.9

4

Frequency

Log acoustic velocity (km/s)

Acoustic Assessment of Wood Quality in Trees and Logs
General Technical Report FPL–GTR–238

92

of veneer peeled from the logs. UPT is the elapsed time for 

ultrasound to travel longitudinally within the veneer sheet 

between fixed roller wheel points on the Metriguard veneer 

tester. 

Several trials have been run in New Zealand to quantify 

the effectiveness of acoustic sorting strategy and potential 

value gains from segregation of logs for veneer production 

(Carter and Lausberg 2003). Typical results show that for 

Central North Island logs segregated into three classes, the 

high stiffness logs resulted in production of 52% premium 

high stiffness veneer product, compared with unsegregated 

logs at 24%. These results clearly show that segregation using acoustics results in substantially higher proportions of 

higher stiffness veneer being produced. Equally, if a higher 

grade out turn is required for plywood veneer production, 

log segregation with acoustics will result in a value gain. 

An economic analysis of sorting veneer logs for LVL production in the United States resulted in a gain of about 

US$16/m3 on log volume (about $80–$100 per thousand 

board feet Scribner log scale) (Carter et al. 2005).

Sorting Logs for Pulp and Paper Quality

One challenge that paper mills face is to quantify the quality of pulp logs going into the mill. Unlike saw logs that 

are used to produce structural lumber and veneer, quality 

specification for pulp logs deals with fiber characteristics, 

especially fiber length. Without appropriate sorting technologies to help “see through” individual logs for internal fiber 

quality, buyers or producers of pulp logs will not be able to 

know if logs meet the quality specifications for the product 

out turn. If unsorted, the below-specification logs must be 

processed along with the in-specification logs. This results 

in pulp and paper products of variable quality, depending on 

the proportion of below-specification logs entering the mill 

process at any time and the extent to which they depart from 

the specified quality (Albert et al. 2002). 

Similar to sorting saw logs for improving structural uses, 

acoustic technology could be used in pulp and paper mills to 

segregate pulp wood for pulp and paper manufacture. Albert 

et al. (2002) tested the hypothesis that acoustic measures 

of pulp logs are linked to fiber characteristics and paper 

properties. In a trial with 250 radiata pine peeler cores, they 

sorted the cores into 18 classes using acoustic velocity. Subsequent chemical pulping and testing demonstrated that fiber 

length, wet strength, and various handsheet properties varied 

systematically. The acoustic velocity of the peeler cores was 

found to be strongly related to the length-weighted fiber 

length and the wet zero-span tensile strength of the fibers 

from the peeler cores. Two follow-up studies carried out 

in New Zealand were summarized by Clark et al (2003). 

In the first study, the velocity of sound was measured in 

each of 250 small-diameter radiata pine logs (120–420 mm 

large-end diameter), the logs were segregated into one of 

four groups based on velocity, the logs in each group were 

chipped, each chip sample was then chemically pulped, and 

the resultant pulps were evaluated. This study confirmed the 

relationship between a range of fiber properties and whole 

log acoustic velocity. The second study involved 5,000 

radiata pine logs segregated into three acoustic velocity 

based groups and pulped through a commercial continuous 

digester. Resulting pulp characteristics are shown in Figures 

8.9 to 8.11, and the conclusion was that acoustics offers a 

means of sorting pulp logs and this study demonstrates that 

commercially useful separations can be obtained. 

Mechanical pulp properties were evaluated in another mill 

study with 2,247 radiata pine logs. Bradley et al. (2005) 

confirmed that acoustics could be used to segregate logs into 

groups that perform very differently in terms of pulp properties when refined to a given freeness or at a certain energy 

input. At a given target freeness, there was a 20% difference 

in energy requirement between the lowest and highest velocity logs for a given specific energy. They conclude that 

acoustic sorting and subsequent reblending has great potential to reduce fluctuations in pulp quality of the mill output. 

Monitoring Moisture Changes in Log Stocks

A further application of acoustic methods has been identified for monitoring changes in moisture content of freshly 

harvested logs from as high as 150% down to an air-dry 

state of 30% to 40%. Moisture content (MC) is important 

for fuel wood supplies to determine at what stage a log 

should be chipped and burned, as well as for certain mechanical and semi-chemical pulping processes where MC is 

critical for effective processing. Traditional sampling methods are cumbersome and time consuming, and there is no 

convenient portable tool capable of measuring MC at these 

levels because standard electrical conductivity or impedance 

methods become inaccurate at MCs above fiber saturation 

Figure 8.8—Relationship between acoustic velocity of 

Southern Pine log batches and the average ultrasound 

propagation time (UPT) of veneer from the logs (Carter et 

al. 2005). 

440

460

480

500

520

540

560

580

2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0

U

P

T of veneer (μs)

Log acoustic velocity (km/s)

Chapter 8
Nondestructive Evaluation of Wood: Second Edition

93

point. Yet the defined relationship between acoustic velocity and green density enables the evaluation of changes in 

density caused by loss of moisture simply by monitoring the 

average increase in acoustic velocity that is observed as MC 

and associated green density decline with time.

Results are currently being evaluated (Foulon 2006) but 

look very encouraging for the emerging fuel wood sector in 

the United Kingdom where they need to measure and manage moisture content of log stockpiles because generating 

companies do not want to chip and burn wood above 40% to 

55% MC (dry basis). The acoustic-based procedure has the 

following steps for monitoring the increase in velocity 

as green density declines: 

•	 Establish a definitive MC start point using the traditional lab-based sampling method 

•	 Mark a sample of logs within the log stack 

•	 Measure acoustic velocities using a portable acoustic 

tool 

•	 Remeasure acoustic velocities at any later date 

•	 Compare average velocity increase, which defines loss 

of water such that reduction in green density is proportional to increase in velocity squared

Acoustic Verification of Log Supply for 

Visually Graded Lumber

The introduction of Verified Visual Grading (VVG) in New 

Zealand has been the response to variability in the design 

strength of visually graded lumber, typical of younger 

plantation-grown softwood resources around the world. Following an extensive consultation process, new standards 

and building regulations were introduced in New Zealand in 

2006 with full compliance required by early 2007. According to the new standards, all visually graded lumber became 

subject to a sample proof test (1 in 1,000 pieces of lumber). 

A 30-sample rolling average must exceed the requirements 

for MOE and MOR, meeting both average and minimum 

standards. An implication of these new VVG standards is 

that the stiffness of log supply becomes even more critical 

to ensure that suitable logs are processed to meet end-of-line 

proof testing standards. Otherwise, structural lumber is cut 

and processed at significant cost, only to find that it does not 

meet end-of-line stiffness standards. Acoustic tools provide 

valuable guidance and decision support for the forest and 

wood processing sector to meet these new standards.

Assessing Wood Quality of 

Standing Trees

A logical and desirable extension from log acoustic assessment is to apply the technology to measure wood properties 

in standing trees, thereby providing timber sellers and purchasers with a means for improved harvest scheduling and 

timber marketing based on the potential yield of stress-graded products that can be obtained from trees within a stand. 

The applicability of using acoustic waves to assess intrinsic 

wood properties of standing trees has been validated by 

many researchers around the world (Nanami et al. 1992a,b, 

1993; Wang 1999; Ikeda and Kino 2000; Huang 2000; Wang 

Figure 8.9—Chronological plot of pulp fiber length as each 

log group was processed (Clark et al. 2003).

Figure 8.10—Chronological plot of pulp WZST strength 

as each log group was processed (Clark et al. 2003).

Figure 8.11—Chronological plot of pulp tear strength as 

each log group was processed (Clark et al. 2003).

Acoustic Assessment of Wood Quality in Trees and Logs
General Technical Report FPL–GTR–238

94

et al. 2001, 2005; Lindstrom et al. 2002). A typical approach 

for measuring acoustic velocity in standing trees involves 

inserting two sensor probes (transmit probe and receiver 

probe) into the sapwood and introducing acoustic energy 

into the tree through a hammer impact. Unlike the resonance 

method, which obtains the weighted average velocity by 

analyzing whole wave signals transmitted between the ends 

of a log, the standing tree acoustic tool measures TOF for a 

single pulse wave passing through the tree trunk from transmit probe to receiver probe. 

Measuring Wood Properties of Standing 

Trees

Several trial studies aimed at proving the acoustic concept 

for measuring acoustic velocity and wood properties of 

standing trees have been conducted in the United States and 

New Zealand (Wang et al. 2005). A total of 352 trees were 

tested in 2003 and 2004. The species tested included Sitka 

spruce (Picea sitchensis), western hemlock (Tsuga heterophylla), jack pine (Pinus banksiana), ponderosa pine (Pinus 

ponderosa), and radiata pine (Pinus radiata). The trial data 

showed a good linear correlation between tree velocity 

and log velocity for each species tested. The relationship is 

characterized by the coefficient of determination (R2) in the 

range of 0.71 and 0.93. However, further analysis revealed a 

skewed relationship between tree acoustic measurement and 

log acoustic measurement. Observed tree velocities were 

found significantly higher than log velocities. The results 

support the hypothesis that TOF measurement in standing 

trees is likely dominated by dilatational or quasi-dilatational 

waves rather than one-dimensional plane waves, as in the 

case of logs. 

Because of the significant deviation in velocity and the 

skewed relationship between tree and log measurements, 

tree velocity measured by the TOF method needs to be interpreted differently when assessing wood properties of standing trees. To make appropriate adjustments on observed tree 

velocities, Wang et al. (2005) developed two models (multivariate regression model and dilatational wave model) 

for the species evaluated in those trials. As an example, 

Figure 8.12 shows the relationship between tree velocities 

adjusted through a multivariate regression model and log 

velocities. Their results indicated that both the multivariate 

regression model and dilatational wave model were effective in eliminating the deviation between tree and log velocity and reducing the variability in velocity prediction.

With simple velocity measurements, individual trees and 

stands can be evaluated and sorted for their structural quality and stumpage value. In a series of studies evaluating tree 

quality in terms of structural performance, Ikeda and Kino 

(2000) found highly significant correlations between tree velocity and MOE of logs and square sawn timbers. Through 

several mill trials, Huang (2000) demonstrated that trees 

with the potential to produce high and low stiffness lumber 

can be identified by tree acoustic velocity alone. The upper 

15% and lower quartile of the population can be sorted by 

high and low velocity, respectively. 

For standing trees, going from velocity measurement to 

wood property prediction is also a necessary step for many 

applications. Until recently, post-harvest NDE methods 

such as lumber E-rating, machine stress rating, and ultrasound veneer grading have been the standard procedures for 

evaluating wood stiffness and strength. The timber owner 

does not have a reliable way to assess the value of the final 

product prior to harvest. Recent wood quality research has 

shown that a range of wood and fiber properties can be predicted through a simple acoustic measurement in standing 

trees. Figures 8.13 and 8.14 show the relationships between 

tree acoustic velocity and MOE and microfibril angle (MFA) 

of core samples from trees measured by x-ray densitometry, 

diffractometry, and image analysis. 

Assessing Silvicultural Treatment Effects

Quality and intrinsic wood properties of trees are generally affected by silvicultural practices, especially by stand 

density. Some silvicultural practices not only increase the 

biomass production of trees but also might improve the 

quality of the wood in trees. Nakamura (1996) used ultrasonically induced waves to assess Todo-fir and larch trees 

and observed significant differences in acoustic velocities 

and acoustic-determined MOE for trees in forest stands at 

different locations and trees of different ages. 

Wang (1999) examined the effect of thinning treatments on 

both acoustic and static bending properties of young growth 

western hemlock and Sitka spruce trees obtained from 

seven sites in southeast Alaska. He found that trees with 

higher acoustic velocity and stiffness were mostly found 

in unthinned control stands and stands that received light 

thinning, whereas the lowest values were found in stands 

that received heavy and medium thinning. A typical trend of 

Figure 8.12—Tree acoustic velocity (adjusted) versus log 

acoustic velocity (Wang et al. 2007).

1,000

2,000

3,000

4,000

5,000

1,000 2,000 3,000 4,000 5,000

Log velocity (m/s)

Adjusted tree velocity (m/s)

Sitka spruce

Western hemlock

Jack pine

Ponderosa pine

Radiata pine

Chapter 8
Nondestructive Evaluation of Wood: Second Edition

95

acoustic and static MOE as a function of thinning regimes 

is illustrated in Figure 8.15. These results were encouraging 

and indicated that TOF acoustic technology may be used in 

the future to monitor wood property changes in trees and 

stands and to determine how environmental conditions and 

silvicultural innovations affect wood and fiber properties so 

that the most effective treatment can be selected for future 

plantations for desired fiber quality. 

Assessing Young Trees for Genetic 

Improvement

The future of the forest industry lies in fast-grown plantations. The economic imperative continuously seeks shorter 

rotations to meet the needs of a growing market. Young 

plantations will contain a higher percentage of juvenile 

wood, thus creating a lower quality and more variable 

wood resource for industry to process (Kennedy 1995). 

Consequently, genetic improvement of juvenile wood properties is now receiving attention and getting higher priority 

in research. To help capture genetic opportunities, there is a 

need to determine wood quality at an early age (Lindstrom 

et al. 2002). The major challenge in operational tree improvement programs is to develop rapid and cost-effective 

assessment methods for selecting candidate trees with superior wood quality trait. 

Wood stiffness is the most important property of structural 

lumber. The attractiveness of using MOE as a breeding 

criterion has been widely recognized in the forest industry 

(Addis et al. 2000a). In an investigation of sugi (C. japonica) clones from three different growth-rate groups, Hirakawa and Fujisawa (1995) found that juvenile wood in stiffer 

clones is much stiffer than mature wood of less stiff clones 

in all three growth categories. Similarly, Addis et al. (1998) 

reported that with radiata pine there is little difference in 

wood quality between juvenile wood of high stiffness trees 

and mature wood of low stiffness trees. Therefore the 

ability of selecting high stiffness trees opens the door to 

genetic improvement for future plantations. 

With the ability to nondestructively assess wood properties 

of standing trees and raw log materials, acoustic methods 

have quickly been recognized as a useful tool in tree breeding programs (Walker and Nakada 1999; Huang et al. 2003). 

Lindstrom et al. (2002) investigated the possibility of selecting Pinus radiata clones with high MOE and found that 

acoustic measurement yielded results similar to traditional 

destructive and high-cost static bending methods. They conclude that acoustic tools could provide opportunities of mass 

screening for stiffness of fast-grown radiata pine clones at a 

very early age. 

Figure 8.13—Relationship between tree acoustic velocity 

and modulus of elasticity (MOE) of the core samples measured by Silvascan-2 for radiata pine (Wang et al. 2007). 

Figure 8.14—Relationship between tree acoustic velocity 

and microfibril angle (MFA) of the core samples measured 

by Silvascan-2 for radiata pine (Wang et al. 2007).

Figure 8.15—Modulus of elasticity (MOE) of young growth 

Sitka spruce in relation to thinning treatment (Wang et al. 

2000).

R2 = 0.895

0

2

4

6

8

10

12

14

16

18

1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0

Tree acoustic velocity (km/s)

MOE (Silvascan-2) (GPa)

R2 = 0.855

0

5

10

15

20

25

30

35

40

45

1.0 1.5 2.0 2.5 3.0 3.5

Tree acoustic velocity (km/s)

MFA (Silvascan-2) (degree)

0

2

4

6

8

10

12

14

16

Control Light Medium Heavy

M

O

E (G

Pa)

Thinning treatment

Acoustic MOE of trees

Static MOE of small clear wood

Acoustic Assessment of Wood Quality in Trees and Logs
General Technical Report FPL–GTR–238

96

Evaluation of Plantation Resource for 

Wood Quality

In applying acoustic technology to a plantation resource, 

typically a number of stages will be considered. For example, a program to define wood quality for structural applications could have the goal of targeting extraction of greatest 

commercial value from the forest resource available, while 

recognizing the need to solve the problem of relatively lowstiffness wood in younger stands of much of the softwood 

plantation resource coming available in many countries.

Stages in a wood quality assessment program using acoustic 

hand tools for trial work and stand selection could include 

the following:

•	 Undertake a forest survey by mapping acoustic velocity at stand level across a range of topography, altitude, 

soils, ages, and silviculture (sample approximately 50 

stands)

•	 Confirm the relationship between average standing tree 

velocity and average log velocity by felling 20 to 30 

trees on each of 15 or more sites. Confirm velocity pattern up tree on a sub-sample of these

•	 Saw a sample of logs and confirm static MOE and 

MOR of lumber and grade out turn, relative to recorded 

log and standing tree velocity

•	 Correlate static MOE with predicted MOE from 

commercial testing devices (x-ray density, acoustic, 

mechanical bending)

By following this approach, the plantation resource can be 

characterized according to stiffness to enable management, 

planning, harvesting, and wood processing to be carried 

out in a way that optimizes stiffness-related value from the 

resource.

Operational Application of 

Acoustic Technology

As operational application of acoustic technology is considered, there is a recognized need for the technology to 

be applied at a number of stages in the operational value 

chain, from timberlands through to the processing site. Figure 8.16 illustrates potential application of acoustic technology through the forest-to-mill wood supply chain. Different 

tools have been developed to suit each specific application.

Standing tree assessment is relevant for tree breeding, preharvest assessment (PHA) for forest or stumpage valuation, 

and decision support at time of thinning, where trees cannot 

be cut. The harvesting processor application provides decision support for log-making as well as collation of data for 

subsequent forest management, harvest planning, and valuation. Felled stems can be tested to assist in optimization 

of value capture in log-making, while logs can be assessed 

for ranking of average wood quality (stiffness and related 

characteristics) or segregated for supply to different customers or processing options. Automated on-line log testing is 

relevant to valuation of log supplies, ranking of log supply 

sources, prediction of MSR yields for output planning, and 

providing a very efficient means for segregation of logs 

based on quality and suitability for different customers or 

processing options.

Automated On-Line Log Sorting/Grading

Although initial operational deployment of acoustic technology was undertaken using hand tools, automated log sorting 

or grading is now available, enabling log-by-log optimization of sawcut patterns to maximize the extracted value 

from a mixed quality log resource (IFI 2008; Carter 2011). 

Figure 8.17 shows the acoustic log grading operation in a 

sawmill where a Hitman LG640 automatic log grader is installed after the debarker where it measures logs just prior to 

sawing. The machine uses acoustic wave velocity to assess 

wood quality by swinging a hammer at the end of each log 

Figure 8.16—Schematic outlining potential application of acoustic technology through 

the operational value chain.

Chapter 8
Nondestructive Evaluation of Wood: Second Edition

97

going through the system and recording the acoustic signals 

using a microphone. By analyzing the resonant frequency 

of the longitudinal acoustic wave signals and calculating 

the acoustic velocity of the hit along the log, the machine is 

able to provide an immediate and accurate assessment of the 

stiffness of the wood and therefore its suitability for use as 

structural or nonstructural products. Logs are queued on the 

in-feed deck, and as each rolls into the machine infeed, its 

acoustic reading is displayed on the operator’s control panel, 

which allows the operator to adjust the cut pattern to maximize the structural lumber grade out turn and profitability. 

The operational trial at Waimea Sawmillers in Nelson, New 

Zealand, has demonstrated at least a 3% gain in machine 

stress grading (MSG) recovery since installing the Hitman 

LG640 in-line acoustic grader. 

Processor Head Acoustic Technology in 

Forest Harvest Operations

Recent research and development on acoustic technologies 

has brought the operational assessment of acoustic velocity as a measure of stiffness into timber harvesting and log 

making sectors of the forest and wood processing industry. 

Fibre-gen has developed a Hitman acoustic measurement 

system to be mounted in a processor head (Fig. 8.18). 

Previously reported field trials involved the integration of 

Hitman PH330 mechanized acoustic measurement devices 

into a Ponsse processor head in UK, two Waratah 626 processor heads in New Zealand, and one Waratah 624 processor head in Oregon, USA (Carter 2011). Further validation 

studies have been run in West Australia and Nelson, New 

Zealand. 

Figure 8.17—Hitman LG640 automated log grader installed on the 

debarker out-feed. (Photo on the right shows the swinging hammer 

and microphone sensor.) (Photo courtesy of Fibre-gen Ltd.)

Figure 8.18—Schematics of a processor head integrated with an acoustic measurement system enabling tree quality evaluation during harvesting operation. (Drawing 

courtesy of Fibre-gen Ltd.)

Acoustic Assessment of Wood Quality in Trees and Logs
General Technical Report FPL–GTR–238

98

The system measures the longitudinal TOF of a sound wave 

in the stem section held by the processor head, immediately 

following a cross-cut. With the acoustic velocity displayed 

in the cab, the operator can cut structural or nonstructural 

log product from the next section of stem and segregate 

the logs, based on a user-defined velocity threshold level. 

These trial studies have demonstrated the use of processor 

head acoustic technology in forest harvesting operations as a 

means to enable cutting to the correct log length and segregating logs for higher value structural markets. This avoids 

the need to test logs after they are cut to length and having 

to subsequently re-cut those logs that fail to meet the log 

buyer’s acoustic speed specifications. Costs of sorting and 

re-cutting logs to a shorter length can significantly expend 

resources and generate waste, with the alternative of selling 

nonstructural logs at a discount in nonpreferred lengths. 

Two alternative assessment strategies have been highlighted 

in recent performance validation projects: increase the 

average stiffness of a segregated log batch, or meet some 

specific minimum log velocity specifications defined by 

log resonance measurement (Carter et al. 2013). Processor 

head acoustic technology was able to identify lower stiffness sawlogs in the forest before any mill processing was 

undertaken. An example-based analysis of benefits and costs 

showed positive net benefits from the potential use of this 

technology to segregate structural logs from within stands 

otherwise considered nonstructural. 

Processor head acoustic technology has now been demonstrated through a series of research, development, and validation projects over a period of 6 years and has been confirmed capable of delivering effective segregation to reliably 

meet a range of market and end user requirements. 

Effect of Environmental Temperature on 

Field Acoustic Assessment of Trees and 

Logs

Depending on the geographic locations and the timing of 

forest operations, trees and logs can be acoustically tested 

and evaluated in different climates or different seasons. The 

wood temperature of trees and logs at the time of testing 

could range from above 30 °C to well below freezing. Previous studies on small wood samples and structural lumber 

suggest a direct effect of temperature on acoustic properties 

of wood. The observed effect of temperature on acoustic 

velocity in wood below fiber saturation point (FSP) is in 

agreement with generally accepted information on this subject, but conflicting results were reported for wood above 

FSP when wood temperature transitioned from above freezing to below freezing. There has been limited data showing 

the effect of temperature on acoustic velocity of wood measured on standing trees and logs. Little is known about the 

effect of seasonal temperature changes on acoustic evaluation of standing trees and freshly cut green logs. This information is important to field operations when the acoustic 

technology is used to assess the quality and values of logs, 

trees, and stands. 

The laboratory study on red pine (Pinus resinosa) small 

clear specimens conducted by Gao et al. (2012) showed a 

significant change in acoustic properties of wood as wood 

temperature changed from –45 to 35 °C. A dramatic shift 

in acoustic velocity and energy loss was observed when the 

wood temperature changed to above or below the freezing 

point. Gao et al. (2013) further investigated the effects of 

environmental temperature on acoustic wave velocity of 

wood in standing trees and freshly cut logs over a duration 

of 12 consecutive months. Results indicated that ambient 

temperature had a significant effect on acoustic velocities 

of trees and logs in winter when temperatures were below 

freezing point. Acoustic velocities increased dramatically 

as the ambient temperature dropped below 0 °C, but then 

the change became less significant when the temperature 

decreased to below –2.5 °C. Above freezing point, acoustic 

velocities were found less sensitive to ambient temperature 

changes. Gao et al. (2013) further developed analytical 

models based on the change in acoustic velocity relative to 

a base temperature 20 °C. The relationships between the 

percentage of change in velocity (ΔV, %) and ambient 

temperature for red pine trees and logs are shown in Figure 8.19. Table 8.1 tabulates ΔV values for red pine trees and 

logs that were derived from the analytical models. Because 

acoustic velocity is more sensitive to temperature changes 

below freezing than above freezing, ΔV values are tabulated 

at a 1 °C increments from –11 to –2.5 °C and at 2 °C 

increments from 0 to 30 °C. From a practical standpoint, 

acoustic velocities of trees and logs measured well above 

freezing may not need a temperature adjustment, because 

the velocity changes observed were not large enough to 

have a practical significance.

From a practical standpoint, acoustic velocities of trees and 

logs measured at different climates or different seasons can 

Figure 8.19—Effect of ambient temperature on change in 

acoustic velocity relative to 20 °C (Gao et al. 2013).

Chapter 8
Nondestructive Evaluation of Wood: Second Edition

99

be adjusted to a standard temperature if measurements are 

conducted well above freezing temperatures or well below 

freezing temperatures. However, measurements conducted 

around freezing temperatures could cause complications in 

making temperature adjustments. Users should try to avoid 

conducting field acoustic testing when wood temperature is 

around the freezing point.

Literature Cited

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Acoustic Assessment of Wood Quality in Trees and Logs

Table 8.1—Velocity change (∆V) of red pine trees and 

logs in the range of –11 to 30 °C (Gao et al. 2013) 

Ambient 

temperature 

(°C) 

Tree 

ΔV

(%) 

Log 

ΔV

(%) 

Ambient 

temperature 

(°C) 

Tree

ΔV

(%) 

Log

ΔV 

(%) 

–11 21.0 27.5 6 2.5 0.8 

–10 20.6 26.6 8 2.2 0.6 

–9 20.1 25.7 10 1.8 0.5 

–8 19.7 24.9 12 1.4 0.4 

–7 19.2 24.0 14 1.1 0.3 

–6 18.8 23.1 16 0.7 0.2 

–5 18.3 22.2 18 0.4 0.1 

–4 17.9 21.3 20 0.0 0.0 

–3 17.4 20.4 22 –0.4 –0.1 

–2.5 17.2 20.0 24 –0.7 –0.2 

0 3.6 1.1 26 –1.1 –0.3 

2 3.2 1.0 28 –1.4 –0.4 

4 2.9 0.9 30 –1.8 –0.5 
General Technical Report FPL–GTR–238

100

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Acoustic Assessment of Wood Quality in Trees and Logs

Nondestructive Evaluation of Wood: Second Edition

103

Chapter 9

Laser Scanning of Logs and Lumber

R. Edward Thomas, Research Scientist

Northern Research Station, Princeton, West Virginia

The use of laser scanning technology has become an accepted and economical means of determining the size, 

shape, and features of logs and lumber. The key components 

of a laser-scanning system are a laser line generator and a 

camera. Figure 9.1 shows an industrial laser scanner unit 

manufactured by JoeScan (Vancouver, Washington) that can 

be used either with logs or lumber. On this particular scanner, the laser aperture is on the left and the camera is located 

to the right. Laser scanner design has several variations, 

as some have multiple laser beams and a single camera, 

whereas other scanners have two cameras and a single laser. 

Using two cameras avoids areas of missing data that occur 

when a protrusion on the surface prevents a camera from 

“seeing” the laser. Rather than project a single point, the 

scanners project a line onto the surface of the object being 

scanned. Figure 9.2 shows the laser line projected onto the 

surface of a log.

The camera and laser are separated by a measured distance 

and the camera is aimed toward the laser line at a specific 

angle. Using the camera angle, distance between camera 

and laser, and triangulation, the distance of points along the 

laser line projected on an object can be determined. In many 

instances, scanning systems are calibrated using an object 

with a known shape and size. Figure 9.3 shows the mechanics of projecting a point from the laser line onto the camera’s image plane. Processing the location of points on the 

laser line onto the camera’s image plane allows the shape 

and size of an object to be calculated.

Laser scanning systems must be able to discriminate between the laser line and the surface being scanned. Most 

scanners use lasers in the visible spectrum while others scan 

in the invisible (near infrared and ultraviolet) spectrum. Visible spectrum scanners must operate out of direct sunlight, 

exposure to which can prevent the scanner from finding the 

laser on the surface being scanned.

Log Scanning

There are two typical methods of laser scanning logs. The 

first involves a stand-alone scanning system where the log 

passes through an array of three to four laser scanning 

heads arranged around the log (Figure 9.4a). Typically, the 

logs are on a chain and are passed through the scanner. In 

the second method, a series of laser scanning heads are 

mounted at the headrig of a sawmill (Figure 9.4b). 

Figure 9.1—Typical industrial laser scan head used in log 

and lumber applications.

Figure 9.2—Laser scan line as projected onto a log during 

3D scanning. 

Figure 9.3—Diagram of scanning head 

showing laser, camera, and image plane.
General Technical Report FPL–GTR–238

104

Scanning at the headrig is more easily integrated into existing mills, as the first approach requires the addition of a separate log scanning station. However, the first approach can 

provide higher density scanning with fewer areas of missing 

data. The LogEye series of log scanners (Innovativ Vision, 

Linköping, Sweden) are one example of this type of scanner. 

Table 9.1 lists additional manufacturers of laser scanning 

equipment for logs and stems.

During laser scanning, a series of scan lines are projected 

around the circumference of the log along its length. The 

resolution of the scanning system is the distance between 

the scan lines. The shorter the distance between the scan 

lines, the higher the resolution. The resolution of a log laser 

scanning system is normally between 6 and 24 inches, although much higher resolutions are possible. The higher the 

resolution, the greater the detail captured about the log and 

the greater the amount of data that must be processed. Figure 9.5 shows a high-resolution scan of a hardwood log with 

a resolution of 0.0625 inches. This scan is from an experimental scanner developed by the Forest Service (Thomas et 

al. 2008). It is easy to discern defects in the image as well as 

the shape of the log. 

Laser scanning is normally done just before processing and 

is used to determine the diameter, shape (round, elliptical, 

fluted, etc.), length, sweep, and taper of logs. To efficiently 

convert logs into lumber, these aspects must be accurately 

measured. In hardwood sawmills, laser scanning is chiefly 

used to accurately position the log, given diameter, taper, 

and sweep measurements, such that the minimum opening 

face can be determined and sawn. The minimum opening 

face is the width of the first board cut from the face of a log 

(Malcolm 2000). If the width of the board is too narrow, 

then the grade and value of the board is decreased. If the 

width of the board is too wide, then too much wood was left 

in slab and is wasted. In addition, the most valuable wood 

in a hardwood log is normally in the jacket boards, the first 

boards sawn from the log. If the log has significant amounts 

of taper or sweep, the log can be positioned to minimize 

volume loss (Malcolm 2000).

The process of sawing hardwood lumber is significantly different from that of sawing softwoods. In hardwoods, the log 

is continually examined for the presence of defects during 

sawing, with the goal of producing lumber with a minimal 

number of defects. In softwoods, the presence of defects is 

much less of a concern and the sawing pattern can be optimally determined before sawing begins. A sawing pattern 

describes the steps involved in sawing the log and the sizes 

of boards that will be produced. Figure 9.6 shows the end 

view of a laser-scanned hardwood log with a proposed sawing pattern showing the fitting of boards within the log. With 

such a system, the volume yield of a log is known before 

sawing. More importantly, the grade yield and value of the 

lumber can be estimated and optimized before sawing.

With high-resolution laser scanning are additional processing applications. Given a high-resolution scan as shown in 

Figure 9.5, log volumes can be determined that are comparable in accuracy to those obtained using immersion tanks 

(Thomas and Bennett 2014). In addition, methods have 

been developed for automated detection of severe defects 

on hardwood log surfaces. (Thomas et al. 2008) Such data 

permits optimization of the sawing process to produce maximum value (Lin et al. 2011).

Laser Lumber Scanning

Laser scanning is commonly used in several aspects of lumber production and quality assessment in both hardwoods 

Figure 9.4—Typical configurations for laser scanning 

logs: (a) three headed scanner where log passes through 

the middle of scanning array; (b) array of laser scanning 

heads configured to scan front and back sides of logs on 

the headrig (image courtesy of JoeScan, Inc.). 

Figure 9.5—High-resolution 3D scan of a hardwood log.

Chapter 9
Nondestructive Evaluation of Wood: Second Edition

Laser Scanning of Logs and Lumber 105

Table 9.1—List of laser scanning equipment manufacturers

 Application Scan type 

Company Contact URL Logs Lumber Profile Tracheid 

ATB Blank GmbH 49.0.7300.9218.0 www.atb-technology.de No Yes Yes Yes 

Am Priel 2 info@atb-technology.de 

D-89297 Roggenburg 

Germany 

Automation & Electronics 64.7.574.6223 www.automationelec.com Yes Yes Yes No 

4 Portside Drive 

PO Box 4044 

sales@automationelec.com 

Mount Maunganui 

New Zealand 

COE USNR 1.800.289.8767 www.coemfg.com Yes Yes Yes Yes 

PO Box 310 info@usnr.com www.usnr.com 

Woodland, WA 98674 

USA 

Comact 1.418.228.8911 www.comact.com Yes Yes Yes No 

4000 40e Rue Ouest 

Saint-Georges, QC G5Y 8G4 

Canada 

Innovativ Vision AB 46.13.460.51.00 www.ivab.se Yes Yes Yes Yes 

Idögatan 10 info@ivab.se 

SE-58278 Linköping 

Sweden 

JoeScan 1.360.993.0069 joescan.com Yes Yes Yes No 

4510 NE 68th Dr., Ste. 124 sales@joescan.com 

Vancouver, WA 98661 

USA 

LIMAB UK Ltd 44.0.1823.668633 limab.co.uk Yes Yes Yes No 

Unit 3L Westpark 26 

Wellington TA21 9AD 

England 

LMI 1.604.636.1011 www.lmi3.com Yes Yes Yes No 

1673 Cliveden Ave. 

Delta, BC V3M 6V5 

Canada 

Michael Weinig AG 49.0.9341.86.0 www.weinig.com No Yes Yes Yes 

Weinigstraße 2/4 info@weinig.com 

97941 Tauberbischofsheim 

Germany 

MicroTec GmbH 39.0472.273.611 www.microtec.eu Yes Yes Yes Yes 

Julius Durst Str. 98 info@microtec.eu 

I-39042 Brixen (BZ) 

Italy 

SOFTAC Systems Ltd. 1.604.888.9507 www.softacsystems.com No Yes Yes No 

#220 - 19358 96th Ave. sales@softacsys.com 

Surrey, BC V4N 4C1 

Canada 
General Technical Report FPL–GTR–238

106

and softwoods. Figure 9.7 shows a typical configuration of 

a laser scanner in a lumber mill. This setup uses two lasers 

to capture two data types. The rightmost laser aimed at the 

board in shallow angle is for profile scanning. Profile scanning is used to detect void and wane on the edges of the 

board as well as splits, cracks, and holes. The laser perpendicular to the board is used to scan for the tracheid effect. 

This type of scanning is used to determine the angle of 

wood grain along the length of the board.

Profile Scanning

Profile scanning yields accurate measurements of the 

amount of wane, as well board width, and the distance 

between two edges with wane. This use is perhaps the 

largest application of laser scanning—the measurement 

and automated detection of wane and void on the edges of 

lumber. The amount of wane affects the grade and value of 

almost all hardwood and softwood boards and structural 

lumber (SPIB 2013; NELMA 2013; NHLA 2011). In addition, the accurate measurements of wane and board size 

can be used to automate the edging and trimming of lumber 

to produce maximum grade and value (Abbott et al. 2001). 

Several companies manufacturer laser scanners and systems 

for measuring lumber, determining wane, and optimizing 

edging and trimming of lumber (Table 9.1). 

Figure 9.8 shows a sample laser profile gray-level image. In 

the image, the normal, level surface of the board is shaded 

as dark gray. Slightly higher areas of the board are represented as darker shades of gray. Lower areas are shown as 

progressively lighter shades of gray. On the right hand side 

of the photo, along the upper edge, wane is represented in 

a narrow region along the edge where the dark gray of the 

normal board surface fades to almost white. In addition, 

taper can be seen along the upper edge of the image as the 

board outline narrows. A white jagged line inside the board 

above the wane area indicates a split, crack, or tear out in 

the board.

Tracheid Effect Scanning

When a laser beam strikes the surface of wood, the beam is 

propagated along the fibers of the wood, this beam propagation characteristic is commonly referred to as the tracheid 

effect. The angle of glow of the laser beam of the wood 

surface shows the angle of the wood grain. Thus, the angle 

of the wood around defects such as knots and pitch pockets 

can be detected and used as indicators for defects (Jolma 

and Mäkyen 2007). One of the first commercial systems to 

implement tracheid scanning was Cell-Scan (Barr-Mullin, 

Raleigh, North Carolina). This system was used to locate 

defects in hardwoods and softwoods for the production of 

furniture and mouldings. In addition, the tracheid effect 

can be used to detect sloped grain and as a result areas of 

potential weakness in the board (Olsson et al. 2013). Olsson 

Figure 9.6—End view of laser scan imagery with proposed 

sawing pattern. Large blue area to left is potential internal 

knot defect.

Figure 9.7—Diagram of laser scanner used in a 

lumber mill.

Figure 9.8—Sample laser profile image of a board.

Chapter 9
Nondestructive Evaluation of Wood: Second Edition

107

et al. (2013) used a WoodEye commercial lumber scanner 

(Innovativ Vision, Linköping, Sweden) to collect tracheid 

effect imagery then correlated it to the stiffness of the wood, 

allowing the use of tracheid imagery data as a strength predictor. Table 9.1 lists known manufacturers of tracheid scanning equipment.

Figure 9.9 shows a series of tracheid effect examples for 

various defect and grain types on a sample of white pine 

(Pinus strobus). In this series of pictures, the laser beam is 

slightly square and fixed in position to provide a consistent 

beam along the length of the board. What is important to 

notice is how the shape of the laser beam changes when areas of the board with different grain patterns are illuminated. 

Figures 9.9a and b show two examples of clear grain. However in 9.9b, the grain has a swirl and the edges of area illuminated by the laser are bent along the angle of the grain. 

Figure 9.9c shows an illuminated pitch pocket oriented 

along length of the wood grain. Figures 9.9d and 9.9e show 

sloped grain near knots. What is noteworthy is that the laser 

light is conducted almost completely into the board surface 

(note the decreased area or halo of light around the laser 

point) rather than along the surface as seen in Figures 9.9a 

and 9.9b. In addition, Figure 9.9e illuminates a pitch pocket, 

but the sloped grain characteristics conduct most light into 

the board, making this example significantly different than 

the example in Figure 9.9c. Finally, Figure 9.9f is a small 

resinous knot that is completely illuminated by the laser. In 

addition, the laser light is conducted along the length of the 

swirled grain toward the bottom of the image (Fig 9.9f).

Acknowledgments

Figures 9.7 and 9.8 are provided courtesy of Phillip Araman, 

U.S. Forest Service, Southern Research Station, Blacksburg, 

Virginia. Figure 9.4b is provided courtesy of JoeScan Inc., 

Vancouver, Washington. 

Literature Cited

Abbott, A.L.; Schmoldt, D.L.; Araman, P.A.; Lee, S-M. 

2001. Automatic scanning of rough hardwood lumber for 

edging and trimming. In: Proceedings of ScanTech International Conference, November 4–6, 2001, Seattle, WA. Wood 

Machining Institute, Berkeley, CA. p. 101–110.

Jolma, I. P.; Mäkyen, A.J. 2007. The detection of knots in 

wood materials using the tracheid effect. Proc. SPIE 7022. 

Advanced Laser Technologies 2007, 70220G.

Malcolm, F.B. 2000. A simplified procedure for developing 

grade lumber from hardwood logs. Res. Note FPL–RN–098. 

Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 13 p.

Lin, W.; Wang, J.; Thomas, R.E. 2011. A three-dimensional 

optimal sawing system for small sawmills in central Appalachia. In: Proceedings of the 17th Central Hardwoods Forest 

Conference, April 5–7, 2010, Lexington, KY. Gen. Tech. 

Rep. P-78. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 

p. 67–75.

NELMA. 2013. Standard grading rules for Northeastern 

Lumber. Cumberland Center, ME: Northeastern Lumber 

Manufacturers Association. 

NHLA. 2011. Rules for the measurement and inspection of 

hardwood and cypress. Memphis, TN: National Hardwood 

Lumber Association. 104 p.

Olsson, A.; Oscarsson, J.; Serrano, E.; Källsner, B.; Johansson, M.; Enquist, B. 2013. Prediction of timber bending 

strength and in-member cross-sectional stiffness variation 

on the basis of local wood fibre orientation. European Journal of Wood Products. 71: 319–333.

Thomas, R.; Edward, Thomas, L.; Shaffer, C. 2008. Defect 

detection on hardwood logs using high-resolution laser scan 

data. In: Proceedings of the 15th International Symposium 

on Nondestructive Testing of Wood, September 10–12, 

2007, Duluth, MN. Natural Resources Research Institute, 

University of Minnesota, Duluth: 163–167.

Thomas, E.; Bennett, N. 2014. In: Groninger, John W.; 

Holzmueller, Eric J.; Nielsen, Clayton K.; Dey, Daniel C., 

eds. Proceedings, 19th Central Hardwood Forest Conference, March 10–12, 2014, Carbondale, IL. Gen. Tech. Rep. 

NRS–P–142. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 

p. 299–310. 

SPIB. 2013. Standard grading rules for southern pine 

lumber. Pensacola, FL: Southern Pine Inspection Bureau.

Figure 9.9—Photo demonstrating the tracheid effect of 

a laser for clear wood and different defect types on white 

pine (Pinus strobus): (a) clear wood; (b) clear wood with 

swirled grain; (c) pitch pocket; (d) sloped grain near 

a knot; (e) sloped grain with a pitch pocket; (f) small, 

resinous pin knot.

Laser Scanning of Logs and Lumber

Nondestructive Evaluation of Wood: Second Edition

109

Chapter 10

Ultrasonic Veneer Grading

Brian K. Brashaw, Program Manager

Forest Products Marketing Unit, USDA Forest Service, Madison, Wisconsin

The development and growth of the laminated veneer lumber (LVL) industry has been a direct result of the application 

of ultrasonic nondestructive evaluation (NDE) methods for 

assessing the properties of wood veneer. Individual veneer 

sheets are fed through opposing transducers that send and 

receive a wave. This wave travels longitudinally through 

the veneer. The time it takes for the wave to travel between 

the transducers is referred to as the propagation time and is 

used to calculate the velocity of the wave. With previously 

determined empirical relationships that relate wave velocity to wood stiffness and strength, each sheet of veneer is 

assigned to a strength category based on the velocity of the 

wave traveling through it. These relationships were derived 

from fundamental research studies in which samples of veneer were tested to determine the velocity at which a wave 

traveled through them and were then destructively tested to 

determine their strength and stiffness. Knowing the properties of each sheet of veneer enables manufacturers to design 

and produce engineered wood products that have properties 

with predictable performance characteristics.

Ultrasonic veneer grading is based on elementary onedimensional stress wave theory, which states that the 

modulus of elasticity (MOE) of a material is a function of 

the velocity C of stress wave propagation and mass density 

ρ as defined by

Metriguard, Inc. (Pullman, Washington), is the only commercial manufacturer of ultrasound veneer grading equipment. Their first commercial system was installed in 1977 at 

Trus Joist Corporation’s first LVL plant in Eugene, Oregon 

(Pieters 1979). This system was capable of production 

speeds of 100 ft/min (30.5 m/min). Douglas-fir veneer was 

sorted into three grades based on veneer propagation time in 

the longitudinal grain direction. Figure 10.1 shows an original Metriguard Model 2600 commercial ultrasonic veneer 

grader.

Technical advances have coupled the velocity of the wave 

with the use of a radiofrequency probe to determine the 

specific gravity and moisture content of each veneer sheet 

(Logan 2000). Research resulted in the development of better relationships between the temperature of wood and wave 

velocity. These improvements resulted in the ability to 

sort veneer on the basis of calculated MOE based on the 

wave velocity and specific gravity (Logan 2000).

Figure 10.2 shows the Metriguard Model 2800 DME capable of achieving production speeds of 300 to 425 ft/min 

(91.4 to 129.5 m/min). It is equipped with wood temperature 

compensation, radiofrequency measurement of specific 

gravity and moisture content, and sheet width and thickness 

detection capabilities.

Laminated Veneer Lumber 

Production

In 2000, approximately 150 ultrasonic veneer graders were 

in use around the world, resulting in a grading capacity of 

15 million m3 of veneer for LVL (Logan 2000). Today the 

number of ultrasonic veneer graders in-use totals nearly 200. 

Laminated veneer lumber is manufactured from C and D 

grade rotary-peeled veneer that is typically 0.17 to 0.10 in. 

(4.3 to 2.5 mm) thick. Species that are used for LVL include 

Douglas-fir, southern yellow pine, lodgepole pine, western 

Figure 10.1—Metriguard 2600.

Figure 10.2—Metriguard 2800 DME.

ρ

2 MOE = C
General Technical Report FPL–GTR–238

110

hemlock, western larch, aspen, black spruce, paper birch, 

and red pine. After drying, the veneer is graded through a 

commercial ultrasonic veneer grader into several grades. 

Adhesive is applied to the veneer, which is oriented in the 

longitudinal direction prior to pressing. Typically, LVL is 

manufactured in continuous length billets (1.25 to 3 in. 

thick, 52 in. wide (3.2 to 7.6 cm thick, 1.3 m wide)). The 

LVL is then sawn into sizes based on customer requirements. This LVL is used in many structural products including prefabricated wood I-joists, truss chords, headers, and 

beams.

Trus Joist Corporation (Boise, Idaho) was the initial entrant 

into the LVL industry in the early 1970s. They pioneered 

the manufacturing and grading technology that allowed for 

the exponential growth of LVL since 1992. The annual estimate for LVL production was approximately 9 million ft3

(255,000 m3) from six engineered wood products manufacturers in 1992 (Guss 1994). Laminated veneer lumber 

production increased to 51.9 million ft3 (1.5 million m3) in 

1999 (APA 2000). Approximately 61% of the LVL production was used as flanges for wood I-joists in 1999, followed 

by 31% as headers and beams, and 8% for other applications (APA 2000). Although it would be difficult to estimate 

current production statistics, LVL production has increased 

considerably in recent years.

Technology Development

The concept for determining wave propagation time, stress 

wave velocity, and MOE for veneer was based on lumber 

studies that were completed at Washington State University 

(Pullman, Washington) (Galligan and Courteau 1965; Pellerin and Galligan 1973). The presence of stress waves in 

softwood lumber was detected using a probe that was sensitive to the piezoelectric charge in wood in response to strain 

caused by impact. The MOE for a sample set of 40 pieces 

of Douglas-fir lumber was determined through stress wave 

velocity and density and compared with the corresponding 

static MOE. A correlation coefficient of 0.95 was reported, 

which shows excellent potential for using the stress wave 

timing technique to grade lumber based on MOE.

An effort to apply this technology to veneer was initiated by 

Roy Pellerin of Washington State University (WSU) and Peter Koch of United States Department of Agriculture, Forest 

Service, Southern Forest Experiment Station (New Orleans, 

Louisiana). Rotary cut southern yellow pine veneer for this 

study was shipped to WSU for evaluation of MOE using a 

laboratory built experimental stress wave timer. Modulus of 

elasticity for each veneer sheet was determined from static 

tests conducted on samples obtained from each veneer sheet. 

There was a strong relationship between the static tensile 

MOE and ultrasound MOE as evidenced by a correlation 

coefficient of 0.92 (Koch 1967).

Based on this success, a faster stress wave grading apparatus was constructed by WSU and sent to the Southern 

Experiment Station for an extensive study of 10,350 southern yellow pine veneers that were to be manufactured into 

laminated beams. The goal was to determine the MOE of 

each veneer and use this information to place the stiffest 

veneer in the highly stressed tension and compression zones 

and to place the lowest stiffness near the neutral axis (Koch 

and Woodson 1968). Figure 10.3 shows the WSU stress 

wave timer used in that study. The stress wave was introduced through an anvil impact system and captured by a 

piezoelectric probe.

Each piece of southern yellow pine veneer was 0.17 in. 

thick by 2.75 in. wide and 100 in. long (4.3 mm thick by 

7 cm wide and 2.5 m long). The stress wave velocity was 

determined across a 92-in. (2.6-m) span as an average of 

three consecutive time readings for each veneer. This was 

the basis for sorting the veneer into groups for manufacturing laminated beams. Every 50th veneer was also tested to 

determine the static tensile MOE for comparison against the 

ultrasound MOE. Figure 10.4 shows the regression of static 

MOE and ultrasound MOE for these veneers. The correlation coefficient was 0.94 for the 177 veneers evaluated.

The veneer was then strategically placed during beam layup 

with the stiffest veneers on the outside and those with the 

lowest stiffness near the neutral axis. The resulting beams 

had predictably high static MOE values with low standard 

deviations. These beams were essentially the first experimental LVL manufactured from stress wave graded veneer. 

Subsequent laboratory studies (Hunt and others 1989; Jung 

1979; Jung 1982) have been conducted to investigate the 

concept for use in evaluating veneer strands, in addition to 

examining the effect natural occurring defects have on wave 

behavior. 

Metriguard, Inc., was founded in 1973 to commercialize 

the use of stress wave timing technology for lumber, wood 

composites, paper, and veneer. Metriguard’s first system was 

the portable 239 stress wave timer, designed to measure the 

transit time of an impact-induced wave. The wave was introduced with a pendulum impactor to a clamp attached to the 

Figure 10.3—Washington State University stress wave 

timer.

Chapter 10
Nondestructive Evaluation of Wood: Second Edition

111

sample. Piezoelectric transducers were placed at two points 

of the specimen and used to sense the passing of the wave. 

The time it takes for the wave to travel between the sensors 

was measured and displayed. This time was then used to 

compute wave propagation speed through the material.

Concurrently, Trus Joist was developing commercial LVL 

for use as a flange material for open web wood and steel 

trusses and wood I-joists. They began manufacturing LVL 

in 1972 but experienced wide variation in the mechanical 

properties of the LVL they manufactured. They found that 

there was no relationship between the visual grade mix of 

the Douglas-fir veneer and the strength properties of the 

LVL (Kunesh 1978).

Trus Joist approached Metriguard, Inc., about their portable stress wave timer to grade veneer. Robert Kunesh of 

Trus Joist Corporation and Roy Pellerin of WSU used a 

Metriguard Model 239 to grade 0.10-in.- (2.5-mm-) thick 

Douglas-fir C and D visual grade veneer. The veneer was 

sorted into several groups based on stress wave velocity. 

Laminated veneer lumber was then manufactured from these 

groups and tested to determine the relationships between 

the veneer stress wave velocity and bending and tension 

properties of the LVL billets. Correlation coefficients were 

0.92 between ultrasound MOE and tension MOE and 0.91 

between ultrasound MOE and static bending MOE. The use 

of stress wave NDE technology allowed Trus Joist to accurately assess veneer sheet quality and translate it into LVL 

with predictable properties with low variability (Kunesh 

1978; Pieters 1979).

Based on the success of stress grading veneer, Metriguard 

focused development efforts on building an automated 

production line ultrasonic veneer grading system for Trus 

Joist. The Metriguard 2600 system used rolling ultrasonic 

transducers to input and receive stress waves that traveled 

along the longitudinal length of each veneer sheet. Approximately 60 ultrasonic pulses per second were sent into the 

material, resulting in 50 to 100 time measurements for each 

sheet of 27-in. wide veneer. These measurements were averaged for each sheet of veneer and used to sort the veneer 

into one of several grades based on transit time. Low transit 

time corresponded to high velocities, which is indicative of 

veneers with high stiffness as defined by fundamental onedimensional wave theory.

The first commercial machine developed had a production 

speed of about 100 ft/min (30.5 m/min), which was limited 

by acoustic noise emissions produced by the veneer and by 

ultrasonic intensity of the sending transducer (Logan 1987; 

Logan 2000). Improvements in transducers and signal processing allowed production speeds to increase to 200 ft/min 

(61 m/min) by the early 1990s (Uskoski et al. 1993).

Metriguard continued to refine and develop ultrasonic 

veneer grading technology and equipment and introduced 

the Model 2650 DFX and 2800 DME in 2000. Digital 

signal processing coupled with continued noise reduction 

has increased the stress wave sampling rate by 60%, 

up to 200 ultrasonic pulses per minute. This resulted 

in increased production speeds of 300 to 425 ft/min 

(91.4 to 129.5 m/min) of veneer through the ultrasound 

veneer graders (Logan 2000). Newer/current models can 

operate up to 600 ft/min (e.g., 2655 and 2805). This system 

also includes temperature correction for stress wave propagation time and radio frequency measurement of specific 

gravity, moisture content, and veneer sheet width and thickness and computes the MOE of each sheet of veneer. As 

many veneer manufacturers evaluate hot veneer directly after the dryer outfeed, the temperature effect on stress wave 

velocity is important. The temperature correction factors are 

based on recent research completed at the Forest Products 

Laboratory and Metriguard (Green et al. 1999; Logan 2000). 

The ability to determine specific gravity allows for the determination and sorting by MOE instead of only by transit 

time and should allow for improved utilization of the resource (Logan 2000). Current equipment models provide 

ultrasonic propagation measurements along with specific 

gravity measurements and MOE determination and allow 

sorting veneer based on MOE. MOE sorting and corresponding LVL layup allow for very precise control of the 

MOE in the final LVL product (Moore 2002).

Ultrasonic Grading of Green Veneer

The initial application of ultrasonic methods to sort veneer 

for LVL manufacturer was restricted to dry veneer. By the 

time the veneer is sorted into stress grades, significant 

drying costs have been incurred. If a similar sorting procedure for green veneer could be developed, it would be 

Figure 10.4—Regression of static and ultrasound MOE of 

southern yellow pine veneer (Koch and Woodson 1968).

Ultrasonic Veneer Grading
General Technical Report FPL–GTR–238

112

possible to dry material with similar strength and stiffness 

together in customized drying schedules. These custom 

drying cycles, based on veneer strength and stiffness, could 

lead to increased efficiency, energy savings, and lower costs 

by reducing or eliminating veneer redrying.

Laboratory investigations of the use of ultrasonic methods 

for assessing the quality of green veneer have yielded promising results. Brashaw et al. (1996) evaluated the potential 

of using these methods to sort green southern yellow pine 

and Douglas-fir veneer into stress grades. Stress wave transit 

times were measured on a sample of veneer using both a 

digital oscilloscope and a commercial grading device. The 

stress wave transmission times were measured in both green 

(wet) and dry conditions. They found that wave velocities in 

green and dry veneers were strongly correlated. Figure 10.5 

compares longitudinal wave velocities of southern yellow 

pine veneer at several moisture content ranges to the velocity of dry veneer measured with an oscilloscope. Figure 10.6 

shows longitudinal wave velocities of southern yellow pine 

veneer at several moisture content ranges compared with the 

velocity of dry veneer as measured with a commercial stress 

wave timer. This research effort indicated that it is possible 

to sort green veneer prior to drying. 

Radio frequency measurements for density and moisture 

content of the green veneer can be made with the Metriguard veneer grading equipment. The lack of physical sorting capability in green veneer lines is currently limiting implementation of these processes in manufacturing facilities. 

Literature Cited 

APA. 2000. Regional production and market outlook for 

structural panels and engineered wood products, 2000–

2005. APA Economics Rep. E66. Tacoma, WA: American 

Plywood Association.

Brashaw, B.K.; Ross, R.J.; Pellerin, R.F. 1996. Stress wave 

nondestructive evaluation of green veneer. In: Proceedings, 10th international symposium on nondestructive testing of wood; 1996 August 26–28; Lausanne, Switzerland: 

135–146.

Galligan, W.L.; Courteau, R.W. 1965. Measurement of the 

elasticity of lumber with longitudinal stress waves and the 

piezoelectric effect of wood. In: Proceedings, 2nd symposium on nondestructive testing of wood; 1965 April; 

Spokane, WA. Pullman, WA: Washington State University: 

223–244.

Green, D.W.; Evans, J.W.; Logan, J.D.; Nelson, W.J. 1999. 

Adjusting modulus of elasticity of lumber for changes in 

temperature. Forest Products Journal. 49(10): 82–94.

Guss, L. 1994. Engineered wood products: a bright future 

or a myth. In: Proceedings, 28th international particleboard/

composite materials symposium. 1994 April 12–14; Pullman, WA: Washington State University: 71–88.

Hunt, M.O.; Triche, M.H.; McCabe, G.P.; Hoover, W.L. 

1989. Tensile properties of yellow poplar veneer strands. 

Forest Products Journal. 39(9).

Jung, J. 1979. Stress wave grading techniques on veneer 

sheets. General Technical Report FPL–GTR–27. Madison, 

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

Products Laboratory. 10 p.

Jung, J. 1982. Properties of parallel-laminated veneer from 

stress-wave-tested veneers. Forest Products Journal. 32(7): 

30–35.

Koch, P. 1967. Location of laminae by elastic modulus may 

permit manufacture of very strong beams from rotary-cut 

Southern pine veneers. Res. Pap. SO–30. New Orleans, LA: 

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

Forest Experiment Station.

Koch, P.; Woodson, G.E. 1968. Laminating butt-jointed, 

log-run southern yellow pine veneers into long beams of 

Figure 10.5—Stress wave velocities of southern yellow 

pine veneer at several moisture content (MC) ranges 

compared with stress wave velocity of dry veneer measured with a digital oscilloscope (1 in/ms = 25.4 m/s) 

(Brashaw et al. 1996).

Figure 10.6—Stress wave velocities of southern yellow 

pine veneer at several moisture content (MC) ranges 

compared with stress wave velocity of dry veneer measured with a stress wave timer (1 in/ms = 25.4 m/s) 

(Brashaw et al. 1996).

Chapter 10
Nondestructive Evaluation of Wood: Second Edition

113

uniform high strength. Forest Products Journal. 18(10): 

45–51.

Kunesh, R.H. 1978. Using ultrasonic energy to grade veneer. In: Proceedings, 4th symposium on nondestructive 

testing of wood; 1978 August 28–30; Vancouver, WA. Pullman, WA: Washington State University: 275–278.

Logan, J. D. 1987. Continuous Ultrasonic Veneer Testing: 

Sorting Veneer for Structural Applications. Sawmill Clinic, 

Portland, OR. 

Logan, J.D. 2000. Machine sorting of wood veneer for 

structural LVL applications. In: Proceedings, 34th 

international particleboard/composite materials symposium; 

2000 April 4–6; Pullman, WA: Washington State University: 

67–77.

Moore, H. W.; H. Bier. 2002. Predicting laminated veneer 

lumber properties from the sonic propagation time and 

density of dried veneer in the production environment. In: 

Proceedings of the 13th international symposium on nondestructive testing of wood, University of California, Berkeley, 

CA.: 89–96.

Pellerin, R.F.; Galligan, W.L. 1973. Nondestructive method 

of grading wood materials. Canadian Patent 918286. 

Pieters, A.R. 1979. Ultrasonic energy: a new method for 

veneer grading. In: Proceedings, ASCE convention; 1979 

April 2–6. Preprint #3534. Reston, VA: American Society of 

Civil Engineers.

Uskoski, D.A., Bechtel, F.K.; Gorman, T.M. 1993. Ultrasonic stress graded veneer. In: Proceedings, 1st international 

panel and engineered wood technology exposition; 1993 

October 19–21; Atlanta, GA. San Francisco, CA: Miller 

Freeman.

Acknowledgment

Special thanks to Jim Allen, Vice President (retired), Metriguard, Inc., for providing an in-depth technical review and 

updated production statistics.

Ultrasonic Veneer Grading

Nondestructive Evaluation of Wood: Second Edition

115

Chapter 11

Machine Grading of Lumber

William Galligan, Private Consultant

Keizer, Oregon

John Kerns, Manager

Wood Science and Engineering, Weyerhaeuser Company, Tacoma, Washington

Brian K. Brashaw, Program Manager

Forest Products Marketing Unit, USDA Forest Service, Madison, Wisconsin

Sorting lumber into classes suitable for an end-use is termed 

“grading.” Grading is a nondestructive process, traditionally 

carried out by a visual evaluation of lumber characteristics 

known to affect strength and stiffness. Mechanical devices 

to enhance the grading process were introduced commercially to North America in 1963. These initial devices evaluated the stiffness of lumber on a piece-by-piece basis as part 

of a combined visual and mechanical process that evolved 

as machine stress rated (MSR) lumber. Starting with limited 

commercial ventures in 1963, the production of machine 

graded lumber exceeded 1 billion board feet by 1996 (Galligan and McDonald 2000).

As new mechanical devices, grades, and agency requirements developed, nomenclature for grades was created that 

was more specific to end uses and to agency requirements. 

Consequently, MSR and machine evaluated lumber (MEL) 

are the two types of machine graded lumber produced in 

North America under the auspices of the American Lumber Standard Committee (ALSC) (Galligan and McDonald 

2000). The ALSC maintains the American Softwood Lumber Standard (Voluntary Product Standard PS 20, published 

by the National Institute for Standards and Technology) 

(NIST 2010) and in accordance with PS 20 administers an 

accreditation program for the grademarking of lumber produced under that system. (The American Lumber Standard 

(ALS) is an integral part of the lumber industry and provides 

the basis for acceptance of lumber and design values for 

lumber by the building codes throughout the United States. 

The ALSC is the guiding constituent committee; the ALS 

Board of Review is a certification and accreditation board.)

With regard to machine graded lumber, the Machine Graded 

Lumber Policy of the ALSC sets forth the procedures for 

grademarking of machine graded lumber conforming to the 

American Softwood Lumber Standard PS 20. The policy 

also includes requirements specific to the machine graded 

lumber process and to the approval of the machines (ALSC 

2014). (The current lumber policy and list of machines is 

available at http://www.alsc.org/untreated_machinegraded_

mod.htm.)

This chapter covers nondestructive evaluation (NDE) procedures that use mechanical devices as part of the grading process to assign allowable design properties to lumber. The 

mechanical device may measure physical or mechanical 

properties of a piece; the generic term “machine grading” 

will be used where specific technology is not cited. Grading 

systems in North America that incorporate mechanical devices most often include some visual evaluation. In-depth 

descriptions of the nondestructive tests used, specifically 

static bending, transverse vibration (vertical oscillation of a 

simply supported beam) and longitudinal stress wave (sometimes referred to as acoustic) are presented in Chapter 2, 

“Static Bending, Transverse Vibration, and Longitudinal 

Stress Wave Nondestructive Evaluation Methods.” 

This chapter traces the development of machine grading 

in North America with background research references. A 

discussion of some commercial processes is provided to 

indicate critical operating variables and interactions. This 

includes qualification and subsequent quality control of 

grades. Assessment and optimization of the grading system 

is included. The chapter closes with updates on recent developments including the development and use of acoustic 

based machine grading equipment. 

Nondestructive Evaluation for 

Lumber Grading

Although mechanical grading made its commercial debut 

in North America in 1963, the nondestructive testing basis 

for the mechanical system had been explored at the research 

level for several years before. An example is the demonstration of the fundamental relationships published in 1962 and 

shown in Figure 11.1 (Senft et al. 1962). This figure suggests the usefulness of using modulus of elasticity (E) to 

predict modulus of rupture (MR, now commonly referred 

to as MOR). This, in fact, was the grading basis at that time 

under exploration for mechanical grading at Potlatch Corporation, Lewiston, Idaho, and the Western Pine Association, Portland, Oregon (McKean and Hoyle 1964). At that 

time, the relationship between density and strength shown 
General Technical Report FPL–GTR–238

116

in Figure 11.1 was also being investigated using gamma ray 

and microwave through transmission; however, commercial 

development did not succeed until the early 1990s (Ziegler 

1997).

Early development of machine lumber grading was marked 

by worldwide involvement and a commitment of Federal, 

State, and commercial funding. A 1968 compilation of research and development efforts estimated an accumulated 

36 years of lumber-related research at seven Federal laboratories worldwide between 1961 and 1968, 28 years at U.S. 

universities, 15 years between Potlatch Corporation and 

the Western Pine Association (late 1950s and early 1960s), 

and 13 years among five machinery companies (Galligan 

and Moyer 1968). In recognition of this developing interest, the first symposium on nondestructive testing was held 

at the USDA Forest Products Laboratory (FPL) in 1963 

(FPL 1964). It was followed in 1964 by a symposium that 

focused on “needs” for NDE, rather than on the technology 

(FPL 1965). In 1965, the first in a series of international 

symposia was sponsored by Washington State University 

(WSU), subsequently a joint effort by WSU and FPL (Galligan 1965). Although these symposia were not limited to 

lumber grading, development of machine grading technology was a principal component for the early symposia and 

has been reported on in subsequent symposia. A publication, 

Fifty Years of Nondestructive Testing and Evaluation of 

Wood Research—International Nondestructive Testing and 

Evaluation of Wood Symposium Series, was produced by the 

FPL (Ross and Wang 2012). The publication includes a brief 

history of the symposium series, summaries of each symposium, and searchable electronic copies of the proceedings 

from each symposium.

This chapter is limited to practices in the United States and 

Canada, with an emphasis on lumber grading machines approved by the ALSC Board of Review (ASLC 2014). Machine grading developments in other parts of the world have 

differing characteristics because of industry custom, governmental regulation, and commercial requirements. Early 

overviews of developments in other countries are found in 

Hoyle (1970). Galligan et al. (1986) published a list of early, 

important international research on machine lumber grading. Contemporary papers are found in the proceedings of 

the International Nondestructive Testing and Evaluation of 

Wood Symposium.

The advent of commercial NDE devices for lumber grading 

hinged on both technological and commercial feasibility. 

From the very beginning of NDE for lumber grading, these 

factors have been the subject of study and debate (Galligan 

1965). The technology of machine lumber grading will be 

discussed first in this chapter, based on the two commercial 

machine-based predictors of strength, modulus of elasticity 

(MOE), and density.

Assignment of Design Properties

Machine grading methods provide measurements that have 

a predictive relationship to assigned properties. These grading systems sort lumber into categories to which allowable 

design properties can be assigned using standardized methodology. Regardless of the predictive mechanism (for example, density or MOE) and the extent of visual oversight, 

the output of the process is design properties that are not 

directly measured by the grading system. Grading systems, 

whether visual, mechanical, acoustic, or a combination, are 

based on predictive procedures.

Machine-Based MOE as the 

Predictive System

For the first 30 years of commercial practice, machine grading systems in the United States and Canada were based 

solely on stiffness measurement. Although the machines 

actually measured stiffness in some form, predictive relationships between properties were based on MOE, rather 

than stiffness, because adjusting for cross section allowed 

examination across sizes and directly addressed a fundamental wood property. 

Predicting Modulus of Rupture

Early research emphasized MOR over MOE. Because the 

machines measured flatwise stiffness, flatwise MOE and 

MOR received attention. Figure 11.2 illustrates this early 

Figure 11.1—Diagram showing simple correlation between lumber variables. Based on studies at Purdue 

University. D, density; E, modulus of elasticity; M, moisture content; MR, modulus of rupture; S, slope of grain 

(Senft et al. 1962).

Chapter 11
Nondestructive Evaluation of Wood: Second Edition

117

work (McKean and Hoyle 1964). Division of the flatwise 

MOE spectrum is based on potential flatwise MOR design 

values. Figure 11.3 shows the relationship between edgewise and flatwise MOE (McKean and Hoyle 1964). The 

variability between flatwise and edgewise MOE in Figure 

11.3 is caused by the different effect of orientation of knots 

and other characteristics relative to that in the static test, the 

influence of shear in the different test modes, and the fact 

that different lengths of pieces were probably included within a piece of lumber tested flatwise (as a plank) or edgewise 

(as a joist) to obtain a proper MOE test. Adjustments were 

made for shear based on the “accepted” value for modulus 

of rigidity and a span/depth ratio of 14 in both orientations.

Interest in flatwise properties soon waned in favor of edgewise MOE and MOR because the latter were used more 

often in wood design. Consequently, even though the 

machine measurements were made in a flatwise orientation, research began to emphasize edgewise properties and 

industrial quality monitoring used the edgewise orientation. 

Johnson (1965) illustrated the different results and presentations possible because of the variety of test orientations 

(Fig. 11.4). In particular, Figure 11.4 shows the effect of 

selection of edgewise piece orientation (“worst” compared 

to “best” edge in tension) on the relationship between MOR 

and MOE. Flatwise MOE is the independent variable as a 

machine-related parameter, not the edgewise MOE “design 

value.”

A summary of research on the relationship of MOEedge to 

MORedge and other strength property relationships over a 

20-year period was prepared by Hoyle in 1986 (Galligan et 

al. 1986). This compilation includes research in other parts 

of the world and distinguishes between reports with and 

without accompanying data.

Mechanical grading has focused primarily on the statistical relationship between MOEedge and MORedge; however, 

this relationship is not statistically strong, often yielding R2

values on the order of 0.6. The lack of statistical strength 

affects the efficiency of the grading system; nevertheless, 

it provides a practical level for grading. The relationship 

provides thresholds of stiffness below which unacceptable 

strength is unlikely to occur, as suggested for flat properties 

by McKean and Hoyle (1964) (Fig. 11.2). Figure 11.5 shows 

a superimposed 5% lower confidence level on MORedge for a 

small data set. From this plot, threshold MOE estimates can 

be established for machine operation. Figure 11.5 is from a 

publication by Galligan and McDonald (2000), which provides guidance for a producer wishing to study the feasibility of machine grading. This feasibility is measured as yield 

of machine grades against the yield of the traditional visual 

grading system.

Grading machines do not make the same measurements 

as those of the laboratory machines on which predictive 

research is carried out (see sections on MOE prediction). 

Figure 11.2—Relationship between flatwise modulus of 

rupture (MOR) and flatwise modulus of elasticity (MOE) 

for Douglas-fir lumber; both properties measured as plank 

(McKean and Hoyle 1964).

Figure 11.3—Relationship between edgewise and flatwise 

MOE (McKean and Hoyle 1964).

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General Technical Report FPL–GTR–238

118

Some machine systems are based on measuring only the 

lowest stiffness in a piece, others seek the lowest stiffness 

but may also provide an average output, and others provide 

an average only. On the assumption that the lowest stiffness region of a piece is directly related to the strength of 

the piece, strength predictive models may show greater efficiency when the prediction is based on the lowest stiffness. 

An example is provided by Corder (1965), who selected 

the minimum of nine static flatwise MOE measurements 

from each of 145 pieces and regressed this value against 

MORedge. This resulted in an improvement in the correlation of approximately 0.07 in R2

 (0.62 to 0.69) over using 

the average flat MOE in limited laboratory tests. Since this 

research, the capability of grading machines for identifying 

the lowest MOE value has improved and can be decidedly 

more sensitive than the Johnson laboratory measurements. 

Nevertheless, capabilities of industrial systems vary by 

machine and company practice. These installed systems are 

qualified through empirical testing of output, not by testing 

the predictive variables for their efficiency. Consequently, 

only careful yield studies of actual mill grade output can 

determine the relative actual efficiency of the stiffness measurement of a particular device in predicting MORedge.

The discussion of MOE versus MORedge is not complete 

without mention of visual restrictions. In practice in North 

America, most grading systems use visual grading restrictions or “overrides.” The term override signifies the visual 

scrutiny of a piece after the grade category based on stiffness has been identified. If the visual characteristic is too 

large for the sort qualified by the stiffness, the piece is 

placed in a lower category— the grade sort suggested by 

stiffness alone is overridden. Visual overrides have two 

purposes: one is intended for improving appearance, and the 

second is designed to improve the efficiency of selection 

of strength properties. Both types of overrides can have an 

effect on sorting and consequently on assigned properties; 

however, strength-related criteria, such as restrictions on 

edge knot size, have the most effect on design values and 

must receive attention when considering the estimation 

of MORedge. In some operations, lumber is categorized by 

visual restriction prior to the stiffness test. This order may 

be chosen for operating efficiency but does not change the 

predictive function.

Although the term override continues to be used for historical reasons, this term tends to discount the contribution 

of the visual stress grading as part of the grading model. 

Modern machine grading usage in North America incorporates visual and mechanical criteria as part of qualification, 

grading, and quality control—these components act together 

to control performance and efficiency. Consequently, a 

preferred term for the visual stress-grading component is 

“criterion.”

In response to a survey by the Federal Housing Administration of commercial machine-graded lumber in the first 

years of its existence, the industry adopted a sliding scale 

of restrictions on edge characteristics based on fraction of 

the cross section (Table 11.1) to increase efficiency of the 

grading system. This addition of another variable to the sorting criteria created a three-dimensional model, with some 

machine-estimated MOEflat (from stiffness flat) and edge 

restrictions as two independent variables. The flexibility 

Figure 11.4—Influence of test orientation on relationship 

between MOR and MOE. MOR was measured flatwise. 

Edgewise “best” and “worst” edges were regressed on 

flatwise MOE (Johnson 1965).

Figure 11.5—Estimation of MOE level required to maintain MORedge of a grade. Top line shows mean trend; 

bottom line is estimate of 5th percentile of MORedge

distribution. VQL-1 and VQL-2 represent visual restrictions of 1/6 and 1/4 applied to edges of pieces (Galligan 

and McDonald 2000).

Chapter 11
Nondestructive Evaluation of Wood: Second Edition

119

and effectiveness of this three-dimensional model was later 

shown by FPL research (Ethington 1970). (Most written 

discussion focuses on the design property, MOEedge, which 

is really not the predictor, itself predicted from a machinemeasured flatwise stiffness.)

Commonly called “edge knot restrictions,” the fraction of 

the cross section occupied by any strength-reducing characteristic, such as knots, distorted grain, or decay, is limited 

if the characteristic occurs at the edge of the piece. The allowable design value for bending strength is designated by 

the letter f and is expressed in pounds per square inch, as 

in 2100f. These restrictions began to appear in early grade 

rules (for example, WCLIB 1964, WWPA 1966). In recent 

years, some agencies address visual limitations in different 

ways; consequently, reference to the current rules of the appropriate agency is essential.

The variables of MOE and edge characteristics are not truly 

independent, because a characteristic such as a knot on the 

edge of lumber has a negative influence on stiffness. Nevertheless, the scheme of adding an edge restriction to an MOE 

limitation has formed the basis of all machine grading from 

approximately 1964 to the present. Consequently, the historical statement relating MOR to MOE in North American 

practice applies only if the third variable, edge restriction, is 

added. Furthermore, the actual machine stiffness measurement, varying by machine type, is not addressed. Empirical 

qualification systems and feedback quality monitoring insist 

on consistent, accurate estimation of design properties; however, they do not address efficiency.

Some rule-writing agencies have applied visual restrictions 

in different ways; the traditional limitations of Table 11.1 

may not apply, and interpretation is often essential. It is important to refer to the most current version of agency grading rules. Of particular interest may be the enforcement of 

visual rules applying to those portions of the piece not measured by the grading machine. Some agencies have a long 

tradition of visual grading for knots and other strength-reducing characteristics, which is linked to the concept that 

the quality of the wood in the non-tested portions should 

approximate that in the areas that were subject to measurement. These rules stemmed from early resistance to machine 

grades by engineering and regulatory groups that were unaccustomed to the machine grade system. The addition of an 

“end-of-the-piece” rule that related quality to the test area 

enhanced the credibility of the system. Ancillary to these 

discussions, the industry found through observation in the 

early operating period that mill trimming practices could 

heavily bias the quality of the end of the piece. A particular 

concern was slope-of-grain in pieces cut from butt logs, but 

knots were also a serious concern. A visual rule to cover 

these issues is now commonly, but not uniformly, accepted. 

Specific visual criteria that may interact with machine measurement or grade yield may be required by customers in 

addition to the visual criteria of the grading rules. These 

customer-required criteria are in anticipation of further machining or fabrication, location of fasteners, or visual 

appearance. 

In the balance of this chapter, bending strength will be referred to in terms of the allowable stress in bending (Fb), 

which is the 5th percentile MOR divided by 2.1; in machine 

grade labeling, this may be designated by the letter f after 

the allowable design strength value in bending.

Predicting Edgewise MOE

Edgewise MOE (MOEedge) has wide application in design; 

consequently, it has received the greatest research emphasis 

and is included in the grade description for machine grades. 

MOEedge can be estimated from flatwise measurements, as 

suggested in Figure 11.3; however, even though the machine is segregating in this flatwise mode, the relationship 

between flatwise and edgewise MOE is not often studied or 

emphasized in the industrial environment. The predictive 

relationship is strong; R2 values of about 0.9 can be commonly experienced in laboratory tests. If the machine measurement output is an “average” MOE, this would emulate 

a laboratory flatwise average suggested by Corder (1965) 

as estimating MOEedge with an R2 of about 0.92; Corder’s 

laboratory measurements based on minimum stiffness introduced a small amount of additional uncertainty, lowering the 

R2 to about 0.88.

Many quality monitoring systems are based on direct measurement of MOEedge, thus bypassing the information on 

the relationship between a standardized flatwise test and 

the machine-related flatwise measurement. This introduces 

“noise” from the edge versus flat relationship into the quality system if monitoring the efficiency of the actual machine 

flatwise function is an objective, such as part of grading machine quality control. If the mill quality monitoring system 

focuses only on adherence to the edgewise MOE specification and does not address machine function, this may be a 

moot point.

Predicting Flatwise MOE

The type of stiffness measurement made by a machine, the 

sampling on the piece, and the computation of the resulting 

data determine the results that will be obtained. Regardless of differences, however, flatwise stiffness remains the 

Table 11.1—Traditional edge 

knot restrictions

Machine grade level

Edge knot 

restrictions

2100f and higher 1/6

1500f to 2050f 1/4

1200f to 1450f 1/3

900f to 1150f 1/2

Machine Grading of Lumber
General Technical Report FPL–GTR–238

120

fundamental measurement of many current North American 

machines. Consequently, if a producer wishes to qualify 

lumber for flatwise MOE (MOEflat), the database from the 

actual machine measurement will probably relate more 

closely to this property than to any other. A discussion of 

MOEflat, however, must address both measurement and assignment issues.

Even though MOEflat is closely related to the flatwise stiffness measured in the grading machine, it was the late 1980s 

before a flatwise MOE was assigned as a design property for 

machine-graded lumber to be used in laminating (WCLIB 

1989) and the 1990s when this flatwise MOE was assigned 

to MSR for use in 4 by 2 trusses, I-joists, and other applications (WCLIB 1997). In these applications, the flatwise 

MOE is defined by an American Society for Testing and 

Materials (ASTM) standard D 4761-13 test with a span/

depth ratio of 100 using center-point loading (ASTM 2014).

As noted, commercial machines measure stiffness in different ways, such as different span/depth ratios, deflections, 

data collection, and computation. Consequently, the outputs 

produced by different machines—the signals produced after 

measurement and computation—will differ in relationship 

to a standardized ASTM static test. Machines that produce 

an “average” MOE by measuring stiffness of many samples 

along the length of a piece closely approximate the ASTM 

D 4761-13 MOEflat. One example is 

ASTM D 4761 MOEflat = 1.22 (machine “average” 

MOE) − 0.22 (11.1)

R2 = 0.99

based on 2 by 6 lumber ranging in MOE from 1.0 to 

2.8 × 106 from two independent samples, one each from 

two different companies, both operating a machine that 

sampled frequently along the length and averaged the 

reading to produce an “average” MOE.

If the grading machine does not sample frequently along the 

length, takes only a small sample, or searches only for the 

lowest stiffness area of the piece, the relationship shown in 

Equation (11.1) will not apply. In many cases, a useful relationship may still result, but it will not be as robust as that 

shown in the equation. It is important to note that industrial 

machine-based measurements do not provide a database for 

comparability or standardization—all machine measurements depend upon calibration by standardized tests if the 

measurements are to be used on a comparative basis.

Because MOEedge is assigned as an integral part of qualifying a machine grade, is there a relationship to a flatwise 

value to facilitate assigning an MOEflat for design purposes? 

In early work, this relationship was found to be a function 

of grade and other factors. However, one principal element 

is the nature of the static test used to define the property because both MOE values are “apparent” values, that is, they 

are not corrected for shear and are influenced by the area of 

the piece being tested. The flatwise MOE is based on a test 

that is essentially shear free (span/depth of 100, center-point 

load); the edgewise test has a span/depth ratio of 21 with 

third-point loading. The result is a contribution (lowering) 

of the edgewise value as a result of shear.

However, edge characteristics such as knots may have a 

greater lowering effect on the apparent MOEedge than do flatface characteristics on the MOEflat because of the predominance of critical edge characteristics in a test sample (resulting from mill sawing patterns). Consequently, studies conducted to relate MOEflat and MOEedge are influenced by mill 

practice as well as differences in the static tests employed.

In 1997, the West Coast Lumber Inspection Bureau 

(WCLIB) completed a survey of existing data on MOEedge

and MOEflat and issued a report to the ALSC. The report 

concluded that trends in the data could be summarized 

to permit the design relationship MOEflat = MOEedge + a 

“standardized increase,” where MOEflat is measured on a 

span/depth ratio of 100, center-point load, and MOEedge is 

measured on a span/depth ratio of 21, third-point load. The 

approved increase was 100,000 lb/in2 for MOEedge values 

above 1.3 × 106 lb/in2; a 50,000-lb/in2 increase was selected 

for MOEedge values of 1.3 × 106 lb/in2 or less. This relationship was accepted and printed in grading rules in April 1997 

(WCLIB 1997).

Establishing Fiber Stress in Tension Parallel to Grain

One of the earliest demonstrations of the basic relationship 

of tensile strength (Ft) to MOE was by Nemeth (1965). Before these tests of full-size lumber, the design value in tension was assumed equal to the assigned Fb. After these and 

a few other early observations, a ratio of 0.8 was assigned 

for Ft/Fb. Further changes were made in this relationship 

in 1969 as a result of accumulated research information. 

Table 11.2 chronicles these changes in the assignment process (Galligan et al. 1979).

In the early 1980s, the study of the relationship between 

tensile strength and MOE was rekindled with a series of 

proof-loading tests of more than 3,000 specimens of several 

species, sizes, and grades by the WCLIB (Galligan et al. 

1993). This study illustrated that the ratio of Ft/Fb can be 

overstated and suggested verification of the assigned tensile 

value rather than use of the 1969 ratios as default values. As 

a result of this and subsequent work (Galligan and DeVisser 

1998), the current practice is to use the 1969 ratios as a basic starting point. If qualification tests in tension justify use 

of a ratio different from that shown in Table 11.2, it must be 

supported by subsequent quality control. If the 1969 ratios 

are to be applied as a default, verification of assigned tensile 

values is required under ASTM D 6570-04(2010) (ASTM 

2014e); if verification is not carried out, the default alternative is to use a ratio of tension/bending of 0.49, based on 

ASTM D 1990-14 (ASTM 2014a).

Chapter 11
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121

The grades shown in Table 11.2 are typical of those that 

dominated the selection of machine grades during the first 

approximately 15 years of production. In present practice, 

the values in the right column are subject to verification; 

some market grades may deviate from this schedule on the 

basis of test and quality control.

Galligan et al. (1993) illustrated the effect of piece width 

on Fb. The width effect was described by an exponent of 

0.29 on width, the same factor used for visually graded lumber under ASTM D 1990-14. Because the machine grades 

examined were based on stiffness segregation, the tensile 

strength relationship for machine grades includes the average MOE of the test sample:

Tensile strength TL = 1869(MOEedge/W0.29) – 917 (11.2)

where TL is the 5% nonparametric tolerance limit (75% 

confidence) in pounds per square inch adjusted to a design 

value by a divisor of 2.1, W is the actual width of the piece 

(in.), and MOEedge is the mean edgewise MOE of sample 

(×106 lb/in2).

This relationship supports the current practice of requiring 

qualification of each width of a machine grade, regardless of 

the similarity of the sorting criteria.

Establishing Fiber Stress in Compression Parallel to 

Grain

Fiber stress in compression parallel to grain (Fc∥) was not 

studied extensively in the early work on MOE relationships. 

By 1964, however, the grading rules used a ratio of 0.8 for 

the relationship of Fc∥Fb across all grades. In a 1968 review, 

Hoyle reported the positive correlations of Fc∥ on MOE in 

limited tests. The slope of the regressions was much lower 

than that reported for bending and for tensile strength on 

MOE (Hoyle 1968). The constant ratio of 0.8 for all grades 

was used until 1991, when it was replaced by the current 

relationship to correspond with similar changes for the visual grades:

Fc∥ = (0.71Fb + 2061)/1.9 (11.3)

(for example, WCLIB 1991) where Fc∥ is allowable stress 

in compression parallel to grain (lb/in2) and Fb is allowable stress in bending (lb/in2

). This formula follows the 

same format used for visual grades in ASTM D 1990-14 

(ASTM 2014a). With use of this formula, Fc∥ is higher than 

previously for the lower grades and lower for grades above 

2400f.

Assigning Specific Gravity, Fiber Stress in Shear, 

and Compression Perpendicular to Grain

From the earliest research until 1992, specific gravity, allowable stress in shear (Fv), and compression perpendicular to 

grain (Fc⊥) were assigned to machine grades by species with 

no differentiation by grade. These three properties are based 

on clearwood and are derived from ASTM D 2555-06(2011) 

(ASTM 2014b). The familiarity of these clearwood-based 

values and the lack of an accumulated database for machine 

grades made it expedient to use these properties in the 

same manner as in the visual grades. This practice did not 

make use of grade differentiation by MOE, and the higher 

machine grades suffered in end use by having specific gravity, Fv, and Fc⊥ properties that did not reflect the high level 

of Fb, Ft, and Fc∥ capability; that is, they were limited in 

certain design aspects by the value of the commercial species groups for these three properties. This practice has now 

changed. Each of these properties is discussed individually 

in the following text.

Specific Gravity—Until the late 1990s, specific gravity was 

not listed as an allowable property in the grading rules; 

however, it is used in critical applications, such as calculation of plating capacity (setting plate sizes for metal plate 

connected wood trusses). Specific gravity has also come to 

form the basis for other metal connectors such as nails and 

bolts (NDS 1991). Consequently, when the higher machine 

grades claimed increased capacity in allowable strength 

properties, use of the species specific gravity for all machine 

grades was a detriment to efficient use. This was particularly 

true for the higher grades of “lower” specific gravity species groups such as Hem–Fir. For example, the size of metal 

plates for trusses was selected on the species specific gravity 

of Hem–Fir, whereas the high machine grades of that species group could claim much higher strength properties than 

the specific gravity represented.

The variation of specific gravity by machine grade was reported in the late 1960s by Hoyle (1968). Even though the 

“relatively low” metal plate holding capacity assigned to the 

higher grades of “low” specific gravity species like true fir 

plagued the marketing of these grades for trusses, no action 

was taken to remedy this lack of alignment between strength 

properties and specific gravity until the 1980s. At that time, 

Frank Lumber Company (Mill City, Oregon) began the 

study of the effect of MOE segregation on the differentiation 

of specific gravity by grade, essentially building on the basic 

relationship between MOE and specific gravity shown by 

Senft et al. (1962) in Figure 11.1 and Hoyle (1968). Review 

of data on lumber specific gravity, collected over a period 

of several years, on several grades over four lumber widths 

verified that a useful trend of increasing specific gravity 

with MOE could be identified and maintained as a function 

Table 11.2—Historical assignment of Ft/Fb ratio

Machine grade 

Permissible edge 

characteristic 

Ft/Fb ratio 

Early 

1960s 

Mid1960s 1969 to 

present 

2400f–2.0E1 1/6 1 0.8 0.8 

2100f–1.8E1 1/6 1 0.8 0.75 

1500f–1.4E1 1/4 1 0.8 0.6 

1200f–1.2E1 1/3 1 0.8 0.5 

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General Technical Report FPL–GTR–238

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of machine segregation. This relationship was clearly useful 

for both Douglas Fir and Hem–Fir and was independent of 

width.

By 1996, sufficient data had been collected by the Frank 

Lumber Company and the WCLIB to standardize the grade/

specific gravity relationship for general machine grading 

rules for Douglas Fir and Hem–Fir. For the higher MOE 

grades, specific gravity values were specified, contingent 

upon the values being qualified by test and monitored 

as part of the WCLIB quality system (WCLIB 1997). 

Table 11.3 contains the values published by the WCLIB 

in 1997. Provisions of this type are agency dependent; for 

other species or geographical areas, the appropriate agency 

should be contacted.

Compression Perpendicular to Grain—Coincident with the 

study of the relationship of specific gravity to MOE, a study 

began of the other two “minor” properties, shear and compression perpendicular to grain. Initial emphasis was placed 

on compression perpendicular to grain (Fc⊥) because that 

property could limit bearing for higher strength grades when 

the species Fc⊥ value was used, particularly with Hem–Fir. 

Compression perpendicular to grain had been the subject of 

a series of publications in the 1970s when the design basis 

for the property changed from the proportional limit to the 

stress at 0.04-in. deformation. One result of these studies 

was the development of the relationship between deformation and specific gravity (Bendtsen and Galligan 1979). 

Consequently, in 1992, this information was used to develop 

a generalized relationship with specific gravity that could 

be applied to machine grades that were qualified and quality 

controlled for specific gravity:

Fc⊥ = (2498.9SPG) – 537.7 (11.4)

(for example, WCLIB 1992) where SPG is specific gravity 

based on ovendry weight and volume at 12% moisture content and Fc⊥ is stress in pounds per square inch at 0.04-in. 

deformation. When stated on an ovendry volume and weight 

basis, the formula is

Fc⊥ = (2252.4SPG) – 480 (11.5)

(for example, WWPA 1994). This relationship is used for 

many species groups (SPIB 1996; WCLIB 1997); however, 

reference to individual agency procedures should be made.

With this relationship, Fc⊥ can shift to higher values in step 

with other grade-related increases in other design values. 

The use of these increases is the prerogative of the manufacturer; if the specific gravity is not qualified and monitored or 

marketing does not claim the values for design, the default 

Fc⊥ value is the same as that for the visual grade, species, or 

species group.

Shear—The allowable design value for shear, Fv, is basically a clear wood property that has its roots in ASTM D 

2555-06(2011) (ASTM 2014b). The data in this standard 

illustrates that this property is directly proportional to specific gravity. As the use of specific gravity for differentiating 

properties of MOE-based grades developed, increases in Fv

by grade were also recognized to prevent limitations in use 

by the default species value. In 1992, this use was formalized in the WCLIB grading rules for western species:

Fv = 17.1 + (150.95SPG) (11.6)

(for example, WCLIB 1992), where SPG is specific gravity based on ovendry weight and volume at 12% moisture 

content and Fv is allowable shear stress in pounds per square 

inch. When stated on an ovendry volume and weight basis, 

the formula is

Fv = 20.6 + (136.08SPG) (11.7)

(for example, WWPA 1994). In July 1996, changes in the 

method of assigning shear values were made at the ASTM 

level for all structural lumber. These changes resulted in 

increased shear values and, for machine grades, a modified 

relationship for both western and southern species:

Fv = 40 + (266SPG) (11.8)

(for example, WCLIB 2000), where SPG is specific gravity 

based on ovendry weight and volume basis. These grade increases for Fv may be agency dependent with respect to both 

the assigned value and the criteria required by the agency.

Fv values that change by grade are optional for the manufacturer. Specific gravity must be qualified and monitored. If 

the manufacturer chooses not to use these values, the default 

value is that assigned to the species or species group.

Table 11.3—Example of specific gravity 

assigned by MOE level to machine grades 

by WCLIB (1997)a

Species group

Grade modulus 

 of elasticity 

(×106 lb/in2

)

Specific 

gravity 

Douglas Fir <2.0 0.50 

<2.0 0.51 

<2.1g 0.52 

<2.2 0.53 

<2.3 0.54 

<2.4 0.55 

Hem–Fir <1.6 0.43 

<1.6 0.44 

<1.7 0.45 

<1.8 0.46 

<1.9 0.47 

<2.0 0.48 

<2.1 0.49 

<2.2 0.50 

<2.3 0.51 

<2.4 0.52 

Spruce–Pine–Fir South All grades 0.36 

Western Cedar All grades 0.36 

Western Woods All grades 0.36 

a

Note the species effect. Some species groups have no 

standardized assignment by MOE level. Individual grade 

as assignments are possible with qualification by test 

and quality control. 

Chapter 11
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123

Machine-Based Density as the Predictive 

System

As with stiffness-based machines, strength properties for 

density-based machines are determined through predictive relationships. In this case, density is used as the primary predictor for the strength properties. The strength of 

an individual piece of lumber ultimately depends on two 

properties: (1) the inherent clear wood strength given the 

individual species, density, and moisture content and (2) the 

strength-reducing defects contained within the piece, such 

as knots and grain distortions.

Conceptually, the predicted board strength can be represented as

Predicted strength = Clear wood strength × Effect of defects

The large differences in density between clear wood and 

strength-reducing defects (primarily knots) is used as the 

basis for identifying the size and location (effect) of these 

defects (Hansen and Schajer 1988; Schajer 2001).

Predicting Strength

Density profile data are obtained at regular intervals along 

the length and across the width of each individual board. 

The strength prediction procedure identifies and quantifies 

two significant features from these measured profiles: the 

mean clear wood strength and the localized increases in 

density resulting from knots. Knots are easily distinguished 

because their density values are about double that of the surrounding wood (Fig. 11.6).

The impact of a knot on clear wood strength is estimated 

from its size as determined by the size of the peaks in the 

density profile. The relationship between the size of the 

density peaks and board strength is established empirically 

through experimentation. With multiple scans across the 

width of the board, it becomes possible to estimate not only 

the relative size of the knot but also its location. Location 

of the knot relative to the edge of the piece is also important 

because it allows the application of different weighting factors depending on the location of the particular defect.

Because of the resolution of the density profile measurements, this technique allows estimates of the impact for 

each defect corresponding to localized areas separated by 

fractions of an inch. In addition, the measurements come 

from the interior of the wood and are not limited by what 

can be seen or interpreted from the surface. The empirically 

derived prediction algorithms for MOR (bending strength) 

will typically have R2 values of 0.70 to 0.75, with standard 

error estimates of 1,300 to 1,400 lb/in2. As with bending, the 

predicted tensile strength of the board is taken as the lowest value for all sections within the scanned density profile. 

However, unlike the loading for static bending tests, the 

applied loading for tensile testing is uniform throughout the 

piece and failures are more apt to occur at the predicted 

location during testing. The prediction algorithms for 

ultimate tensile strength (UTS) will typically have R2 values 

of 0.75 to 0.80 with standard error estimates of 900 to 

1,100 lb/in2, which are somewhat better than the bending 

predictions. The predictive algorithms for all properties are 

considered proprietary information and have not been publicly disclosed.

Predicting Modulus of Elasticity

As previously discussed, MOEflat is closely related to the 

measurements made by stiffness-based grading machines. 

This is not the case with density-based machines. The MOE 

determination is predicted on the basis of the density measurements. The predictive algorithm uses the average density of the piece and the localized grain deviation around the 

main defect. The MOE is also dependent on the microfibril 

angle within the cell wall. Juvenile wood and compression 

wood have high microfibril angles compared to normal and 

mature wood, which have low microfibril angles. The high 

microfibril angle reduces the MOE of the material, independent of density, and therefore introduces more variability 

into the MOE predictions. The R2 values for the MOE predictive algorithms range from 0.65 to 0.75, with standard 

error estimates of 120,000 to 150,000 lb/in2.

Establishing Compression Parallel to Grain

Although it is theoretically possible to develop predictive 

algorithms for compression parallel to grain (Fc∥), this has 

not yet taken place on a commercial installation. Grading 

of lumber with density-based machines currently uses the 

same formula as that for visual grades in ASTM D 1990-14 

(ASTM 2014a), which is identical to that used for lumber 

graded with stiffness-based machines as previously discussed.

Assigning Specific Gravity, Fiber Stress in Shear, 

and Compression Perpendicular to Grain

Because density-based machines produce the same lumber 

grades as do stiffness-based machines, the assigned values 

Figure 11.6—Density profile scan as measured along 

length of board.

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for shear and compression perpendicular-to-grain are the 

same regardless of which type of machine is used. The assigned values are species and grade dependent. However, 

because density-based machines measure density, they have 

the capability to more easily produce grades with nonstandard specific gravity values. Some mechanically graded 

lumber standards do allow the assignment of alternative 

specific gravity values, provided additional quality control 

procedures are implemented. This typically involves specific 

gravity testing on the same samples that are selected for 

bending or tension quality control testing. Density-based 

equipment currently in operation has density as one of the 

grade selection parameters, which may or may not be utilized in the grade selection process. Use of density depends 

on the objectives of the individual manufacturer.

Development of Stiffness-Based 

Grading Machines

Early Development

Two grading machines were responsible for the early acceptance of mechanical grading in the United States and 

Canada, the Continuous Lumber Tester (CLT) and StressO-Matic (SOM). The Potlatch Corporation of Lewiston, 

Idaho, conducted research and development for the CLT; 

the Western Pine Association of Portland, Oregon, developed the concepts behind the SOM. Commercial models of 

both machines were finalized by machinery firms. Although 

output from both the CLT and SOM was based on stiffness measurement, the operating concepts were different. 

Lumber passing through the CLT was bent flatwise, first in 

one direction and then the other. The lumber followed a prescribed radius; signal output was based on resistance to the 

curvature as measured with two electronic load cells, one 

for each bend orientation. The loading could be visualized 

as fixed ends and a centerpoint load measurement. Many 

samples (load signals) were taken along the length of the 

piece. The computer then processed the signals to produce a 

signal proportional to the “average” stiffness along the piece 

and a value corresponding to the lowest stiffness detected 

along the cross section. Signal output could be calibrated 

to correspond to a static test value, keeping in mind that the 

machine measurement system was not exactly duplicated by 

any standardized test. The stiffness of the ends of the piece 

was not measured because gripping the ends reduced the test 

length. The CLT was developed for dimension lumber of 

approximately 1.5 in. thick up to 12 in. wide; only moderate adjustment for thickness could be accommodated. These 

early machines were adjustable in speed; common operation 

was approximately 700 to 1,000 ft/min.

The SOM also bent lumber in the flatwise orientation; however, it loaded in only one direction with a third-point load 

on a total span of 48 in. This was principally a mechanical 

rather than electrical device. It imposed a load on the piece 

as it traversed the machine. If the piece deflected beyond 

a set limit, the load was reduced. The load reduction proceeded until the deflection limit was not reached. That last 

load level determined the stiffness sort for the piece, essentially estimating the lowest stiffness of the piece. The choice 

of load levels set the number of grade sorts. A speed of 

about 600 ft/min was maximum. This placed the machine in 

smaller mills or off-line with production speed planers. The 

SOM was designed for lumber not exceeding approximately 

2.5 in. thick.

Capabilities of early U.S. and foreign grading machines 

were summarized by Galligan et al. (1977). In addition to 

the CLT and SOM, one machine that has been in relatively 

constant use in North America since the 1960s is the EComputer. The E-Computer is a transverse vibration system 

that measures stiffness from the fundamental frequency of 

the flatwise vibration of a piece of lumber supported at the 

ends. One advantage of this system is the diversity of piece 

thicknesses that can be accommodated. The E-Computer is 

often thought of as only a research or survey tool. However, 

it has been in commercial use for sorting structural stock for 

cooling towers. In addition, for several years, the E-Computer has been the production line grading machine for several producers, grading dimension (nominal 2-in.) lumber 

destined for trusses or laminated beam construction. This 

system has low piece throughput, measured in pieces per 

minute, depending on the material handling requirements of 

the installation. In fact, the use of transverse vibration is the 

only NDE technique guided through a consensus standard, 

ASTM 6874-12, Standard test methods for nondestructive 

evaluation of wood-based flexural members using transverse 

vibration (ASTM 2014f).

Subsequent Developments

Advances in all segments of mechanical and electronic 

technology have greatly benefited mechanical grading. 

Early electronic “1960s quirks” disappeared with the advent 

of solid-state systems. Feedback to the operator and clarity 

of instruction have kept pace. Serious temperature effects 

that were poorly understood and not controlled in early 

operation are now better understood and controlled where 

possible. All machines now rely on computer control and 

output. Most modern machines focus on dimension lumber; 

however, they have different throughput capabilities. As a 

consequence, some machines continue to focus on highspeed planer mill operation, even exceeding 2,000 ft/min, 

whereas others are optimized for lower planer speeds or offline applications such as grading laminating stock. Lumber 

producers that focus on large markets, such as lumber for 

metal-plate-connected trusses, often select a high-capacity 

machine to match planer capability. Machines of more limited capacity are well suited for operations that may focus 

on a limited number of grades and/or sizes. 

In addition to machines that bend lumber to measure stiffness, machines use dynamic measurements, one transverse 

vibration and the other longitudinal or “stress wave” 

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propagation. These developments are based on scientific 

concepts first explored and proven in the 1960s at Washington State University (WSU). During the 1960–1970s time 

period, WSU conducted many field trials to demonstrate 

and evaluate early prototypes of both stress wave and transverse vibration machines. Collaborators included the Western Canadian Laboratory in Vancouver, BC, Canada; BC 

Hydro, Vancouver, BC, Canada; Western Pine Association, 

Portland, Oregon; West Coast Lumber Inspection Bureau 

(WCLIB) and the Western Wood Products Association, Portland, Oregon; Oregon State University, Corvallis, Oregon; 

Metriguard, Inc., Pullman, Washington; and the USDA Forest Products Laboratory (Madison, Wisconsin). Wood products tested included structural lumber, proprietary structural 

members, laminating lumber, and laminated beams. The 

WSU–Metriguard–FPL collaboration resulted in a prototype 

longitudinal stress-wave-based lumber grading machine 

using microprocessors to control the testing and analysis 

(Logan and Kreager 1975). These field trials provided basic 

experience with commercial wood products, established 

the viability of the testing concepts, and helped spur similar 

work in other laboratories and machinery companies in the 

United States, Canada, and many other countries.

Development of Density-Based 

Grading Machines

Early development of a commercial density-based grading 

system was conducted in the mid- to late 1980s. Weyerhaeuser filed a patent in 1988 for a method to estimate the 

strength or stiffness of a piece of wood by measuring its 

longitudinal density profile. This patent was issued in July 

1990. Initial development by Weyerhaeuser involved the use 

of gamma ray absorption to measure density and generate 

density profiles. As development progressed, the use of a 

radiation source was discontinued in favor of using X-rays, 

which were found to be more suitable for commercial installations (Schajer 2001). The X-rays are similar to those used 

for baggage inspection in airports, allowing the machines to 

be classified as “Cabinet X-Ray Machines.” In 1992, Weyerhaeuser licensed their density-based technology to CAE–

Newnes, a commercial equipment manufacturer in Salmon 

Arm, British Columbia. The first commercial prototype was 

installed the following year in a lumber mill in southeastern 

United States.

CAE–Newnes and Weyerhaeuser continued to participate in 

a cooperative development project, and the first commercial 

X-ray lumber gauge (XLG) was built and installed in 1993. 

One advantage of this concept was the capability to scan the 

entire board from end to end. This capability eliminated the 

need to apply additional visual restrictions to the ends of the 

pieces as is common with stiffness-based machines. With its 

noncontact methodology, the XLG is capable of processing 

pieces as short as 4 ft and can operate with line speeds of 

greater than 2,000 ft/min. Its footprint of 8 by 8 ft generally allows easy inline installation or retrofit directly behind 

existing planers. The XLG is designed to process 2-by-3 to 

2-by-12 dimension lumber. Both the ALS Committee and 

the Canadian Lumber Standards Accreditation Board accept 

the XLG as an approved mechanical grading machine. 

Approved ALSC Machine Grading

A mill planning to produce machine-graded lumber under 

the ALS using bending, transverse vibration, longitudinal 

stress wave (acoustic), or density technologies must utilize 

an approved grading machine. These machines are approved 

under the ALSC through the application to and supervision 

of a sponsoring certified grading agency. The criterion for 

approval is that the machine demonstrates the ability to segregate lumber in accordance with the measurement system 

employed, such as stiffness or density measurement. The 

evidence provided shall include determination of measurement accuracy, including appropriate statistical analysis, 

relative to an accepted consensus standard. Information 

listing the manufacturer’s recommended operational limits 

is required including information on machine measurement repeatability, variability, and recommended limits for 

the machine environment such as temperature, operational 

speed, and humidity, as well as lumber conditions such as 

temperature, moisture content, and warp.

Demonstration of the relationship between the measurements made by the machine system and design properties is 

not an integral part of ALSC approval of the machine. Requirements for this latter step are part of the machine qualification conducted on lumber of the candidate grades from 

the producing mill after installation and are specified by the 

operations manual of the ALS-approved agency. This qualification step not only includes the ability of the machine to 

make the claimed measurements but also incorporates agency-required visual criteria and specific tests, the influence of 

grade choices on sampling and test results, and mill operating variables such as sawing patterns and lumber dryness.

Detailed specifications for machine approval, agency accreditation, qualification procedures for a mill or facility by 

an agency, agency requirements for mill quality control, residual production, and ALSC monitoring of agencies is provided in the ALSC machine graded lumber policy (ALSC 

2014). Approved lumber grading machines operating on the 

basis of stiffness and density measurement by ALSC are 

listed in the Appendix to this chapter. Although not included 

in this chapter, ALSC Board of Review identifies operation 

limitations for this equipment. Current information and limitations can be accessed at the ASLC webpage (www.alsc.

org).

Application of Stiffness-Based 

Machine Grading

Machine Measurement

The intent of a machine grading system based on the measurement of stiffness is to sort using flatwise MOE. In 

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actuality, each machine makes a mechanical measurement 

that relates to stiffness response. In some machines, this 

will be the load to accomplish a target deflection; in others, 

it may be a deflection measurement under prescribed load. 

However, because the machine must grip or support the 

specimen in some fashion, the response is conditional upon 

the mechanical arrangement and processing of the signal. 

This can be compared to running a static test with the ends 

of the specimen firmly gripped in one case; in another case, 

to using “free–free” support at the ends; and in yet another 

case, to using intermediate supports so that the test section 

may be less than the length of the piece. Each static test arrangement in this example will yield a different apparent 

stiffness. In the same manner, the direct machine output is 

determined by the physical support, gripping, and measurement system.

In the early period of machine development, output could be 

quite simple, the average or the low point was based on a rudimentary measurement. Some early machines output only a 

voltage. It was the responsibility of the mill to calibrate this 

output to a grade level of MOE. Other machines produced 

only a “go–no go” response. Now, machines employ more 

sophisticated algorithms that can process the signal to account for multiple measurements and other variables.

Regardless of the type of machine, the objective is to determine a grade sort based on the machine measurement. However, because this is a machine-based stiffness measurement, 

not MOE, the machine output must be converted (calibrated) to relate to a recognized standard static measurement. 

Some modern machines output a reading in MOE, having 

done conversion in the machine algorithm for standard 

piece size and, perhaps, containing adjustments for the support and handling system of the machine. It is important to 

remember, however, that unless the machine also measures 

the size of each piece coincident with stiffness, the machine 

output is based on size assumptions. Consequently, the more 

accurately the lumber is machined, the more accurately the 

machine output will report apparent MOE. 

Static MOE measurements are the basis for grade, not dynamic machine output. Supervisory agency procedures will 

specify the standard test to be used. This may be an edgewise or a flatwise test (or both), depending upon the target 

grade or grades. Without exception, these static tests will 

have specimen support and measurement systems that differ 

from the dynamic machine system. These differences produce different apparent stiffness values between the machine 

and the static test because of differing contributions of shear, 

differing specimen “samples,” and computational choices.

The nature of the “sampling” influence needs to be understood, particularly by the quality control technician. 

Compared to a machine system, the static test may be able 

to sample more (flatwise test) or less (edgewise test) of the 

piece, depending on the machine configuration. Some 

machines take periodic readings and average the result; 

usually the static test selects one location to center the apparatus. If the output is based on only the lowest stiffness, the 

machine output may be either simple or complex (algorithm 

based). With a corresponding static test, it is often very 

difficult to identify the same location. Consequently, differences between the machine output and the static test cannot 

achieve agreement for all pieces even with the most careful 

calibration.

Influence of Lumber Characteristics

Individual pieces of lumber vary in size, moisture content, 

and MOE. All these characteristics vary both within and 

between pieces, even if the lumber is well manufactured.

Size

Within-piece variability in dimensions will be “inferred” 

as differences in MOE by stiffness-based machines when, 

in fact, the effect is one of changing moment of inertia, I, 

where I = bh3/12, with b the width and h the thickness for 

machines making a flatwise measurement. Differences in 

thickness (h) thus have a cubic effect on apparent MOE, 

whereas width (b) differences have less influence. As an 

example, assume the finished thickness target is 1.500 in. If 

the planer is set in error at 1.450 in. (keeping in mind that 

the machine is still calibrated for 1.500), the lumber will 

have 90% of the flatwise stiffness it would have shown if 

it were correctly sized. Pieces that should have graded at 

2.0E6 when correctly sized will grade instead as 1.8E6. If 

the mill problem is skip or another variation in thickness 

along the length of the piece, each area that is “thin” will 

contribute a low stiffness reading to the machine computer 

that searches for the lowest area (low point) or integrates 

readings to obtain a mean value.

The influence of variation in piece width is less than that of 

thickness, as shown by the formula for moment of inertia 

described in the previous text. Some machines have been set 

to grade rough or uneven-edged material through a calibration that conservatively compensates for width variation.

These significant influences of size control have forced machine grading mills to hold tight tolerances in planing. The 

best thickness control is maintained if planing and grading 

are done in tandem, that is, “in line.” If machine grading 

is done “off line” sometime after planing, moisture gradient adjustment and moisture pickup or loss will affect the 

dimension at time of grading and thus the accuracy of the 

MOE prediction.

Moisture Content

The influence of within-piece variability in moisture content 

can be serious but subtle because often the lumber is not 

scanned and marked by moisture distribution. The change 

in apparent MOE with moisture content (MC) is essentially 

linear in the MC region of S–Dry or MC–15 lumber. An 

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increase of 1% in MC can result in a 1.7% decrease in 

MOE. A “wet spot” that is 10% higher than the average MC 

of a piece will indicate an MOE in that area 17% lower than 

would be the case if the piece were uniform in moisture (assuming a uniform real MOE).

In some species, “wet-spot” issues may not be as important 

as the general issue of variation in MC within a lot of lumber. If the goal is S-dry lumber, often the assumption is a 

mean of 15% and a maximum of 19%. This often implies 

a range in MC within the lot of about 12% to 19%. To illustrate the significance of this moisture range on machine 

grading using stiffness, consider the following two examples. In both examples, it is assumed that planing takes 

place at the time of grading so that the sizes are uniform.

1. If all pieces in a sample had the same MOE of 1.8E6 

at a MC of 15%, but actually ranged in MC within the 

sample from 12% to 19%, the grading machine would 

evaluate the stiffness of the 19% MC pieces as if they 

were 1.7E6 and the 12% MC pieces as 1.9E6.

2. Assume a grading system intends to sort out (exclude 

from the grade) all pieces in the sample that are below 

1.8E6 (on a 15% MC basis) as perceived by the machine. Assume the same sample distribution of moisture 

as in example 1. Pieces that are 19% MC when graded 

would have to be 1.9E6 at 15% MC to have the stiffness at 19% to pass the sort “cut-off” of 1.8E6.

These moisture-induced variations in MOE are real in the 

sense that they reflect real differences in stiffness. These 

effects are found in lumber graded by any system; however, 

for stiffness-based grading systems, moisture-induced variation in MOE introduces some of the variation in grading 

machine quality control. Therefore, the effect of moisture 

influences mills to improve and control drying through 

better drying technology and better judgment in selecting 

schedules, electronic controls, and in-mill operating priorities. Yield loss and loss of control of the grading process are 

the primary issues when mill control is lost or is imprecise.

MOE Variation

Modulus of elasticity is an intrinsic wood property; that is, it 

is controlled by the fiber orientations and growth patterns in 

the wood. If the lumber is sized accurately and the moisture 

content is well controlled, variation in stiffness sensed by 

the grading machine is caused by variation in MOE within 

the piece. Regardless of the method of stiffness sensing 

used by the machine, there is a linear relationship between 

the actual MOE and the machine-sensed stiffness. Variation 

in MOE along a piece occurs naturally and is the result of 

factors such as local slope of grain around a knot or knot 

hole, spiral grain variation along the length, slope of grain 

changes caused by distortion in the bole of the tree, growth 

patterns such as pith-associated wood (juvenile wood), and 

local incidence of abnormal wood such as compression 

wood. All these sources of variation can contribute to the 

measurement of the apparent piece stiffness and in fact are 

positive factors, because the response of stiffness-based 

machines to local slope of grain, for example, is an asset. 

Nevertheless, relating machine output to a static quality 

control test is sometimes difficult unless the laboratory measurement can accurately replicate both the location and level 

of stiffness of localized, low (or high) stiffness areas sensed 

by the machine.

Quality control tests to emulate the machine measurement 

of low stiffness have often used the Pelster tester, a static 

testing system developed originally to replicate the loading system of the Stress-O-Matic (Galligan and McDonald 

2000). This system has been adopted by ASTM in Standard 

D 4761-13 (ASTM 2014d). Although the Pelster design 

may not match other grading machines as well as it does the 

Stress-O-Matic, this design has been shown to be useful for 

quality control with other machines since the relatively short 

span (48 in.) permits searching for variation in stiffness 

along a piece. Being able to sense the low area with a static 

test can be particularly critical in quality control where the 

lowest area of stiffness is a criterion for a grade boundary.

The sensitivity of the machine grading system, including the 

accuracy of the calculation method, to the average stiffness 

of the piece and/or the lowest stiffness is critical to the operating efficiency of the machine. This will be reflected in the 

sort criteria selected and verified by qualification and quality control. Some machines output both an average and a 

“lowest” signal, which can be used to set grade boundaries. 

The selection and balance between these selections depends 

on the grades selected, the number and interaction of the 

grades, the dependency of the strength test results to lowest 

stiffness criteria, and the dependency of the grade MOE to 

the “averaging” and/or “lowest” stiffness output. Because 

these decisions inherently interact with grade qualification, 

supervisory grading agency involvement is often critical 

in setting limits. Prequalification tests in concert with the 

agency are useful.

Relating Machine Measurement to 

Specifications and Quality Monitoring

Grading machines vary both from one another and from 

standard static tests in the manner of loading, sensing response, and analyzing the response. Further, machines 

produce a result that is used to estimate, not duplicate, the 

design properties, as previously shown. To further illustrate 

the “estimation” issue, consider the edgewise properties 

used most commonly for design.

Design specifications for wood structural design primarily focus on edgewise stiffness and strength properties, 

whereas grading machines that use bending measurements 

develop a “flatwise” value. Static laboratory tests used for 

quality monitoring commonly use flatwise tests but usually 

cannot completely emulate the grading machine. Further, 

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most bending machines use measurement and analysis procedures based on “sampling” as the lumber passes through 

the machine. The machine design determines the sample 

“size” (length of measurement), frequency of sampling, and 

the subsequent analysis that presents the machine output. 

Portions of the piece may not be stressed or included in the 

analysis. Stress-wave-based machines develop a unique 

longitudinal value; transverse vibration machines a different 

and unique value. Both of the latter may include essentially 

the entire length; however, all portions of the length do not 

have equal influence on the output (Murphy 2000).

Considering differences between machine measurement and 

output, design specifications, and mill quality monitoring, it 

is usually advised to focus separately on mill and specification needs. Specification tests may necessarily be arbitrary 

with respect to test method and orientation and placement 

of the piece in testing. Specification adherence will be controlled by supervisory agency requirements. For example, if 

testing an 18-ft 2 by 8 for adherence to agency requirement, 

the test span would be 152 in., approximately 70% of the 

piece length. Because one-third point loading is used, the 

full bending moment will be applied to only 51 in. (or 24%) 

of the piece. For agency qualification, the location of the 

test apparatus may be stipulated by a standard. If the piece 

is tested for both stiffness (MOE) and strength (MOR), both 

results are dependent upon a relationship to grading machine 

measurements that are not measured in the qualification or 

quality adherence tests. Thus, as noted above, successful 

adherence depends upon supervisory agency procedures for 

quality measurement and monitoring and upon consistent 

mill quality assurance procedures.

Mill quality assurance procedures, however, may be considerably different from the agency monitoring requirements. 

Because the intention is machine performance monitoring, 

rather than only adherence to the standard, the question 

is what laboratory tests can best emulate actual machine 

measurement. This includes selection of the location on the 

piece to collect the sample data and how to analyze the data.

One approach is to realize that the relationship between the 

specification MOE and the machine measurement, although 

very useful, is empirical and may be only loosely related 

to process control. For example, regardless of whether the 

grade being generated is an ALS joist grade (edgewise 

properties) or a laminating (lam) stock grade (flatwise properties), a bending-type grading machine based on “lowest” 

stiffness measures all pieces in the same empirical way. A 

grading machine that integrates short-span, flatwise values 

for an “average” value presents the piece output differently, 

and perhaps more closely to the lam grade MOE, but also 

only with an empirical relationship to the ALS joist grade 

MOE. 

Static testing against the specification using edgewise tests, 

or even some flatwise tests, may be of limited value for 

process control if the machine makes no measurements in 

that configuration. On the other hand, the “lowest” stiffness 

area flatwise may be a useful predictor of bending edgewise 

or tensile strength. Is the short-span flatwise measurement 

by the machine accurate? This requires an effort to emulate 

the machine measurement with an appropriate short-span 

static test. Is the product a lam stock grade where the principal interest is in a long span flatwise MOE? How well 

do the machine measurement and algorithm predict the 

long-span flat value? For this, a full-length flatwise test (or 

at least a long “sample” of the length) is required to assess 

machine performance.

Where possible, it is desirable to use standard test methods 

for process control as well as for tests against the specifications. This enhances communication and prevents variation 

between and within tests. One example is the use of the 

Pelster test referenced in ASTM D 4761-13 for short-span 

MOE measurement. This may not be the optimum test to 

emulate all machine measurement systems; however, its 

status as a “standard” test permits comparative testing. 

Nevertheless, to the mill operating personnel focusing on 

process control, tests specifically developed to emulate machine function may be most effective. The ultimate goal is 

to have a quality system that meets the specification requirements of the supervisory agency while providing meaningful measures of machine function for operating personnel. 

Regardless of machine function, the mill quality assurance 

challenge is assessing the function of the machine on the 

line. The better this machine function is understood, the better the quality monitoring challenge is understood, and the 

better yield and adherence to performance can be achieved. 

Although some of these issues will be incorporated in mill 

qualification and subsequent quality control interaction with 

the supervisory agency, the mill will bear the primary responsibility for identifying and controlling the interactions 

between wood quality, machine function, and grade yield.

Application of Density-Based 

Machine Grading

Machine and Test Measurement

Predicting the bending strength of a board is generally much 

more difficult than predicting the strength at a single location on the board because the applied load during testing 

is dependent on the particular load arrangement used. The 

arrangement typically used to conduct bending tests on 

machine-graded lumber has uniform load only in the central 

span of the bending test (Fig. 11.7).

The entire test span, which is normally 21 times the depth 

of the piece, could be only slightly more than 30% of the 

total length of a 20-ft 2 by 4 board, with about 10% of the 

piece undergoing maximum stress. As total length of the 

test piece becomes shorter, a higher percentage of its overall length falls within the area of maximum stress. Further 

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complicating the bending prediction is the test protocol for 

machine-graded lumber, which typically allows the board to 

be oriented randomly (with regard to edge orientation). This 

will place the maximum strength-reducing defect on the 

tension edge for some tests and the compression edge for 

others. Normally an attempt is made to locate the maximum 

defect within the area of maximum moment, but this is not 

always possible because of its location within the piece; for 

example, the maximum defect might be too close to the end. 

Judgment must also be made as to which defect will actually be the most strength-reducing. These are some factors 

and challenges that must be considered when developing 

predictive algorithms for bending strength based on density 

measurements.

Influence of Lumber Characteristics 

Size

Because predictive algorithms of density-based grading 

machines are based on an assumed constant “net” lumber 

dimension, actual variations from the assumed values induce variation in the results from the predictive formulas. 

Maintaining good size control at the planer will minimize 

size effects that might otherwise occur within and between 

pieces and will provide more accuracy in the strength and 

stiffness predictions.

Moisture Content

Moisture content is another variable that must be considered 

when predicting MOE using density-based equipment. The 

predictive algorithms are based on an assumed moisture 

content, generally 12%. Because water absorbs radiation 

just as wood does, higher MC material will appear to be 

denser and therefore stronger and stiffer. The error induced 

for strength prediction is relatively minor because the impact of the knots in the algorithms is much more influential. 

However, the error could be as high as 3% to 4% in the 

MOE predictions. Integrating an in-line moisture meter with 

a density-based machine has improved MOE prediction because it allows density to be calculated based on the actual 

MC of the piece rather than an assumed MC.

Although the addition of a moisture meter permits some 

improvement in MOE prediction, additional technology 

is currently under development to incorporate other MOE 

measuring technologies into density-based machines. When 

fully developed and integrated, it is likely that the MOE 

measuring capability of density-based machines will be on 

par with that of stiffness-based machines, with R2 values 

expected in the 0.85 to 0.95 range.

Sampling

Density data are generated by passing X-rays through the 

lumber and measuring the amount absorbed by the wood. 

X-ray detectors located on the opposite side from the X-ray 

source measure the intensity of the X-rays after passing 

though the wood. The resulting intensity is dependent on 

source strength, local density of the wood material, length of 

the radiation path, and a material-dependent constant. Current X-ray detectors are about 1 in2

 in area. If intensity measurements are taken at approximately 1/2-in. intervals along 

the length of the piece, then a longitudinal density profile 

can be determined, as shown in Figure 11.6. The same procedure takes place across the width. Wider lumber requires 

the addition of multiple X-ray sources and detectors. Not 

only can the width and shape of the X-ray beam be adjusted, 

but the increment of the density measurements can be varied as well. The predictive algorithms have the ability to 

be fine tuned by either decreasing or increasing the density 

measurements to account for how much area is included in 

each predictive segment along the length of the piece. Finer 

resolution detectors (0.1 in2) will be used in newer version 

of the XLG machine.

Commercial Machine-Graded 

Lumber

Truss Lumber

From the beginning of commercial machine grading in 

1963 until about 1968, machine grade emphasis was on 

Figure 11.7—Symmetric placement of two equal 

concentrated loads (P).

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130

development of grades for the growing metal-plateconnected roof truss market. The incentive for this emphasis 

came from the visual grading system of that era that set allowable design values significantly lower for “secondary” 

species such as white fir than for “primary” species such as 

Douglas Fir. Consequently, there was economic incentive 

to machine grade species such as white fir and Hem–Fir 

to develop grades that could compete with Douglas Fir in 

the roof truss application. The target grade of the time was 

1500f. This grade could not be produced in a significant 

quantity by visual means from secondary species, but machine grade yields were economically attractive for white fir 

from the inland empire and Hem–Fir from coastal Oregon 

and Washington.

As the metal-plate-connected wood truss industry began to 

grow and the lumber for that use began to approach a commodity, an important but specialized outlet for the higher 

grades of machine-graded lumber was developed by Trus 

Joist Corporation. The use of Trus Joist’s proprietary metal 

web industrial truss grew rapidly in the 1960s and 1970s. 

It required machine-graded lumber flanges of the higher 

grades such as 2100f and 2400f—grades higher than normally required in the metal-plate-connected wood truss 

industry, which was dominated by a relatively short-span 

house truss at that time. Trus Joist consumed a high percentage of the higher grades produced by the major western machine grading operations, thus providing an essential yield/

economic balance for those mills: mid-level grades to the 

metal-plate-connected wood house truss market and high 

grades to the proprietary metal-web/wood flange industrial 

truss.

By the mid-1970s, continued development of the proprietary industrial truss market began to outstrip the supply of 

high grades from the few western producers. No machine 

grade production was available in Canada or in the southern 

United States in this early period. Trus Joist’s needs resulted 

in the development of Canadian production; the company 

produced some machine grades in Georgia to support the 

developing market. Projections of ever-increasing needs for 

high-quality stock for the flanges of both the proprietary 

trusses and the newly developing I-joists contributed to 

the development of laminated veneer lumber by Trus Joist 

and, subsequently, less dependence on machine grades. 

Some high machine grades are still used on the flanges of 

metal-web industrial trusses. However, since the 1980s, the 

metal-plate-connected wood truss industry has developed 

longer span and industrial truss systems; at present, the full 

spectrum of machine grades, low to high, is consumed in 

that industry.

During the late 1970s, the major emphasis on efficient use 

of “secondary” species began to shift; interest increased 

in Canadian Spruce–Pine–Fir (SPF), Southern Pine, and 

Douglas Fir. The dominance of the 1500f grade, both visual and machine grades, had changed to a machine grade 

target of 1650f by the mid-1970s as a result of visual grade 

adjustments. Inquiries about the potential of machine grading spurred the issuance of a guide for lumber companies 

interested in estimating the potential benefits of machine 

grading (Galligan et al. 1977; superseded by Galligan and 

McDonald 2000).

The period 1980 to 2000 was one of continual refinement of 

the machine grade market. Acceptance of machine grades by 

the metal-plate-connected wood truss industry and by code 

and regulatory bodies is now unquestioned. Modern design 

procedures and associated design and analysis software for 

roof trusses permit evaluation of any potential grade and 

species. Machine grades in the range of 1500f to above 

1800f are regarded as commodity items. Lower grades (for 

example, 1450f) can be used for some trusses; “high” grades 

(2100f and above) are used in higher performance trusses.

Producers have concentrated on optimizing yield, with a focus on allowable properties. What was once a small, orderly 

set of machine grades—such as 1500f, 1800f, 2100f—has 

now changed to include intermediate grades, such as 1950f. 

In fact, the Southern Pine Inspection Bureau (SPIB 2014) 

lists 28 MSR classifications ranging from a low of 1.4E–

750f to a high of 2.4E–3000f. 

The advent of qualification of grades in both tension and 

bending and an understanding of the width effect on strength 

led to grades with different combinations of strength properties. For example, where 2.0E–2400f with a tensile value 

of 1925t was accepted previously, combinations based on 

performance tests yielded grades such as 2.2E–2750f–1925t 

in 2 by 4 lumber, with a comparable grade of 2.3E in 2 by 

6 lumber. The need for marketing uniformity sometimes 

added to this scenario by insisting on one property claim 

across lumber width, even though properties were different 

by width and controlled for quality on that basis. The advent 

of the machine evaluated lumber (MEL) grading system 

in the 1990s added another set of nomenclature and grade 

choices. The choice of grading system and subsequent uniformity across widths (affecting yield and efficiency in use) 

as opposed to individuality by width (affecting communication and design) is a commercial decision; however, these 

choices illustrate the evolution of the industry during the 

past 40 years.

Perhaps the most recent changes in the marketing of machine grading are applications beyond roof systems. In the 

1960s, efforts to promote the use of machine stress rated 

(MSR) lumber in floor systems were not successful. Recent 

research suggests potential for this application where credit 

can be given in the design for the reduced variability in the 

machine grades, compared with the visual grades, that are 

accepted as the standard floor component (Suddarth et al. 

1998). Lower grades of MSR, such as 1350f and 1450f, 

have shown feasibility as wall framing members (DeVisser 

et al. 1993; Galligan et al. 1994). In combination with 

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131

traditional roof and specialty truss markets, floor and wall 

applications offer a broader market base for MSR. This 

broadening of the use of machine grades has the ultimate 

potential of simplifying the grading operation within a mill 

(now a combination of many grading “systems” operating 

simultaneously) as well as marketing (now a combination of 

grades from a variety of grading “systems”).

Laminating Lumber

In the late 1960s, one research objective was to explore the 

use of machine grades for glued laminated beams. In a cooperative project between Timber Structures, Inc. (Portland, 

Oregon), Oregon State University (OSU), and machine 

grade suppliers of the West Coast Lumber Inspection Bureau (WCLIB) and the Western Wood Products Association 

(WWPA), beams were constructed of MSR lumber and 

tested at OSU in 1968 and 1970 (Johnson 1969, 1971). Subsequent studies within the laminating industry led to the exploration of E-rated grades in the late 1970s. E-rated grades 

were standardized by AITC in the 1980s and recognized under AITC/AITC A190.1-2012. The current title of the standard is ANSI A190.1-2012, “American National Standard 

for Wood Products—Structural Glued Laminated Timbers” 

(ANSI 2012). (WCLIB acquired the AITC glulam design, 

grading, and certification programs in 2013 (WCLIB 2015). 

APA–The Engineered Wood Association replaced AITC as 

the Secretariat for ANSI standards ANSI/AITC A190.1, and 

AITC 117. ANSI/AITC A190.1-2012 was renamed ANSI 

A190.1-2012, retaining the same title. AITC 117-2010 was 

renamed ANSI 117-2010, retaining the title, “Standard 

Specification for Structural Glued Laminated Timber of 

Softwood Species.”)

E-rated grades are selected by stiffness criteria based on 

“long-span” MOE (100/1 span/depth ratio, flatwise orientation) and by MC and visual criteria that ensure laminating 

quality for gluing and appearance (AITC 1988). There are 

no strength requirements for E-rated grades. For E-rated 

grades to be used in laminating, their knot characteristics 

and wood strength characteristics are considered in a manner similar to that used for visual grades in the referenced 

ASTM standard D 3737-12 (ASTM 2014c).

E-rated grades are compatible with MSR grading. The two 

systems can exist simultaneously in a grading operation if 

the criteria of both systems are maintained. The fact that 

an E-rated grade, which is not qualified for strength, is produced using the same machine as that for MSR production 

has created a legacy of confusion among both lumber producers and laminators who have not focused on the different 

requirements; that is, a history of calling an E-rated grade 

“MSR” because the lumber went through a machine has not 

enhanced the understanding of grading essentials.

In the 1980s, the WCLIB introduced tension testing 

(qualification) of MSR grades. This provided another avenue for machine grade use in laminating by meeting the 

requirements of lumber for the critical tension zone of the 

glued laminated beam. The traditional lumber for the tension zone, denoted by the nomenclature 302-24, 302-22, 

and 302-20, has to fulfill severe visual grading requirements 

to meet ASTM D 3737-13 (AITC 1988; ASTM 2014c). 

The performance requirements of these grades are tensile 

strength and MOE. Consequently, if an MSR grade is qualified in tension and meets the MOE requirements, grades 

“equivalent” to the 302 series can be developed. In addition 

to their MSR nomenclature, these MSR grades are labeled 

C14-24, C14-22, and C14-20 to show the correspondence to 

the 302 series. These labels are required to be ALSC-qualified and quality-controlled MSR grades where the qualification is in tension (AITC 1988). Because use of these grades 

is now common, the yield of tension zone material has benefited significantly.

Grade Labels

Machine grades are required to have a label that displays the 

grade and the species, MC category, mill of origin, identity 

as a machine grade, and supervisory agency (Figs. 11.8–

11.10). Agencies may develop a unique format to display 

the required information. If the grade has been qualified for 

more than one category, for example as MSR and E-rated, 

this may be indicated by additional notation. Additional 

qualification of an MSR or MEL grade for a tension laminating grade such as C14-24 may not appear on the stamp; 

the qualification may be handled by correspondence. Stamps 

are permitted to carry other information essential to proper 

interpretation in end use. Mills must work closely with the 

supervisory agency for conformance to requirements. This is 

particularly important when two agencies are involved, for 

example, an ALSC grading agency that is supervising qualification of a laminating grade by test and subsequent quality 

control and an ANSI A190.1 agency that is concerned with 

proper interpretation and application of the grade in 

laminating.

Prediction of Production Potential

One challenge to potential producers of machine grades is 

prediction of production yield. The investment of production 

personnel and equipment alone justifies a thorough study; 

however, experience demonstrates that incorporation of machine grading affects the entire mill operation and consumes 

significant effort in marketing and sales.

In most instances, machine grading production will be 

added to an existing visual grading operation. In fact, usually more than one visual grading system is in place (for example, visual stress grading and visual appearance grading). 

The spectrum of visual grades and their respective yields 

are the source for the projected machine grades; yet, this 

will change the status quo in the visual grading to which the 

mill is accustomed. Consequently, an assessment of machine 

grade potential starts with a clear picture of the current visual grading systems—variability in species, grades, and 

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132

yields, and interactions with mill operating variables (such 

as log sourcing, dry kiln operation, species selection, and 

market swings). The second step is market oriented: What 

machine grades does the mill wish to sell? The assessment 

of machine grade potential must have targets. Of course, 

these targets may be modified as knowledge is accumulated; 

however, the initial targets should be based on market 

analysis.

Once these two elements, understanding of the status quo 

and the selection of market targets, are completed, the assessment of production potential moves to mill studies using 

the grading criteria of the target grades, analyzing the yield 

by grade, and projecting the combined yields, including 

that of the visual grades. The process is described with examples and discussion in the FPL General Technical Report 

FPL–GTR–7, Machine Grading of Lumber—Practical 

Concerns for Lumber Producers (Galligan et al. 1977; Galligan and McDonald 2000). Originally published in 1977, 

FPL–GTR–7 was revised in 2000 to incorporate recent 

practice. The original histograms and data presentation were 

preserved; however, modern users of the projection method 

have often converted the process to a spreadsheet format for 

analysis. In FPL–GTR–7, grading based on bending stiffness is used to illustrate estimating grade yield. The same 

general approach can be used for other grading systems with 

appropriate changes in testing procedures.

A word of caution concerning mill yield projections is appropriate. Mill studies of this type invariably identify operating variables that the mill may wish to modify, regardless 

of the yield study output. These mill observations result 

from the focus on process by those best suited to identify the 

variables—mill operators and supervisors. Consequently, 

assumptions basic to the study, including log breakdown, 

edging and trimming practices, drying efficiency, and planing and grading, may be changed as a result of the attention 

given the assessment. These potential changes can affect 

machine grade projections and should be considered in 

completing the analysis.

Installation and Operation of 

Grading Machines

Machines for grading lumber vary widely in size, capacity, and installation. Some machines are suited for very 

high-speed production (potentially in excess of 2,000 ft/min 

velocity through the machine); others are more suited for 

lower capacity operations. The installations can vary widely 

as well. In the past, some machines were well suited for “off 

line” production and consequently required dedicated lumber infeed and outfeed equipment. Today, many machines 

closely couple to the planer where the feed is linear, whereas 

others, require lumber moving transverse to the grading 

unit. These factors influence capital cost, maintenance, and 

efficient material flow. Lumber handling for older systems 

are discussed in FPL–GTR–7 (Galligan and McDonald 

2000). This publication provides actual mill flow patterns to 

aid estimating equipment needs and discusses environmental concerns of mill operation. Temperature, vibration, dust, 

and noise are all important considerations for measurement 

equipment. Because these concerns change as technology 

progresses, it is important to work closely with equipment 

manufacturers.

The operation of a grading machine has always required 

an investment in technician and/or quality control personnel beyond previous staffing. Without this added personnel, 

agency quality control requirements and maintenance of 

Figure 11.8—Facsimile of MSR grade stamp. Essential 

components include notations for grade, moisture content, species, MSR, mill, and agency. Other information 

may be incorporated as well.

Figure 11.9—Facsimile of MEL grade stamp.

Figure 11.10—Facsimile of grade stamp for an E-rated 

grade.

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the machine and its auxiliary devices are difficult to accomplish. As machines have become more sophisticated, 

electronic technician skills have become more important. 

Mills concerned about optimization of yield and process 

variables have found it desirable to have support personnel 

that understand wood properties, can participate technically 

with the supervisory grading agency, and communicate well 

within the mill operating structure, particularly in support 

of marketing. The lack of emphasis on these factors in the 

early days of machine grading contributed to many commercial failures.

Optimization of Machine Grading

Processing Interactions

The actual machine grading operation is one of the final 

processing steps in a mill. As a result, lumber that reaches 

the grading machine is the result of all previous processing 

steps. Because machine grading measures some physical 

or mechanical properties and also incorporates visually detected characteristics, the process is more sensitive to some 

processing steps than is visual grading. As noted previously, 

these include the effect of drying on stiffness measurement 

and the quality of surfacing on machine response. However, 

this sensitivity to processing extends well “upstream” to 

initial log selection, log breakdown, and all subsequent operations that affect the variable or variables measured by the 

machine system.

Log Selection and Primary Breakdown

The influence of log selection and initial breakdown is 

based on wood quality. This, in turn, is affected by the age 

of the tree and by the location of the log in the stem, as well 

as other variables. A number of log features can produce 

less desirable lumber. Machine grading will sense these 

features—a desirable result from a grading perspective. 

However, negative effects on grade yield may be countered 

somewhat by such practices as the proper selection of the 

log and the method of log bucking.

All trees contain juvenile wood; the proportion of this wood 

varies by the age of the wood and by log location in the 

stem. Juvenile wood is lower in stiffness and strength than 

is mature wood and directly affects the yield of machine 

grades. Butt logs often contain severe swell, which results in 

growth ring and fiber distortion in the lumber cut from this 

region. Severe taper and sweep can result in lumber with 

severe slope of grain, which is detected by some grading 

systems. Logs from leaning trees may contain compression wood (softwood species) or tension wood (hardwood 

species); both are termed abnormal wood and will reduce 

lumber yield of higher machine grades. The incidence of 

abnormal wood and its characteristics varies with species. 

In summary, the most fundamental wood characteristics 

observed in machine grade yield studies originate in and are 

controlled in large measure by tree and log selection.

Sawmill Processing

When the tree-length log is bucked into processing log 

lengths, the lumber quality scenario narrows in focus. Log 

length decisions interact with market desires for lumber 

length. Market desires for length vary by grade: for example, longer lengths for higher grades (longer span end uses) 

and shorter lengths for lower grades (wall and floor elements). However, grade can also interact—long length wood 

consisting of juvenile wood will not make a high grade. This 

is further confounded by the radial gradient of quality of the 

stem, generally low to high, pith to bark. Because the geometry of cutting rectangles from circles dictates that wide 

widths must be cut close to the pith, the grade influence of 

radial wood quality gradients interacts with lumber width. 

These variables all affect yield of machine grading systems 

that are sensitive to wood quality in the stem. This, in turn, 

affects market targets for the mill.

Mill edging and trimming operations are critical to any 

lumber grading system. They are particularly important to 

machine grading because grading machines will respond 

adversely to physical or mechanical characteristics in the 

lumber, such as slope of grain, that may have been avoided 

with better processing. Conversely, the machine system can 

respond positively to good wood such as higher density/

higher stiffness wood next to acceptable wane from the 

outer part of the stem. If the machine grade target is E-rated 

laminating (lam) stock, edging for lam stock takes special 

attention and can be linked to discernable wood quality features. Good edging practices are rewarded.

It should also be noted that machines configured to capture 

stiffness or density measurements along the length of the 

board have the potential to optimize value by length and 

grade.

Drying and Planing

The importance of proper drying was discussed previously 

in this chapter. However, drying for structural applications 

often receives too little attention. Structural wood should not 

be produced with severe drying-related stress gradients that 

will encourage subsequent distortion. Wood that is dried at 

too high a temperature loses strength; lumber that is dried 

below approximately 8% to 10% MC has lost strength. 

Lumber that retains severe wet spots can reduce grade yield. 

Because machine-graded lumber is qualified by strength 

tests as well as by MOE, mill yield is affected by the processing decisions made prior to grading. MOE and density 

grading systems and subsequent quality control will not 

identify weak wood created by poor drying practices. Efficiency in drying is often measured by hours in the kiln or by 

British thermal unit (Btu) per charge. In a mill that produces 

structural material, especially by machine grading technology, drying efficiency includes consideration of grade yield 

and structural performance in qualifying tests and quality 

monitoring.

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Lumber size and surface quality interact with drying practices as well as many “upstream” mill processing procedures. 

Surfacing quality is important to any system that must contact the lumber—as in stiffness-based machines that employ 

rolls and contact sensors. Holding tight planing tolerances 

is feasible with modern planers; this enhances yield coupled 

with good mill sizing and dry kiln practices. However, sizing tolerances are strict for machine-graded lumber destined 

for laminating. Good planing opens this market potential.

Grading Interactions

In most traditional visual grading mills, more than one 

grading system operates concurrently with little apparent 

confusion or conflict. The most prominent example is visual 

stress grades (such as #1 & Btr, #2, Standard) and appearance grades (C & Btr, for example) being graded on a chain 

at the same time. Because this is traditional and all decisions 

are visual, decisions of precedence are often merely based 

on price or value. With the introduction of machine grading, 

which involves nonvisual variables, decisions of precedence 

become somewhat more complicated and some rules are 

applied.

Concurrent Grading Systems

In most mills, the machine grading system operates concurrently with one or more other systems. In other words, 

all systems present options to the final grading decision. 

All systems compete for yield; price differentials exist for 

each piece of lumber. Three competing systems are common: ALS visual stress, ALS visual appearance, and ALS 

machine grade. Five systems are readily found: ALS visual 

stress, ALS visual appearance, ALS machine grade, ANSI 

E-rated, and foreign visual. Some mills will add further systems for proprietary grades.

Grading systems with different selection criteria will conflict when operating concurrently. Resolution of the conflict 

depends on either value-added comparison or arbitrary rules. 

Traditionally, clear lumber will have greater value than one 

of the higher machine stress grades. Consequently, even 

though a given piece of lumber could meet both the high 

stress requirement by a measured criterion (such as density) 

and the clear criteria by visual assessment, the higher valued clear piece would be selected. Likewise, some lumber 

will meet both visual stress grade and machine stress grade 

criteria. Although there may be a yield difference depending 

on the resource and grading criteria, the machine grade will 

often have a higher allowable design value for roughly the 

same visual criterion, which will translate to a higher market value. This is also where the arbitrary rule that governs 

precedence between visual and machine stress grade rules is 

applied.

The ALSC rule states that the highest visual stress grade that 

may be produced simultaneously with machine grades must 

be lower in bending stress value than that of the lowest 

machine grade being produced. This recognizes the 

comparative grading efficiency of the machine system and 

eliminates the risk of selecting visually stress-graded lumber from lots of lumber that had been rejected at that stress 

level by the quality-controlled machine stress system.

It is important that a producer consider grading system options prior to beginning machine grade qualification tests. 

Will pulling grades simultaneously affect qualification 

levels? Will competing grades be added in the near future 

and thus should be included now in sampling? Even if there 

were no effects on qualification levels, will noncurrent but 

anticipated grades affect yield significantly in the future? 

For example, there are no ALSC restrictions on nonstress 

grade selection concurrent with machine grades. Yet, if a 

nonstress grade removes a significant quantity of good wood 

from the resource, the mill may have trouble holding the 

necessary quality control levels or, more simply, the yield of 

the machine grade may reach an uneconomic level.

Laminating grades of L1, L2, and L3 are ALSC and ANSI 

nonstress grades commonly graded on the same chain as are 

visual and machine stress grades. Similarly, ALS and ANSI 

E-rated grades can be graded at the same time as stress 

grades. Mill-specific experience may show that this simultaneous grading does not cause conflict in qualification levels 

because of different selection criteria. However, in other 

mill scenarios, simultaneous grading may exert a measurable effect, and this almost always causes yield conflict. It is 

strongly advised that mills, in concert with their supervisory 

grading agency, examine potential grading scenarios and 

address them appropriately in qualification and subsequent 

quality monitoring.

Virtually all grades that are simultaneously on the grading 

chain interact in the overall yield. If one grade is changed 

(for example, criteria adjusted or grade removed), one or 

more other grades will be affected, either in performance, 

appearance, yield, or some combination of these. Understanding concurrent grading is critical to mill practice.

Optimizing Grades

Customer requirements change, grading technology improves, knowledge increases, and mill resources change. 

All these factors are dynamic—all have occurred during the 

maturation of machine grading. To respond, new levels of 

MOE and Fb have been developed. Traditional MOE/Fb/Ft

relationships have been challenged by test and are now more 

flexible. The variety of machine grades, grading machines, 

and grading systems has increased. ASTM D 6570-04(2010) 

(ASTM 2014e) provides new guidance, a framework within 

which all allowable properties of machine grades can be 

examined, and the most appropriate method of assignment 

used.

There will always be a creative tension between marketing 

(the concomitant desire for maximum flexibility to adjust to 

the customer and stability), grading technology (which appears to yield an infinite variety of options, though limited 

Chapter 11
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135

in actual practice by the nature of wood and knowledge), 

and mill operation (given finite resources and short-term 

goals). The mutual objective is to optimize the resource for 

yield to the mill and value to the customer. Close communication with supervisory agencies and customers is needed to 

make grade optimization effective.

In-Plant Assessment

Visualizing relative grade yield and possible grade potential 

with respect to actual strength performance of mill grades 

is often difficult in the mill environment, where the primary 

emphasis is on meeting (and not overstating) grade strength 

criteria. But how “rich” are the grades? What is the strength 

profile of each grade with respect to the adjacent grades in 

the grading matrix? Is there a potential for improved yield 

or for different grade combinations?

The evaluation of mill machine grades can be visualized as 

a matrix in which samples of each grade are proof-loaded 

to the design level of the highest grade. The performance of 

each grade is measured against both expected performance 

related to its design level, the percentage of pieces failing to 

meet requirements, and the percentage of pieces that would 

qualify for a higher strength grade if they could be identified 

in the grading system. If the grading system were perfect, 

exactly 5% of each grade would fall below the target value 

for the grade and each grade would be tightly grouped by 

strength into a unique group (that is, no overlap in strength 

between grades). The fact that both of these concepts are 

basically unobtainable in the practical world of mill grading 

was introduced by Miller (1964). Consequently, the matrix 

test of mill grades gives a real-world assessment of grades 

being produced. This look at machine grades actually in production is a useful tool to reassess both mill efficiency and 

market targets at intervals during production.

A more detailed presentation of the basics of grade matrix 

evaluation was published in Galligan (1985). A practical 

matrix example for a mill with four machine grades, including an explanatory diagram, appears in Galligan and McDonald (2000).

Recent Advances and 

Developments in Nondestructive 

Science and Commercial 

Applications

Nondestructive Science

Commercial machine measurements vary in load application and measurement. Also, static measurements conducted 

as a part of quality assurance programs differ from on-line 

machines. Consequently, the relationship between machine 

output and static calibration varies. Complexities in these 

relationships force empirical approaches and can thwart 

thorough analysis.

Several noteworthy scientific contributions regarding machine grading during the past 10 years begin to address 

these issues. Bechtel (2007) performed an in-depth analysis, 

based on fundamental engineering mechanics, of the effect 

nonuniform mechanical properties have on the measurement of stiffness, as currently practiced. Murphy (2000) 

developed a numerical solution for vibrating tapered beams. 

The method used approximation by portioning the beam 

into constant property short lengths. While directed towards 

solving a tapered beam problem, it accounts for mass density and moment of inertia changes along the length of the 

beam. Given that most pieces of structural lumber have 

considerable variation in properties along their length as a 

consequence of naturally occurring defects, the works of 

Bechtel and Murphy point out the inherent limitations of 

measurement systems currently used and lay the foundation 

for the next generation of analysis techniques used in commercial equipment. 

Commercial Applications

During the past decade, significant growth in the use of mechanical grading concepts for structural lumber has occurred 

worldwide. Research and development efforts on the use 

of these concepts for grading lumber from a wide variety 

of species has taken place, the results of which are being 

implemented in Asia, Australia and New Zealand, Europe, 

North America, and South America. Many excellent scientific papers presenting results from these efforts are included 

in proceedings of the International Nondestructive Testing 

and Evaluation of Wood Symposia (Ross and Wang 2012). 

In addition to widespread growth of mechanical grading of 

lumber utilizing bending type nondestructive testing technologies, there has been recent growth in the development 

and use of acoustic/stress-wave-based lumber grading systems. Several machine manufacturers, including Brookhuis 

Micro Electronics, Calibre Equipment Limited, Dynalese 

AB, Falcon Engineering, and Metriguard, Inc., now supply 

acoustic/stress-wave-based grading machines approved under ALS for grading structural lumber.

Integration of nondestructive technologies within a mill 

has been an important objective for maximum efficiency 

for many years. Advances in lumber size measurement and 

control in saw centers, intelligent scanning for trimming 

decisions, advanced drying monitoring and control, and effective moisture monitoring have made significant impacts 

and they are partners with good grading practices. It is 

now possible to add log scanning for anticipating lumber 

quality levels—in effect, moving nondestructive testing 

upstream in the mill to further enhance the overall mill 

grading quality processes. In-depth presentation of the science and application of nondestructive evaluation of logs 

is covered in detail in Chapters 8 (“Acoustic Assessment 

of Wood Quality in Trees and Logs”) and 9 (“Laser Scanning of Logs and Lumber”). The science and technology 

Machine Grading of Lumber
General Technical Report FPL–GTR–238

136

has advanced significantly and has resulted in development 

of commercially available tree and log grading systems. 

Future Developments

Forecasting future developments is a perilous task and not 

fruitful in the context of this chapter. Rather, the appropriate 

emphasis is on deficiencies in current systems, that is, issues 

that can be objectives for the future. These issues can be 

grouped loosely in three categories: grading efficiency, integrated systems, and design reward. These categories are not 

intended to be all inclusive and are offered to spur creative 

future work.

Grading Efficiency

Although there are differences in the efficiency with which 

strength properties are estimated by existing strength predictors, efficiency, as measured by R2

, seldom exceeds 0.65. 

This means that approximately 40% of the variability in 

strength is not related to the property being measured for 

estimation purposes. This level of efficiency has been sufficient to sustain a machine grading industry only because it is 

more efficient than the visual grading system it supplements. 

This is not a strong endorsement. Increases in strength property estimation with practical mill measurements are seriously needed. An ancillary concern is that attention be given 

to the concomitant properties (such as MOE and shear) that 

are essential for design.

Integrated Systems

Many machine grading systems in North America make 

multiple mechanical or physical measurements. These systems include visual appraisal as well as the mechanical or 

physical measurement made by a device. Some of this visual 

appraisal is integrated into the process for predicting properties, for example, the size of the permitted edge characteristic. Another aspect of visual appraisal is cosmetic—no less 

important than the prediction of mechanical properties but 

not directly integrated into this process. In addition, size and 

MC are not only components of the grade description but 

also variables that affect mechanical property prediction. 

With few exceptions, all these grading-related variables are 

controlled independently in the production mill, yet most are 

not independent of each other. The previous statement on 

grading efficiency is further confounded by this simplistic 

treatment of a covariate process. Comprehensive integrated 

systems at the practical level are needed for mill operation.

Design Reward

From a mill perspective, the increased efficiency of a machine grading system compared with that of its visual counterpart is measured as yield—more yield of a given grade 

level or yield of grades not identifiable with the visual grading system. For about 40 years, this yield reward has been 

sufficient to satisfy the machine grading community. However, if the resource is being differentiated more efficiently, 

should this be reflected in design efficiency? Currently, there 

is design credit for machine-graded material in a limited 

number of applications such as compression chords of trusses, wall framing, and column design. However, it is utilized 

widely only in truss design. Except in the cases mentioned, 

the piece-by-piece machine grading and associated quality 

control are not rewarded in the marketplace by formal design recognition. Restricting all the reward for an expensive 

mill grading process to mill yield limits encouragement for 

improving the system. Design recognition of the qualitycontrolled, lot-based performance of machine-graded products should be an important goal. Proposed more than 40 

years ago, this goal of comprehensive design recognition of 

the lot by lot efficiency of nondestructive testing of lumber 

by machine has yet to be achieved (Galligan and Snodgrass 

1970).

Acknowledgment

We thank Don Devisser, Executive Vice President, West 

Coast Lumber Inspection Bureau, for his valuable 

comments.

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70–74.

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MSR alternative. Alexandria, VA: Construction Specifier.

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strength prediction. Weyerhaeuser Proprietary Res. Rep. 

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other countries. In: Hoyle, R.J., ed. Machine stress-rated 

lumber. Proceedings, 3rd short course. Pullman, WA: Washington State University.

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Spokane, WA: Pullman, WA: Washington State University: 

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latest edition.) Washington, DC: American Forest and Paper 

Association.

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modulus of elasticity for dimension lumber. In: Proceedings, 

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evaluation of wood—50 years of research: international 

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scanning. Forest Products Journal. 51(1): 43–50.

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SPIB. 1996. Standard grading rules for Southern Pine lumber. Pensacola, FL: Southern Pine Inspection Bureau.

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Pensacola, FL: Southern Pine Inspection Bureau. 

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differing lumber grading systems. Forest Products Journal. 

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WCLIB. West Coast Lumber Inspection Bureau, Portland, 

Oregon.

1964. Standard grading and dressing rules, Rules 15, 

September 1, 1964.

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Rules 16, 1989.

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Rules 17, September 1, 1991.

1992. Standard grading rules for West Coast lumber, 

Rules 17, 1992.

1997. Standard grading rules for West Coast lumber, 

Rules 17, Supplement VIII(B), April 24, 1997.

2000. Standard grading rules for West Coast lumber, 

Rules 17, 2000.

2015. Don Devisser. Personal communication.

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1965 Standard grading rules, January 1, 1966.

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1994.

Ziegler, G. 1997. Machine grading processes for softwood 

lumber. Madison, WI: Forest Products Society. Wood Design Focus. 8(2): 7–14.

Chapter 11
Nondestructive Evaluation of Wood: Second Edition

139

Appendix—ALSC-Approved Machine Manufacturers Known to be in 

Commercial Production at Time of Publication (ALSC 2015)

For detailed information on approved models, refer to ALSC (www.alsc.org).

Machine Grading of Lumber

Manufacturer Method 

Brookhuis Micro Electronics 

Institutenweg 15 

7521 PH Enschede 

Enschede, Netherlands 

www.brookhuis.com 

Stress wave/acoustic 

Calibre Equipment Limited 

PO Box 2783 

Wellington, New Zealand 

calibre-equipment.com 

Stress wave/acoustic 

Conception R.P., Inc. 

405 Galilee Ave, 

Quebec, QC, Canada, G1P 4M6 

www.conceptionrp.com 

Bending 

CRiQ, Centre de recherche industrielle du Québec 

333 rue Franquet 

QC, G1P4C7, Canada 

www.criq.qc.ca 

Bending 

Dimter GmbH Maschineenfabric 

Illertissen, Germany 

www.weinig.com 

Density 

Stress wave/acoustic 

Dynalyse AB 

Brodalsvägen 7, SE-43338 Partille, Sweden 

www.dynalyse.se 

Stress wave/acoustic 

Falcon Engineering USA 

4475 Mission Blvd #234 

San Diego, CA 92109 

www.falconengineeringusa.com 

Stress wave/acoustic 

Lucidyne Technologies, Inc. 

155 SW Madison Ave. 

Corvallis, OR 97333 

www.lucidyne.com 

Density 

Measuring and Process Control Ltd. 

Unit 2 Tabrums Industrial Estate, Tabrums Lane 

Battlesbridge, Essex, SS11 7QX UK 

www.mpcuk.co.uk 

Bending 

Metriguard, Inc. 

2465 NE Hopkins Ct, PO Box 399 

Pullman WA 99163 USA 

www.metriguard.com 

Bending 

Transverse vibration 

Stress wave/acoustic 

Microtec 

Via Julius-Durst Str. 98 

39042 Bressanone/Brixen (BZ), Italy 

www.microtec.eu/en 

Stress wave/acoustic 

Density 

USNR 

1981 Schurman Way 

Woodland, WA USA 98674 

www.usnr.com 

(approved under Newnes Machine Ltd. Company) 

Density 

Weyerhaeuser 

PO Box 9777 CH 2C27 

Federal Way, WA 98063 

Density 

Nondestructive Evaluation of Wood: Second Edition

141

Chapter 12

Inspection of Timber Structures Using 

Stress Wave Timing Nondestructive 

Evaluation Tools

Robert J. Ross, Supervisory Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

Roy F. Pellerin, Professor Emeritus

Washington State University, Pullman, Washington

The purpose of this chapter is to provide guidelines on the 

application and use of the stress wave timing inspection 

method in locating and defining areas of decay in timber 

structures. Practical procedures for field testing, workable 

forms for gathering evaluation data, and guidelines for interpreting data are provided. This information was derived 

from research that quantified the ability of stress wave 

timers to detect decay in wood, from laboratory and field 

studies of deteriorated timber bridges, and most importantly 

from the experience of timber bridge inspectors familiar 

with the use of commercially available devices. A table that 

lists current manufacturers of these devices is included. 

Properties of wood and important aspects of wood deterioration are also reviewed to provide those who are unfamiliar 

with wood the basic information necessary to detect decay. 

This chapter was originally published in 2014 as chapter 3 

of the Wood and Timber Condition Assessment Manual, 

Second Edition, a Forest Products Laboratory General Technical Report:

White, Robert H.; Ross, Robert J. (Eds.). 2014. 

Wood and timber condition assessment manual: 

second edition. General Technical Report FPL–GTR–

234. Madison, WI: U.S. Department of Agriculture, 

Forest Service, Forest Products Laboratory. 93 p.

Principles of Stress Wave 

Nondestructive Testing for 

Condition Assessment

As an introduction, a schematic of the stress wave concept 

for detecting decay within a rectangular wood member is 

shown in Figure 12.1. First, a stress wave is induced by 

striking the specimen with an impact device (such as the 

hammer shown in the illustration) that is instrumented with 

an accelerometer that emits a start signal to a timer. A second accelerometer, which is held in contact with the other 

side of the specimen, senses the leading edge of the propagating stress wave and sends a stop signal to the timer. The 

elapsed time for the stress wave to propagate between the 

accelerometers is displayed on the timer.

The velocity at which a stress wave travels in a member is 

dependent upon the properties of the member only. The term 

ultrasonic and sonic refer only to the frequency of excitation 

used to impart a wave into the member. Ultrasound frequencies begin at 20 kHz; sonic frequencies are between 20 Hz 

and 20 kHz. All commercially available timing units, if calibrated and operated according to the manufacturer’s recommendations, yield comparable results.

The use of stress wave velocity to detect wood decay in timber bridges and other structures is limited only by access to 

the structural members under consideration. It is especially 

useful on thick timbers or glued-laminated (glulam) timbers 

≥89 mm (≥3.5 in.) where hammer sounding is not effective. 

Access to both sides of the member is required.

Because wood is an organic substance, material properties 

and strength vary in accordance with the direction wood 

is hammered compared with the cell structure orientation. 

Hammering the end grain of a beam or post will cause a 

primarily longitudinal shock wave along the length of the 

Figure 12.1—Stress wave timer.
General Technical Report FPL–GTR–238

142

cell structure in the timber. Hammering the side or top of 

the beam will cause a wave across or transverse to the wood 

cells. Cells are arranged in rings around the center of the 

tree.

The velocity at which a stress wave propagates in wood is a 

function of the angle at which the fibers of wood are aligned 

(which is also a determinant of other physical and mechanical properties). For most structural members, fibers of the 

wood align more or less with the longitudinal axis of the 

member (Fig. 12.2).

Stress wave transmission times on a per length basis for 

various wood species are summarized in Table 12.1. Stress 

wave transmission times are shortest along the grain (parallel to the fiber) and longest across the grain (perpendicular 

to fiber). For Douglas-fir and Southern Pine, stress wave 

transmission times parallel to the fiber are approximately 

200 μs/m (60 μs/ft). Stress wave transmission times perpendicular to the fiber range from 850 to 1,000 μs/m (259 to 

305 μs/ft).

Effect of Ring Orientation

Researchers have determined that the longest transverse-tograin transmission times are found at a 45° orientation to the 

annual rings. The shortest is about 30% faster in a path that 

is radial (Fig. 12.3). Table 12.2 and Figure 12.4 show stress 

wave transmission time for wood of good quality at 12% 

moisture content. These values can vary ±10% for species 

variation. These times are based on an assumed stress wave 

transmission time of 668 μs/m radially, 800 μs/m tangentially, and 995 μs/m at 45° to grain.

Effect of Decay

The presence of decay greatly affects stress wave transmission time in wood. Table 12.3 summarizes stress wave transmission values obtained from field investigations of various 

wood members subjected to degradation from decay. Stress 

wave transmission times perpendicular to the grain are drastically increased when the member is degraded. Transmission times for nondegraded Douglas-fir are approximately 

800 μs/m (244 μs/ft), whereas severely degraded members 

exhibit values as high as 3,200 μs/m (975 μs/ft) or greater.

Figure 12.2—Three principal axes of wood with respect to 

grain direction and growth rings.

Table 12.1—Summary of research on stress wave transmission times for various 

species of nondegraded wood 

Reference Species 

Moisture 

content 

(% OD)a

Stress wave transmission time (μs/m (μs/ft)) 

Parallel to grain Perpendicular to grain 

Smulski 1991 Sugar maple 12 256 to 194 (78 to 59) — 

Yellow birch 11 230 to 180 (70 to 55) — 

White ash 12 252 to 197 (77 to 60) — 

Red oak 11 262 to 200 (80 to 61) — 

Armstrong et al. 

1991 

Birch 4 to 6 213 to 174 (65 to 53) 715 to 676 (218 to 206) 

Yellow-poplar 4 to 6 194 to 174 (59 to 53) 715 to 676 (218 to 206) 

Black cherry 4 to 6 207 to 184 (63 to 56) 689 to 620 (210 to 189) 

Red oak 4 to 6 226 to 177 (69 to 54) 646 to 571 (197 to 174) 

Elvery and 

Nwokoye 1970 

Several 11 203 to 167 (62 to 51) — 

Jung 1979 Red oak 12 302 to 226 (92 to 69) — 

Ihlseng 1878, 1879 Several — 272 to 190 (83 to 58) — 

Gerhards 1978 Sitka spruce 10 170 (52) — 

Southern pine 9 197 (60) — 

Gerhards 1980 Douglas-fir 10 203 (62) — 

Gerhards 1982 Southern pine 10 197 to 194 (60 to 59) — 

Rutherford 1987 Douglas-fir 12 — 1,092 to 623 (333 to 190) 

Ross 1982 Douglas-fir 11 — 850 to 597 (259 to 182) 

Hoyle and Pellerin 

1978 

Douglas-fir — — 1,073 (327) 

Pellerin et al. 1985 Southern pine 9 200 to 170 (61 to 52) — 

Soltis et al. 1992 Live oak 12 — 613 to 1,594 (187 to 486) 

Ross et al. 1994 Northern red 

and white oak 

green — 795 (242) 

a

OD, ovendry. 

Chapter 12
Nondestructive Evaluation of Wood: Second Edition

143

A 30% increase in stress wave transmission times implies 

a 50% loss in strength. A 50% increase indicates severely 

decayed wood (Fig. 12.5). Transverse travel paths are best 

for finding decay. Parallel-to-grain travel paths can bypass 

regions of decay.

Weight loss is not a good indicator of decay because considerable strength loss can occur without significant weight 

loss. As Figure 12.5 illustrates, significant loss of strength 

occurs before noticeable weight loss.

Effect of Moisture Content

Considerable research has shown the effect of moisture in 

wood on stress wave transmission time. Several studies 

have revealed that stress wave transmission times perpendicular to the grain of wood follow the relationship shown 

in Figure 12.6. At moisture contents less than approximately 

Figure 12.3—Transverse stress wave paths and transmission times: (a) timber, (b) glulam beam.

Figure 12.4—Transverse stress wave transmission time 

compared with annual ring orientation.

Figure 12.5—Relationship between stress wave transmission time and fungal degradation (Pellerin et al. 1985) 

(1 lb/in2 = 6.9 kPa).

Table 12.2—Typical stress wave transmission 

times for nondecayed Douglas-fir at 12% 

moisture content 

Path length 

(mm (in.)) 

Stress wave transmission time (μs) 

Radial Tangential 45° to grain 

64 (2.5) 43 51 64 

89 (3.5) 60 71 88 

140 (5.5) 94 112 139 

184 (7.25) 123 147 183 

235 (9.25) 157 188 234 

292 (11.5) 195 234 290 

342 (13.5) 229 274 340 

394 (15.5) 264 315 392 

444 (17.5) 297 355 442 

495 (19.5) 331 396 492 

Inspection of Timber Structures Using Stress Wave Timing NDE Tools
General Technical Report FPL–GTR–238

144

30%, transmission time decreases with decreasing moisture 

content. Corrections for various moisture content values are 

summarized in Table 12.4.

At moisture content values greater than approximately 30%, 

little or no change in transmission time occurs. Consequently, there is no need to adjust the measured values for wood 

that is tested in a wet condition.

Effect of Preservative Treatment

Treatment with waterborne salts has almost no effect on 

stress wave transmission time. Treatment with oilborne 

preservatives increases transmission time to about 40% 

greater than that of untreated wood. Round poles are usually 

penetrated to about 37 to 61 mm (1.5 to 2.5 in.). Table 12.5 

was calculated to show expected travel time for round poles 

treated with oilborne preservatives. Although these data illustrate the effect oilborne treatments have on transmission 

time, these values should not be used to estimate level of 

penetration.

Interpretation of Stress Wave 

Velocity Readings

The guidelines in this chapter are useful in interpreting 

readings that are less than those for sound wood. Voids and 

checks will not transmit stress waves. Knots will act as parallel-to-grain wood but are usually oriented perpendicular to 

the long axis of timber.

Based on the direction and length of the stress wave path 

in the wood, moisture content of the wood, and whether or 

not preservative treatment is present, the velocity and travel 

time for sound wood can be determined. For the transverse 

direction, the annual ring orientation and the existence of 

seasoning checks should be recorded.

Figure 12.6—Transverse stress wave transmission times in 

Southern Pine and red oak piling.

Table 12.3—Summary of research on use of stress waves for detecting decay in timber structures 

Reference Structure Wood product Test Analysis 

Volny 1992 Bridge Douglas-fir 

glulam, creosote 

pressure treated 

Stress wave transmission 

time perpendicular to grain, 

across laminations at 0.3-m 

(0.98-ft) intervals 

Sound wood: 1,279 μs/m (390 μs/ft) 

Moderate decay: 1,827 μs/m (557 μs/ft) 

Severe decay: 2,430 μs/m (741 μs/ft) 

Ross 1982 Football 

stadium 

Solid-sawn 

Douglas-fir, 

creosote 

pressure treated 

Stress wave transmission 

time perpendicular to grain, 

near connections 

Sound wood: 853 μs/m (260 μs/ft) 

Incipient decay: 

 –Center of members: 

 1,276 μs/m (389 μs/ft) 

 –38-mm-thick solid wood shell: 

 2,129 μs/m (649 μs/ft) 

Severe decay: >3,280 μs/m (1,000 μs/ft)

Hoyle and 

Pellerin 1978 

School 

gymnasium 

Douglas-fir 

glulam arches 

Velocity of stress wave 

transmission time 

perpendicular to grain, 

near end supports 

Sound wood: 1,073 μs/m (327 μs/ft) 

Decayed wood: 1,574 μs/m (480 μs/ft) 

Table 12.4—Stress wave transmission time adjustment factors for 

temperature at various moisture contents for Douglas-fir 

Moisture 

content (%) 

Adjustment factors 

–18 °C (0 °F) 3 °C (38 °F) 27 °C (80 °F) 49 °C (120 °F) 

1.8 0.94 0.95 0.97 0.98 

3.9 0.95 0.96 0.98 0.99 

7.2 0.93 0.98 1.00 1.01 

12.8 0.97 0.99 1.00 1.01 

16.5 0.99 1.01 1.03 1.05 

23.7 1.05 1.07 1.09 1.14 

27.2 1.07 1.10 1.12 1.17 

Chapter 12
Nondestructive Evaluation of Wood: Second Edition

145

Measurement of Stress Wave 

Transmission Time

General Measurement

Several techniques can be used to measure stress wave 

transmission time in wood. The most common technique 

uses simple time-of-flight-type measurement systems. Two 

commercially available systems that use this technique are 

illustrated in Figures 12.7 and 12.8. Note that for ultrasonic 

measurement systems, a couplant may be used between the 

transducers and the specimen.

With these systems, a mechanical or ultrasonic impact is 

used to impart a wave into the member. Piezoelectric sensors are placed at two points on the member and used to 

detect passing of the wave. The time required for the wave 

to travel between sensors is then measured.

Commercially Available Equipment

Several equipment development firms are producing portable equipment based on this concept. Table 12.6 presents 

contact information for obtaining information on these 

devices. 

Field Considerations and Use of 

Stress Wave Methods

Stress Wave Transmission Time

Figure 12.9 outlines the general procedures used with stress 

wave nondestructive evaluation methods for field work. 

Before venturing into the field, it is useful to estimate stress 

wave transmission time for the size of the members to be 

inspected. Preceding sections provided information on various factors that affect transmission time in wood. This 

information can be summarized, as a starting point, by 

simply using a baseline transmission time of 1,300 μs/m 

(400 μs/ft). Transmission time, on a per length basis, less 

than this would indicate sound material. Conversely, transmission time greater than this value would indicate potentially degraded material. Using this value, you can estimate 

the transmission time for a member by knowing its thickness (path length) and the following formula:

Tbaseline(μs) = 1,300 × WTD

where Tbaseline is baseline transmission time (μs) and WTD is 

wave transmission distance (path length) (m).

[Inch–pound formula: Tbaseline (μs) = 400 × WTD, where 

WTD is wave transmission distance (path length) (ft)]

By knowing this number for various thicknesses, field work 

can proceed rapidly.

Field Data Form

An example of a typical field data acquisition form is 

shown in Figure 12.10. Key items to include on the form 

are structure name, location, number, inspector, and date 

of inspection.

Field Measurements

Field use should be conducted in accordance with instructions provided by equipment manufacturers. In the field, 

extra batteries, cables, and sensors are helpful. Testing 

should be conducted in areas of the member that are highly 

susceptible to degrading, especially in the vicinity of connections and bearing points.

The baseline values provided serve as a starting point in 

the inspection. It is important to conduct the test at several 

Figure 12.7—Technique used to measure impact-induced 

stress wave transmission times in various wood products.

Figure 12.8—Ultrasonic measurement system used to 

measure stress wave transmission times in various 

wood products.

Table 12.5—Stress wave transmission times for 

round poles treated with oil-borne preservatives 

Pole 

diameter 

(mm) 

Stress wave transmission time (μs) 

for various levels of penetration 

37 mm 61 mm Full penetration 

294 222 240 300 

343 254 271 350 

392 286 305 400 

441 321 338 450 

490 350 370 500 

539 386 403 550 

588 422 436 600 

Inspection of Timber Structures Using Stress Wave Timing NDE Tools
General Technical Report FPL–GTR–238

146

Figure 12.9—General procedures used to prepare and use 

stress wave timing methods for field work.

Table 12.6—Commercially available stress wave timing equipment

Manufacturer Product Website Location 

Agricef USLab www.agricef.com.br 

CBS-CBT Sylvatest-Duo www.cbs-cbt.com Saint-Sulpice, Switzerland 

Fakopp Enterprise Various models www.fakopp.com Agfalva, Hungary 

IML GmbH IML Micro Hammer www.imlusa.com Moultonborough, 

New Hampshire, USA 

Metriguard, Inc. Models 239A Stress 

Wave Timer 

www.metriguard.com Pullman, Washington, USA 

James Instruments, Inc. V-Meter MK IV www.ndtjames.com Chicago, Illinois, USA 

Olson Instruments, Inc. Various models www.olsoninstruments.com Wheat Ridge, Colorado, USA 

points at various distances away from the suspect area. In a 

sound member, little deviation is observed in transmission 

times. If a significant difference in values is observed, the 

member should be considered suspect.

Data Analysis and Summary Form

When data have been gathered, it is useful to present them 

in an easy-to-read manner. Figure 12.11 illustrates several 

data summaries. From these, the presence and extent of degradation can readily be seen. The top drawing illustrates the 

side view of a timber, with stress wave travel times noted 

and decay mapped. The middle and bottom illustrations 

show a timber cross section with internal defects and stress 

wave times noted. 

Literature Cited

Armstrong, J.P.; Patterson, D.W.; Sneckenberger, J.E. 1991. 

Comparison of three equations for predicting stress wave 

velocity as a function of grain angle. Wood and Fiber Science. 23(1): 32–43.

Elvery, R.H.; Nwokoye, D.N. 1970. Strength assessment of 

timber for glued laminated beams. In: Proceedings, Paper II, 

Nondestructive testing of concrete and timber, organized by 

the Institution of Civil Engineering and the British Commission for Nondestructive Testing; 1969 June 11–12. London, 

BC: Institute of Civil Engineering: 105–110.

Gerhards, C.C. 1978. Effect of earlywood and latewood on 

stress-wave measurements parallel to the grain. Wood Science. 11(2): 69–72.

Gerhards, C.C. 1980. Effect of cross grain on stress waves 

in lumber. Res. Pap. FPL–RP–368. Madison, WI: U.S. 

Department of Agriculture, Forest Service, Forest Products 

Laboratory.

Gerhards , C.C. 1982. Effect of knots on stress waves in 

lumber. Res. Pap. FPL–RP–384. Madison, WI: U.S. 

Department of Agriculture, Forest Service, Forest Products 

Laboratory.

Hoyle, R.J.; Pellerin, R.F. 1978. Stress wave inspection of a 

wood structure. In: Proceedings, 4th symposium on nondestructive testing of wood; 1978 August 28–30; Vancouver, 

WA. Pullman, WA: Washington State University: 33–45.

Ihlseng, M.C. 1878. The modulus of elasticity in some 

American woods, as determined by vibration. Van Nostrand’s Engineering Magazine. Vol. XIX: 8–9.

Ihlseng, M.C. 1879. On a mode of measuring the velocity 

of sound in wood. American Journal of Science. 3d Series. 

17I(98): 125–133.

Jung, J. 1979. Stress wave grading techniques on veneer 

sheets. Gen. Tech. Rep. FPL–GTR–27. Madison, WI: U.S. 

Department of Agriculture, Forest Service, ForestProducts 

Laboratory.

Pellerin, R.F.; De Groot, R.C.; Esenther, G.R. 1985. Nondestructive stress wave measurements of decay and termite 

attack in experimental wood units. In: Proceedings, 5th 

symposium on nondestructive testing of wood; 1985 

September 9–11; Pullman, WA. Pullman, WA: Washington 

State University: 319–353.

Chapter 12
Nondestructive Evaluation of Wood: Second Edition

147

Figure 12.10—Typical field data acquisition form.

Ross, R.J. 1982. Quality assessment of the wooden beams 

and columns of Bay C of the east end of Washington State 

University’s football stadium. Unpublished research. Pullman, WA: Washington State University.

Ross, R.J.; Ward, J.C.; TenWolde, A. 1994. Stress wave nondestructive evaluation of wetwood. Forest Products Journal. 

44(7/8): 79–83.

Rutherford, P.S. 1987. Nondestructive stress wave measurement of incipient decay in Douglas Fir. Pullman, WA: Washington State University. M.S. thesis.

Smulski, S.J. 1991. Relationship of stress wave and static 

bending determined properties of four northeastern hardwoods. Wood and Fiber Science. 23(1): 44–57.

Soltis, L; Baker. A.; Ferge, L.; [et al.]. 1992. USS Constitution—inspection and evaluation procedures for live and 

white oak members. Report submitted to U.S.Navy, Maintenance and Repair, USS Constitution, Boston Naval Shipyard, Charlestown, MA.

Volny, N.J. 1992. Timber bridge inspection case studies in 

use of stress wave velocity equipment. In: Proceedings, 8th 

symposium on nondestructive testing of wood; 1991 September 23–25; Vancouver, WA. Pullman, WA: Washington 

State University.

Inspection of Timber Structures Using Stress Wave Timing NDE Tools
General Technical Report FPL–GTR–238

148

Figure 12.11—Examples of stress wave data summary (top) and illustrations of the 

cross-section conditions.

Chapter 12
Nondestructive Evaluation of Wood: Second Edition

149

Chapter 13

Use of Laser Scanning Technology to 

Obtain As-Built Records of Historic 

Covered Bridges

Robert J. Ross, Supervisory Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

Brian K. Brashaw, Program Manager

Forest Products Marketing Unit, USDA Forest Service, Madison, Wisconsin

Samuel J. Anderson, Engineer

Natural Resources Research Institute, University of Minnesota Duluth, Minnesota

Covered bridges are part of the fabric of American history, 

and several hundred historic covered bridges still exist today. Although much effort is expended to preserve these 

structures, the high cost of restoration, neglect, vandalism, 

and arson often take their toll. Many are lost forever. One 

of the more famous bridges from The Bridges of Madison 

County movie fame was burned in 2003, and Hurricane 

Irene destroyed a number of New England bridges in 2011. 

Because we cannot completely prevent these types of incidents from occurring, the National Park Service’s Historic 

American Engineering Record (HAER) has efforts under 

way to document historic structures. Their Level I documentation is defined in the Secretary of the Interior’s Standards 

and Guidelines for Architectural and Engineering Documentation. It consists of measured and interpretive drawings, 

large-format photographs, and written historical reports. To 

assist in this effort, newer technologies need to be explored 

that can provide as-built records at a fast rate and with 

greater accuracy.

The demonstration study summarized in this chapter was 

conducted to examine the technical feasibility of using laser scanning technologies for obtaining as-built records for 

historic covered timber bridges. A secondary objective was 

to identify other applications of this technology, notably for 

other transportation structures.

This chapter was originally published in 2012 as a Forest 

Products Laboratory Research Paper:

Ross, Robert J.; Brashaw, Brian K.; Anderson, Samuel 

J. 2012. Use of laser scanning technology to obtain 

as-built records of historic covered bridges. Research 

Paper FPL–RP–669. Madison, WI: U.S. Department of 

Agriculture, Forest Service, Forest Products Laboratory. 

18 p.

Literature Review

Three-dimensional (3D) laser scanners are instruments that 

record precise and accurate surface data of objects in a nondestructive manner. These instruments use an infrared beam 

of light to calculate and record the distance to an object, 

typically as data points with spatial coordinates. These data 

are then analyzed using various types of computer software 

to generate a detailed image of coordinates and dimensions. 

Three-dimensional laser scanners have successfully been 

used to digitize objects of various sizes ranging from small 

diagnostic artifacts to large, complex sites of monumental 

architecture (FARO Technologies 2011a, b).

A wide number of companies manufacture various types 

of 3D laser scanners. Generally, these units use light detection and ranging technology (LiDAR), where laser pulses 

determine the distance to an object or surface. The distance 

to an object is determined by using time-of-flight between 

transmission of a pulse and detection of the reflected signal. 

A point cloud of data is then collected and can be converted 

into the true shape of the object.

The units used in this project, a FARO LS 880 model and a 

Photon 120 model (FARO Technologies, Lake Mary, Florida), used similar technology to acquire the scan points. A 

laser beam is emitted from a rotating mirror out toward the 

area being scanned. The laser beam is then reflected back to 

the scanner by objects in its path. The distance to the objects 

defining an area is calculated, as well as their relative vertical and horizontal angles (FARO Technologies 2011a, b).

Several bridge projects were noted in the literature review. 

The Pennsylvania Department of Transportation (PennDOT) 

completed an initial study in 1999. In this study, the goals 

were to evaluate the technology for creating as-built drawings. A comparison of traditional and 3D scanning estimated 

an overall time savings of 100+ person-hours through the 
General Technical Report FPL–GTR–238

150

use of 3D scanning (Foltz 2000). Based on this assessment, 

PennDOT purchased two laser scanners in 2000. A second 

assessment was completed by Jaselskis and others in 2003, 

showing that laser scanning could be used cost effectively 

for preliminary surveys to develop triangular network (TIN) 

meshes of roadway surfaces and to measure bridge beam 

camber more safely and quickly compared with conventional approaches. Other applications noted in this publication 

showed potential applications for laser scanning to include 

developing as-built drawings of historical structures such as 

the bridges of Madison County.

Methods

Selection of Bridges

The bridges for scanning and assessment used in this study 

are located in Wisconsin, Iowa, and Minnesota. Key background information (Table 13.1), detailed descriptions, and 

photographs are summarized in the following paragraphs. 

Contact was made with the appropriate government or 

administrative staff for each bridge, and permission was 

also secured to obtain scans. Note that four of the bridges 

scanned included the historic Madison County, Iowa, 

bridges.

Cedarburg, Wisconsin, Covered Bridge

The last remaining historic covered bridge in Wisconsin, 

the “Red Bridge,” was built in 1876. It is located just outside the city of Cedarburg, Wisconsin, in Ozaukee County 

(Figs. 13.1, 13.2). According to Ozaukee County records 

(2011),

Built in 1876, the original span measured 120’ long 

and 12’ wide. Its construction was of a certain type 

of pine found near Baraboo, Wisconsin. All of the 

timber and planks were cut and squared in a mill 

near that city. The lumber was then hauled to the 

proposed site on Cedar Creek where all pieces were 

fitted and set in place. The type of construction is 

known as lattice truss with interlacing 3 × 10 inch 

planks all held together by 2 inch hardwood pins 

and floored with three inch planking. In 1927, a 

center abutment was placed to carry the heavier 

traffic of automobiles and trucks. (Ozaukee County 

2011)

Madison County, Iowa Covered Bridges

The bridges in Madison County, Iowa, are world famous, 

and they are excellent examples of the construction of covered bridges in the 1870–1880s. As noted in 2011 on the 

Madison County, Iowa, website,

Our historic, world-famous covered bridges were 

popularized by Robert James Waller’s novel, The 

Bridges of Madison County, and the feature film 

starring Meryl Streep and Clint Eastwood. Five 

of the original covered bridges remain, all listed 

on the National Register of Historic Places. The 

bridges were covered by order of the County Board 

of Supervisors to help preserve the large flooring 

timbers that were more expensive to replace than 

the lumber covering the sides and the roof. Most of 

the construction was done by farmers to pay their 

poll taxes. The bridges were usually named for the 

nearest resident. (Chamber of Commerce 2011)

Four bridges were selected for laser scanning in Madison 

County: the Roseman, Imes, Hogback, and Cutler–Donahoe 

Bridges. Photographs of these bridges are shown in 

Figures 13.3–13.6.

Zumbrota Bridge

Minnesota’s only remaining truly historic covered bridge is 

located in the City of Zumbrota. Listed on the National Historic Register, it was originally constructed in 1869 and relocated in 1932, 1979, and finally in 1997. It currently spans 

the Zumbro River in the 65-acre Covered Bridge Park. Considered a lattice through truss design, it is now a pedestrianonly bridge that spans 120 ft. A special documentary of the 

bridge and its importance to the history of Zumbrota can be 

found at http://www.youtube.com/watch?v=EVFd1YwcHw

w&feature=related. As a measure of its importance to Zumbrota, an annual covered bridge festival occurs every June. 

A photograph of the bridge is shown in Figure 13.7.

On September 23–25, 2010, the Zumbro River flooded, 

creating a dangerous situation for the Zumbrota Covered 

Bridge. As Figure 13.8 illustrates, floodwaters rose to nearly 

the same elevation as the bottom of the bridge.

Table 13.1—Background information on the historic covered timber bridges used in this study 

State City Bridge 

Year 

built 

Span

(ft) 

Placement 

on National 

Register of 

Historic Places Design 

Wisconsin Cedarburg Red 1876 120 March 14, 1973 Lattice through truss 

Iowa Winterset Roseman 1883 106 September 1, 1976 Lattice through truss 

Iowa St. Charles Imes 1870 81 February 9, 1979 Lattice through truss 

Iowa Winterset Hogback 1884 106 August 28, 1976 Lattice through truss 

Iowa Winterset Cutler–Donahoe 1871 79 October 8, 1976 Lattice through truss 

Minnesota Zumbrota Zumbrota 1869 120 February 20, 1975 Lattice through truss 

Chapter 13
Nondestructive Evaluation of Wood: Second Edition

151

Three-Dimensional Laser Scanning 

and Data Processing

The laser scanning was completed by Sightline, LLC (Milwaukee, Wisconsin), a private firm that specializes in the 

use of laser scanning technologies with structures. Approximately 20–30 scans were completed for each bridge from 

a variety of angles using a FARO LS 880 laser scanner. 

Figure 13.9 shows a FARO laser scanner being used to 

inspect an historic covered bridge.

The scanning process consisted of scans that were completed by Sightline, and data processing was completed by 

the University of Minnesota Duluth’s Natural Resources 

Research Institute (UMD NRRI). The following steps were 

completed and an estimate of the time duration was provided for each step:

1.	 Paper “targets” are placed in numerous locations on the 

bridge for use in linking up to 30 individual laser scans 

together. Time duration: 2 person-hours.

2.	 A FARO LS 880 3D laser scanner was used to conduct 

the scan. The scanner is placed at several vantage points 

inside and outside the bridge, so that all visible surfaces 

of the bridge can be documented. Individual scans are 

completed in approximately 15–20 min. When a single 

scan is completed, it is saved to a computer as an .ls 

file. It is highly recommended that a computer with 

significant computing power and high levels of RAM 

memory be used to process the millions of data points 

created during the scanning process. Time duration: 

10 person-hours.

3.	 After all visible portions of the bridge have been 

scanned, the software files are linked using FARO 

Scene software. This software allows an individual to 

identify the targets placed prior to the scanning process 

and use them to link or attach one scan to another. It 

is also possible to filter, using various techniques or 

software, extraneous images (for instance, a vehicle 

traveling across the bridge or background vegetation). 

The process of linking two individual scans is repeated 

several times until all scans have been compiled into 

one large scan depicting the entire bridge. Time duration: 9 person-hours.

4.	 Once a bridge has been completely assembled using all of the individual scans, it can be exported as 

a point cloud, depicting all visible aspects and actual 

dimensions of the bridge. This cloud of data was then 

exported into AutoCAD software (Autodesk, Inc., San 

Rafael, California) using an add-in provided by Kubit 

USA (Houston, Texas). This add-in allows a user to import point cloud files in addition to the ones inherently 

recognized by AutoCAD 2011 and has additional modeling tools for working directly with point cloud data in 

AutoCAD. Once a point cloud has been exported into 

AutoCAD, it can be divided into multiple cross 

sections. This is done so that specific components of the 

bridge can be seen more clearly. From this point, twodimensional (2D) and 3D models of the bridge can be 

generated. Time duration: 40+ person-hours.

Results

Laser Scanning

A number of types of images can be presented from processing point cloud data. These images include a point 

cloud image resulting from only one scan, a point cloud 

image created from multiple scans, a parametric picture 

created from a point cloud scan, a point cloud image 

imbedded in AutoCAD, and 2D/3D AutoCAD images. 

These images can be created using FARO Scene software or AutoCAD 2011 with a Kubit USA add-in.

As to the project activities, the majority of the scan processing for the Cedarburg, Wisconsin, bridge was completed 

by Sightline, LLC. The processing of the scan data for the 

Madison County, Iowa, bridges and the Zumbrota, Minnesota, bridge was completed by the UMD NRRI. The project 

team decided that based on the processing time estimates, 

detailed in-point cloud and 3D AutoCAD, data would be 

provided for the Cedarburg, Imes, and Zumbrota Bridges, 

with only point cloud data for the Hogback, Cutler–

Donahoe, and Roseman Bridges. All the digital points could 

be further processed to develop detailed dimensional information, as the point cloud images are considered accurate 

data. The point cloud images could also be further processed 

using Kubit USA add-ins for AutoCAD 2011. Each bridge 

scan and AutoCAD image is shown in the following 

sections.

Cedarburg, Wisconsin, Covered Bridge

Figures 13.10–13.17 show various images created from 

3D laser scanning conducted during the project for the 

Cedarburg Bridge. This includes point cloud images and 

AutoCAD drawings created from the point cloud images. 

The Imes Bridge is the only Madison County Bridge where 

the point cloud data was used to create AutoCAD drawings. 

Our project team has all of the point cloud data that could be 

used in the future to create AutoCAD drawings for the other 

Madison County bridges (Hogback, Cutler–Donahoe, and 

Roseman) scanned during the project.

Imes Bridge, Madison County, Iowa

For the Imes Bridge, the data show the accurate dimensions 

and shape of the bridge at the time of scanning. No corrections were made to straighten any bridge members, such 

as would be done to create new construction drawings. Figures 13.18–13.23 show that the bridge is no longer perfectly 

plane, but that it has twisted and moved over time.

Use of Laser Scanning Technology to Obtain As-Built Records of Historic Covered Bridges
General Technical Report FPL–GTR–238

152

Roseman Covered Bridge, Madison County, Iowa

Figures 13.24–13.25 shows various planar views and point 

cloud images created from 3D laser scanning conducted 

during the project for the Roseman Bridge, located in Madison County, Iowa. These point images are fully dimensioned 

computer files that can be used to determine dimensions, 

skew of the bridge, deflection, and other characteristics as 

needed, without importing into AutoCAD or other software 

platforms.

Hogback Covered Bridge, Madison County, Iowa

Figure 13.26 shows various point cloud images created 

from 3D laser scanning conducted during the project for the 

Hogback Bridge located in Madison County, Iowa. These 

point images are fully dimensioned computer files that can 

be used to determine dimensions, skew of the bridge, deflection, and other characteristics as needed, without importing 

into AutoCAD or other software platforms.

Cutler–Donahoe Covered Bridge, Madison County, 

Iowa

Figures 13.27 and 13.28 show various point cloud images 

created from 3D laser scanning conducted during the project 

for the Cutler–Donahoe Bridge, located in Madison County, 

Iowa. These point images are fully dimensioned computer 

files that can be used to determine dimensions, skew of the 

bridge, deflection, and other characteristics as needed, without importing into AutoCAD or other software platforms.

Zumbrota Covered Bridge, Zumbrota, Minnesota

Additional project funds were received from the Minnesota 

Local Road Research Board’s Operational Research Program (OPERA) to conduct 3D laser scanning of the most 

significant historic covered bridge in Minnesota, located in 

Zumbrota. The bridge, built in 1869 (and relocated several 

times), is now located in a city park and spans the Zumbro 

River. Figures 13.29–13.36 show various scalar images and 

point cloud images created from 3D laser scanning conducted during the project. These figures show each different type 

of possible image that can be created using FARO Scene 

software or AutoCAD 2011 with a Kubit USA add-in. These 

images include a point cloud image resulting from only one 

scan, a scalar image created from a point cloud scan, a point 

cloud image created from multiple scans, a point cloud image imbedded in AutoCAD, and 2D/3D AutoCAD images. 

All of the scan data, images, and figures were provided 

to the City of Zumbrota for any further processing as 

appropriate.

In contrast to the Imes Bridge, significant efforts were made 

to straighten the dimensions of the bridge in AutoCAD in 

an effort to create a drawing that could be used to construct 

a new bridge. This was a significant undertaking, and added 

nearly 40 h of processing time to data processing. This process creates a drawing that would be more consistent with 

the original construction of the bridge, not its current shape 

and condition (Brashaw 2010).

Rapid Prototype Development

Following processing and development of the 3D scanning images, a digital 3D CAD file was used to generate a 

1/100th 3D scale replica of the bridge at the Northern Lights 

Technology Center of UMD NRRI. In processing the data, 

we found that it would only be possible to produce an accurate external version of the bridge. We could not create 

a true replica of the inside of the bridge because a 1/100th 

scale of a 6-in.-wide beam would only have a thickness of 

0.06 in. This is below the thickness that can be created using 

rapid prototyping, and these thin members would not have 

strength because many of the beams and members are only 

connected at the ends. The 3D image that was used by 

the Vanguard unit to create the 1/100th model shown in 

Figure 13.37 from Vanguard Si2 selective laser sintering 

(SLS) equipment (3D Systems, Valencia, California) was 

used to create the scale replica of the bridge (Fig. 13.38). 

Because the scan data were corrected to achieve straight 

members, this model would be consistent with the true nature of the bridge at construction, not its condition in 2010. 

Comparison of Laser Scanning Equipment 

Results with Field Measurements

One of the objectives of this project was to assess the accuracy of laser scanning to determine accurate dimensions 

of the bridges. The FARO laser scanner used for the project 

was rated at an accuracy of 1/4 in. at a distance of 150 ft. 

However, the typical distance of the laser scanner in this 

project ranged from 50–100 ft, improving the accuracy over 

the rated number. For this phase of the project, only the 

Madison County, Iowa, bridges were measured to verify the 

accuracy. To conduct this task, digital photos of each bridge 

were taken as soon as the project team arrived. These photos 

were then printed on a portable printer and used to record 

the accurate dimensions measured with traditional tape measures (1/16-in. accuracy). 

Each bridge was then scanned using the laser scanner at up 

to 20+ locations. The raw scan data were processed into 

point cloud data. The locations measured using traditional 

measuring equipment on the photographs were then located 

in the digital files and the digital dimensions noted. Based 

on these data, a comparison between the actual and digital 

measurements was taken. The comparison data are reported 

for each of the four Madison County bridges in the following sections. Fourteen to sixteen locations were samples 

for each bridge with a very small error noted. This mean 

error of the 14–16 measurements ranged from 0.04–0.08 in. 

(1.01–2.02 mm), within the rated accuracy of 1/4 in. at 

150 ft.

Imes Covered Bridge, Madison County, Iowa

Figure 13.39 shows the locations on the bridges where the 

actual measurements were collected using traditional tape 

measures to a resolution of 1/16 in. Once the point cloud 

was developed from the multiple laser scans of a bridge, 

Chapter 13
Nondestructive Evaluation of Wood: Second Edition

Use of Laser Scanning Technology to Obtain As-Built Records of Historic Covered Bridges 153

Table 13.2—Comparison of measurement data 

from traditional tape measures and point cloud 

measurements for the Imes Covered Bridge, 

Madison County, Iowa 

Member 

number 

Method for measurement 

Tape 

measurement

(in., to 

nearest 

1/16 in.) 

Point 

cloud Difference 

between tape

and point cloud

(in.) (mm) 

3 points

(in.) 

Mean

(in.) 

1 2.88 2.68 2.73 0.15 3.77 

 2.76 

 2.74 

2 2.88 2.84 2.77 0.10 2.58 

 2.84 

 2.64 

3 11.25 11.40 11.21 0.04 0.93 

 11.20 

 11.04 

4 5.50 5.24 5.43 0.07 1.86 

 5.40 

 5.64 

5 11.50 11.32 11.30 0.20 5.08 

 11.20 

 11.38 

6 11.50 11.40 11.48 0.02 0.51 

 11.48 

 11.56 

7 11.75 11.72 11.73 0.02 0.59 

 11.84 

 11.62 

8 11.63 11.52 11.55 0.08 1.99 

 11.52 

 11.60 

9 11.75 11.76 11.79 0.04 0.93 

 11.80 

 11.80 

10 12.13 12.24 12.03 0.10 2.50 

 12.04 

 11.80 

11 1.75 1.76 1.69 0.06 1.44 

 1.68 

 1.64 

12 11.88 11.84 11.79 0.09 2.24 

 11.72 

 11.80 

13 11.63 11.84 11.60 0.03 0.64 

 11.44 

 11.52 

14 11.75 11.88 11.80 0.05 1.27 

 11.72 

 11.80 

15 12.00 11.96 11.95 0.05 1.35 

 12.08 

 11.80 

16 1.88 1.80 1.83 0.05 1.23 

 1.80 

 1.88 

Average difference 0.07 1.83 

Table 13.3—Comparison of measurement data 

from traditional tape measures and point cloud 

measurements for the Hogback Covered 

Bridge, Madison County, Iowa 

Member 

number 

Method for measurement 

Tape 

measurement

(in., to 

nearest 

1/16 in.) 

Point 

cloud Difference 

between tape

and point cloud

(in.) (mm) 

3 points

(in.) 

Mean

(in.) 

1 8.00 7.96 8.03 0.03 0.68 

 8.04 

 8.08 

2 11.75 11.84 11.72 0.03 0.76 

 11.76 

 11.56 

3 12.00 12.16 12.07 0.07 1.69 

 12.04 

 12.00 

4 15.75 15.96 15.77 0.02 0.59 

 15.68 

 15.68 

5 11.38 11.20 11.33 0.04 1.06 

 11.28 

 11.52 

6 11.88 11.96 11.81 0.06 1.57 

 11.80 

 11.68 

7 11.63 11.60 11.59 0.04 0.97 

 11.48 

 11.68 

8 14.88 14.96 14.88 0.01 0.13 

 14.88 

 14.80 

9 11.38 11.20 11.28 0.09 2.41 

 11.40 

 11.24 

10 15.88 16.00 15.88 0.01 0.13 

 15.76 

 15.88 

11 11.50 11.40 11.51 0.01 0.17 

 11.48 

 11.64 

12 11.75 11.64 11.69 0.06 1.44 

 11.80 

 11.64 

13 12.00 11.84 11.89 0.11 2.71 

 11.84 

 12.00 

14 12.00 11.72 11.87 0.13 3.39 

 11.84 

 12.04 

Average difference 0.05 1.26 
General Technical Report FPL–GTR–238

154

the data were extracted from the digital file and simply 

compared for accuracy. Table 13.2 shows the results of this 

comparison, showing that laser scanning is a very accurate 

means of determining dimensions for use in creating as-built 

documentation.

Hogback Covered Bridge, Madison County, Iowa

Figure 13.40 shows the locations on the bridges where the 

actual measurements were collected using traditional tape 

measures to a resolution of 1/16 in. Once the point cloud 

was developed from the multiple laser scans of a bridge, 

data were extracted from the digital file and simply compared for accuracy. Table 13.3 shows the results of this comparison, showing that the laser scanning is a very accurate 

means of determining dimensions for use in creating as-built 

documentation. 

Roseman Covered Bridge, Madison County, Iowa

Figure 13.41 shows the locations on the bridges where the 

actual measurements were collected using traditional tape 

measures to a resolution of 1/16 in. Once the point cloud 

was developed from the multiple laser scans of a bridge, 

the data were extracted from the digital file and simply 

compared for accuracy. Table 13.4 shows the results of this 

comparison, showing that laser scanning is a very accurate 

means of determining dimensions for use in creating as-built 

documentation. 

Cutler–Donahoe Covered Bridge, Madison County, 

Iowa

Figure 13.42 shows the locations on the bridges where the 

actual measurements were collected using traditional tape 

measures to a resolution of 1/16 in. Once the point cloud 

was developed from the multiple laser scans of a bridge, 

data were extracted from the digital file and simply compared for accuracy. Table 13.5 shows the results of this 

comparison, showing that laser scanning is a very accurate 

means of determining dimensions for use in creating as-built 

documentation. 

Summary and Conclusions

This project successfully demonstrated the potential for using 3D laser scanning to accurately develop as-built documentation of historic covered timber bridges as a means to 

develop historical documentation. The following conclusions can be made from the project.

• Three dimensional laser scanning can be used to rapidly 

scan historic covered bridge structures. A number of 

technologies and commercial vendors are available. A 

commercial FARO scanner was used in this project and 

operated by using a phase shift technology rather than 

time of flight. This means that instead of a single pulse 

being reflected and the time of flight measured, constant 

waves of varying length are projected. Upon contact 

with an object, they are reflected back to the scanner. 

Chapter 13

Table 13.4—Comparison of measurement data 

from traditional tape measures and point cloud 

measurements for the Roseman Covered 

Bridge, Madison County, Iowa 

Member 

number 

Method for measurement 

Tape 

measurement

(in., to 

nearest 

1/16 in.) 

Point 

cloud Difference 

between tape

and point cloud 

(in.) (mm) 

3 points

(in.) 

Mean

(in.) 

1 11.63 11.76 11.62 0.01 0.13 

 11.50 

 11.60 

2 11.88 12.00 11.91 0.03 0.80 

 11.92 

 11.80 

3 11.88 11.96 11.84 0.04 0.89 

 11.72 

 11.84 

4 11.75 11.64 11.71 0.04 1.10 

 11.72 

 11.76 

5 11.88 11.76 11.79 0.09 2.24 

 11.84 

 11.76 

6 15.75 15.52 15.69 0.06 1.44 

 15.60 

 15.96 

7 11.63 11.72 11.61 0.02 0.47 

 11.50 

 11.60 

8 5.88 5.92 5.84 0.04 0.89 

 6.00 

 5.60 

9 11.50 11.52 11.47 0.03 0.85 

 11.48 

 11.40 

10 11.50 11.48 11.49 0.01 0.17 

 11.56 

 11.44 

11 11.63 11.76 11.71 0.08 2.07 

 11.72 

 11.64 

12 11.50 11.60 11.57 0.07 1.86 

 11.52 

 11.60 

13 16.00 15.96 15.97 0.03 0.68 

 15.96 

 16.00 

14 11.63 11.64 11.60 0.02 0.64 

 11.60 

 11.56 

15 11.63 11.56 11.61 0.01 0.30 

 11.68 

 11.60 

16 7.63 7.48 7.56 0.07 1.65 

 7.56 

 7.64 

Average difference 0.04 1.01 
Nondestructive Evaluation of Wood: Second Edition

155

The distance from the scanner to the object is accurately 

measured by measuring the phase shifts in the waves 

of infrared light.

• Post-processing of scan data requires experience and 

skill to cost-effectively create as-built documentation. 

The staff at Sightline, LLC, was very efficient in linking 

the 3D scans, importing them into AutoCAD and creating detailed 2D and 3D CAD drawings. Our project 

student engineer faced a steep learning curve but developed an excellent skillset in processing the files during 

the project. For experienced staff, this technology can 

reduce the time associated with creating as-built 

documentation.

• A 3D scanner can be used to create a range of outputs 

that include point cloud scans, parametric images, and 

2D and 3D AutoCAD drawings. For historic covered 

bridges, this information can be used for a variety of 

purposes including as-built documentation and structural assessment, while also providing detail on the 

land topography adjacent to the bridge.

• One of the objectives of the project was to assess the 

accuracy of laser scanning to determine accurate dimensions of the bridges. Each bridge was then scanned 

using the laser scanner at up to 20+ locations. Raw scan 

data were processed into point cloud data. The locations measured using traditional measuring equipment 

on the photographs were then located in the digital files 

and the digital dimensions noted. Based on these data, 

a comparison between the actual and digital measurements was taken. The comparison data are reported for 

each of the four Madison County bridges in the following sections. Fourteen to sixteen locations were samples 

for each bridge with a very small error noted. This 

mean error of the 14–16 measurements ranged from 

0.04–0.08 in. (1.01–2.02 mm), within the rated scanner 

accuracy of 1/4 in. at 150 ft.

Literature Cited

Brashaw, B.K. 2010. Use of laser scanning technology to 

obtain as-built records of the Zumbrota covered bridge. 

NRRI Tech. Rep. NRRI/TR-2011/04. Duluth, MN: University of Minnesota. 

Chamber of Commerce. 2011. Madison County, Iowa—the 

bridges of Madison County [Website]. Winterset, IA: Madison County Chamber of Commerce. http://madisoncounty.

com/index.php?page=the-bridges. Accessed October 6, 

2011.

FARO Technologies. 2011a. FARO laser scanner training 

[Print presentation]. Lake Mary, FL.

FARO Technologies. 2011b. Measuring arms, laser 

tracker, laser scanner. In: FARO laser scanner focus 

3D—applications. Lake Mary, FL. http://www.faro.com/

focus/applications. 

Use of Laser Scanning Technology to Obtain As-Built Records of Historic Covered Bridges

Table 13.5—Comparison of measurement data 

from traditional tape measures and point cloud 

measurements for the Cutler–Donahoe Covered 

Bridge, Madison County, Iowa 

Member 

number 

Method for measurement 

Tape 

measurement

(in., to 

nearest 

1/16 in.) 

Point 

cloud Difference 

between tape

and point cloud

(in.) (mm) 

3 points

(in.) 

Mean

(in.) 

1 11.63 11.60 11.55 0.08 1.99 

 11.64 

 11.40 

2 11.63 11.72 11.57 0.05 1.31 

 11.48 

 11.52 

3 11.75 11.60 11.71 0.04 1.10 

 11.76 

 11.76 

4 11.50 11.48 11.63 0.13 3.22 

 11.72 

 11.68 

5 1.75 1.76 1.73 0.02 0.42 

 1.64 

 1.80 

6 5.88 5.80 5.73 0.14 3.60 

 5.72 

 5.68 

7 3.75 3.68 3.76 0.01 0.25 

 3.80 

 3.80 

8 12.00 11.96 11.89 0.11 2.71 

 11.84 

 11.88 

9 11.50 11.56 11.64 0.14 3.56 

 11.56 

 11.80 

10 12.00 12.16 12.05 0.05 1.35 

 12.04 

 11.96 

11 1.63 1.64 1.64 0.01 0.38 

 1.72 

 1.56 

12 12.00 11.92 11.83 0.17 4.40 

 11.88 

 11.68 

13 5.75 5.68 5.71 0.04 1.10 

 5.72 

 5.72 

14 3.88 3.60 3.73 0.14 3.60 

 3.88 

 3.72 

Average difference 0.08 2.07 
General Technical Report FPL–GTR–238

156

Foltz, L. 2000. 3D laser scanner provides benefits for 

PennDOT bridge and rockface surveys. Professional 

Surveyor magazine. (May). http://www.profsurv.com/

magazine/article.aspx?i=590. 

Jaselskis, E.; Cackler, E.; Andrle, S.; Gao, Z. 2003. Pilot 

study on improving the efficiency of transportation project 

using laser scanning. Final Rep. CTRE Project 02-109. 

Ames, IA: Iowa State University, Center for Transportation 

Research and Education.

Ozaukee County. 2011. History of the covered bridge. 

Port Washington, WI: Ozaukee County public records. 

http://www.co.ozaukee.wi.us/history/bridge.html. 

(October 6, 2011).

Figure 13.1—Photograph of the Cedarburg Covered Bridge 

(Cedarburg, Wisconsin), circa 1934. From the Library of 

Congress.

Figure 13.2—Recent (2010) photograph of the Cedarburg 

Covered Bridge.

Figure 13.3—Roseman Bridge, Madison County, Iowa 

(2010).

Figure 13.4—Imes Bridge, Madison County, Iowa (2010).

Figure 13.5—Hogback Bridge, Madison County, Iowa 

(2010).

Figure 13.6—Cutler–Donahoe Bridge, Madison County, 

Iowa (2010).

Chapter 13
Nondestructive Evaluation of Wood: Second Edition

157

Figure 13.7—Zumbrota Bridge, Zumbrota, Minnesota 

(2011).

(a)

(b)

Figure 13.8—Zumbrota Bridge on September 23, 2010, 

during flooding on the Zumbro River. (a. Photo courtesy of 

kstp.com, b. Photo courtesy of fox9.com.).

Figure 13.9—A FARO laser scanner used to scan a historic 

covered bridge.

Figure 13.10—Point cloud image of the Cedarburg Bridge 

(SightLine, LLC).

Figure 13.11—3D AutoCAD image of the Cedarburg Bridge 

embedded in point cloud data (SightLine, LLC).

Use of Laser Scanning Technology to Obtain As-Built Records of Historic Covered Bridges
General Technical Report FPL–GTR–238

158

Figure 13.12—Dimensioned 2D AutoCAD® image of the 

Cedarburg Bridge (SightLine, LLC).

Figure 13.13—Dimensioned point cloud image of bridge 

entry on Cedarburg Bridge (SightLine, LLC).

Figure 13.14—CAD image of the Cedarburg Bridge 

(SightLine, LLC).

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Figure 13.15—CAD dimensions of ceiling structure of the 

Cedarburg Bridge (UMD NRRI).

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Figure 13.16—CAD drawing and dimensions of front 

entrance of the Cedarburg Bridge (UMD NRRI).

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Figure 13.17—CAD drawing and dimensions of side wall 

and trusses of the Cedarburg Bridge (UMD NRRI).

Chapter 13
Nondestructive Evaluation of Wood: Second Edition

159

(b)

(c)

Figure 13.18—Point cloud images of the Imes Bridge 

(UMD NRRI).

Imes

Natural Resources Research Institute

Isometeric

View

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Figure 13.19—Isometric 3D AutoCAD image of the Imes 

Bridge (NRRI).

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Figure 13.20—Dimensioned 3D AutoCAD image of the 

Imes Bridge showing the existing rotation and out-ofplane nature of the bridge (NRRI).

Imes

Natural Resources Research Institute

Side View

11'-41

4"

91'-03

4"

82'-41

2"

57'-41

4"

1'

11'-11"

10'

4'-33

4"

4'-31

4"

15'-01

2"

2'-11

4"

9"

20'-9"

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Imes

Natural Resources Research Institute

Side View

11'-41

4"

91'-03

4"

82'-41

2"

57'-41

4"

1'

11'-11"

10'

4'-33

4"

4'-31

4"

15'-01

2"

2'-11

4"

9"

20'-9"

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Figure 13.21—Dimensioned 3D AutoCAD side view image 

of the Imes Bridge (NRRI).

(a)

Use of Laser Scanning Technology to Obtain As-Built Records of Historic Covered Bridges
General Technical Report FPL–GTR–238

160

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Figure 13.22—Dimensioned 3D AutoCAD internal side wall 

image of the Imes Bridge (NRRI).

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

PRODUCED BY AN AUTODESK EDUCATIONAL PRODUCT

Figure 13.23—Dimensioned 3D AutoCAD ceiling image of 

the Imes Bridge (NRRI).

Figure 13.24—Planar views of a single laser scan of the 

Roseman Bridge (NRRI). 

(a)

(b)

Figure 13.25—Point cloud images of the Roseman Bridge 

(NRRI). 

(a)

(b)

Chapter 13
Nondestructive Evaluation of Wood: Second Edition

161

Figure 13.26—Point cloud image of the Hogback Bridge 

(NRRI).

(a)

(b)

(c)

(d)

Figure 13.27—Point cloud images of the Cutler–Donahoe 

Bridge (NRRI).

Figure 13.28—Point cloud image (enhanced with clear 

view software setting) of the Cutler–Donahoe Bridge 

(UMD NRRI).

(a)

(b)

Use of Laser Scanning Technology to Obtain As-Built Records of Historic Covered Bridges
General Technical Report FPL–GTR–238

162

Figure 29—First scalar image of the outside of the 

Zumbrota Bridge Point Cloud.

Figure 13.30—Scalar image of the outside of the Zumbrota 

Bridge created from a single exterior scan processed with 

Faro Scene software.

Figure 13.31—Scalar image of the inside of the Zumbrota 

Bridge created from a single interior scan processed with 

Faro Scene software.

(a)

(b)

(a)

(b)

Figure 13.32—Point cloud images of the Zumbrota Bridge 

created from multiple scans and linked together with Faro 

Scene software.

Chapter 13
Nondestructive Evaluation of Wood: Second Edition

163

Figure 13.33—Point cloud images of the Zumbrota Bridge 

created from multiple scans and linked together with Faro 

Scene software, imbedded into AutoCAD with a Kubit USA 

add-in.

Figure 13.34—An AutoCAD 3D image of the Zumbrota 

Bridge created from scanning data.

(a)

(b)

(a)

(b)

(c)

Figure 13.35—AutoCAD 2D images of the Zumbrota 

Bridge created from scanning data.

Use of Laser Scanning Technology to Obtain As-Built Records of Historic Covered Bridges
General Technical Report FPL–GTR–238

164

(a)

(b)

(c)

(d)

Figure 13.36—Three AutoCAD 2D images (top 3) of wall 

components used to create a composite of the Zumbrota 

bridge wall.

Figure 13.37—A 1/100th model of the Zumbrota Bridge 

created using selective laser sintering rapid prototyping 

equipment.

Figure 13.38—A 3D isometric image of the Zumbrota 

Bridge used to produce a 1/100th model using selective 

laser sintering rapid prototyping equipment. 

Figure 13.39—Locations on the Imes Bridge where 

comparison measurements were taken using traditional 

tape measures and extracted from the digital point clouds 

created from the laser scanning process.

(a)

(b)

(c)

Chapter 13
Nondestructive Evaluation of Wood: Second Edition

165

Figure 13.40—Locations on the Hogback Bridge where 

comparison measurements were taken using traditional 

tape measures and extracted from the digital point clouds 

created from the laser scanning process.

(a)

(b)

(a)

(b)

Figure 13.41—Locations on the Roseman Bridge where 

comparison measurements were taken using traditional 

tape measures and extracted from the digital point clouds 

created from the laser scanning process.

Use of Laser Scanning Technology to Obtain As-Built Records of Historic Covered Bridges
General Technical Report FPL–GTR–238

166

Figure 13.42—Locations on the Cutler–Donahoe Bridge 

where comparison measurements were taken using 

traditional tape measures and extracted from the digital 

point clouds created from the laser scanning process.

(a)

(b)

Chapter 13
Nondestructive Evaluation of Wood: Second Edition

167

Appendix

Proceedings of the International 

Nondestructive Testing and Evaluation of 

Wood Symposium Series

The International Nondestructive Testing and Evaluation of 

Wood Symposium Series was initiated by Washington State 

University (WSU) and the USDA Forest Products Laboratory (FPL). The first symposium was held at FPL in fall 1963, 

with proceedings produced and distributed in 1964. At that 

meeting, nearly 100 scientists, engineers, and industry leaders discussed the possibilities of a wide range of scientific 

means for testing wood nondestructively. The goals of 

this series of symposia were (1) to provide a technical and 

scientific forum for researchers to present and exchange 

results from their latest research endeavors and (2) to bring 

researchers and industry together in an attempt to bridge the 

gap between the results of the researchers’ efforts and use of 

those results by the wood industry.

Eighteen symposia have been held to date at sites in China, 

Germany, Hungary, Switzerland, and the United States. 

Early symposia focused on basic nondestructive evaluation (NDE) principles and efforts on lumber assessment 

procedures. The third symposium was devoted entirely to 

the machine stress rating lumber grading process. Symposia 

have attracted researchers and industry representatives from 

throughout the world. Recently, papers have been presented 

on new NDT techniques including those that focus on 

tomography, near-infrared scanning, and innovative combinations of stress wave, laser, and ultrasound techniques. 

A significant number of papers have been presented on 

assessment of trees and logs, with most work on lumber 

focusing on use of well-established techniques for evaluating species in Asia, Europe, and South America. More than 

800 technical presentations have been delivered during these 

symposia, and proceedings containing corresponding papers 

were produced and published.

In 2012, all printed proceedings from the first 17 symposia 

were assembled, digitized, and published in searchable digital format (Ross and Wang 2012). Proceedings of the 18th 

symposium were published in searchable digital format and 

made available at the symposium (Ross and Wang 2013). 

The hundreds of papers making up these two publications 

offer an unprecedented wealth of information on nondestructive testing and evaluation of wood.

Literature Cited

Ross, R.J.; Wang, X. 2012. Nondestructive testing and 

evaluation of wood—50 years of research: International 

Nondestructive Testing and Evaluation of Wood Symposium 

series. General Technical Report FPL–GTR–213. Madison, 

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

Products Laboratory. 6,702 p. 

(http://www.fpl.fs.fed.us/documnts/fplgtr/fpl_gtr213.pdf)

Ross, R.J.; Wang, X., eds. 2013. Proceedings: 18th International Nondestructive Testing and Evaluation of Wood 

Symposium. General Technical Report FPL–GTR–226. 

Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 808 p. 

(http://www.fpl.fs.fed.us/documnts/fplgtr/fpl_gtr226.pdf)