United States

Department of

Agriculture

Forest Service

Northeastern Area

State & Private

Foresty

Timber Bridge

Information

Resource Center

A Hierarchical

Analysis of Bridge

Decision Makers

Morgantown, WV

NA-TP-04-95

The Role of New Technology

Adoption in the Timber Bridge

Market: Special Project

Fiscal Year 1992
PREFACE

This publication is a technology transfer effort by the USDA Forest Service,

Timber Bridge Information Resource Center, in cooperation with the Center

for Forest Products Marketing, Department of Wood Science and Forest

Products, at Virginia Polytechnic Institute and State University, under a grant

from the USDA Forest Service.

This publication examines the process used to characterize the bridge

material selection by design engineers and local highway officials. The

Analytical Hierarchy Process is used to quantify factors that affect decision

criteria and materials selected. Strategies are recommended for promoting

timber as a bridge material.

Edward T. Cesa

Timber Bridge Information Resource Center

USDA-Forest Service

Other publications in this five-part series are:

Factors Influencing the Adoption of Timber Bridges (Literature Review),

NA-TP-02-95 (Part I)

A Perceptional Investigation into the Adoption of Timber Bridges,

NA-TP-03-95 (Part II)

Marketing Practices in the Timber Bridge Industry: 1993, NA-TP-05-95

(Part IV)

A Strategic Evaluation of Factors Affecting the Adoption of Timber

Bridges, NA-TP-06-95 (Part V)

Editor: Brenda L. Wilkins

Design: Tinathan A. Coger

Production: James H. Knotts

This document is disseminated under the sponsorship of the USDA Forest Service, Timber Bridge

Information Resource Center, in the interest of information exchange. The United States Government

assumes no liability for its contents or use thereof. This booklet does not constitute a standard,

specification, or regulation.

The United States Government does not endorse products or manufacturers. Trade and manufacturers’

names appear in this document only because they are considered essential to the object of the

document.

The United States Department of Agriculture (USDA) Forest Service is a diverse organization committed to equal

opportunity in employment and program delivery. USDA prohibits discrimination on the basis of race, color, national

origin, sex, religion, age, disability, political affiliation and familial status. Persons believing they have been discriminated against should contact the Secretary, U. S. Department of Agriculture, Washington, DC 20250, or call 202-720-

7327 (voice) or 202-720-1127 (TTY).
Timber Bridge Information Resource Center

TECHNOLOGY

TRANSFER

The Role of New Technology Adoption in the Timber

Bridge Market: Special Project Fiscal Year 1992

A Hierarchical Analysis of Bridge

Decision Makers

Prepared for:

Timber Bridge Information Resource Center

Prepared by:

Robert L. Smith, Assistant Professor/Extension Specialist

Robert J. Bush, Assistant Professor

Virginia Polytechnic Institute and State University

Center for Forest products Marketing

Department of Wood Science and Forest Products

Blacksburg, Virginia

and

Daniel Schmoldt, Research Scientist

USDA Forest Service, southeastern Forest Experiment Station

Blacksburg, Virginia

August 1995

Northeastern Area

State & Private

Forestry
TABLE OF CONTENTS

Abstract ................................................................................................................................. 1

Introduction ............................................................................................................................ 1

Timber Bridges .................................................................................................................... 2

Background ............................................................................................................................. 3

The Bridge Decision Problem ................................................................................................. 3

The Analytical Hierarchy Process (AHP) ................................................................................... 3

Methods .................................................................................................................................. 4

Data Collection ................................................................................................................... 4

States ................................................................................................................................. 6

Results .................................................................................................................................... 7

The AHP for Wisconsin Counties ........................................................................................... 7

Conclusions ............................................................................................................................. 9

Literature Cited ...................................................................................................................... 10

TABLES

1.

2.

3.

4.

5.

6.

7.

8.

9.

Criteria in Determination of Bridge Material ........................................................................... 13

States Surveyed for Important Bridge Factors .......................................................................... 13

Importance of Criteria in the Bridge Material Decision Process .................................................. 14

Geometric Mean of Paired Comparisons of Bridge Factors as

Rated by Nine Wisconsin Highway Officials ........................................................................... 15

Vector of Priorities for Wisconsin Counties ............................................................................. 15

Matrix of Paired Comparisons for Preferences of Bridge Materials

Under the Bridge Factor (Past Performance) for Wisconsin Counties ........................................... 16

Vector of Priorities for Bridge Materials under Past

Performance for Wisconsin Counties ...................................................................................... 16

Summary of AHP Models by State and Decision-Making Level ................................................... 17

Statistical Comparisons between Decision-making Groups and States ........................................... 18

FIGURES

1.

2.

3.

4.

Analytic Hierarchy Model for the Choice of a Bridge Material ................................................... 19

AHP Computation of the Choice of a Bridge Material .............................................................. 20

Decision Model for Wisconsin County Decision Makers ............................................................ 21

Choice of a Bridge Material by State and Decision Level ............................................................ 22

i
ABSTRACT

Bridge design engineers and local highway officials make bridge replacement decisions across the

United States. The Analytical Hierarchy Process was used to characterize the bridge material selection decision of these individuals. State Department of Transportation engineers, private consulting

engineers, and local highway officials were personally interviewed in Mississippi, Virginia, Washington, and Wisconsin to identify how important factors determine their choice of a bridge material.

The Analytical Hierarchy Process allowed us to quantify this subjective material selection decision

for different groups of decision makers. Based on the importance of various decision criteria and

how well the material alternatives (steel, reinforced concrete, prestressed concrete, and timber) meet

them, strategies are recommended for those promoting timber as a bridge material.

INTRODUCTION

The disciplines of Management and Marketing have evolved into separate sciences over the years.

Management is organization driven, wherein the effective manager must determine the purpose and

direction of the organization, foster and manage change, and conduct operations so that the organization and its people function efficiently and effectively (Levitt 1991). Levitt states that managers

make decisions. Decisions deal with choices. Choices involve alternatives, which include prospects

for making, avoiding, resisting, and creating change. Drucker ( 1983) states that effective management requires precise analysis, rigorous allocation of resources, and timely decision making. Managers are accountable to stockholders, financial backers, employees, and customers, so choices must be

not only the best, but also justifiable.

Marketing has been called a philosophy. It is a total system of business activities which is designed

to determine customers’ needs and desires, to plan and develop products to meet those needs and

desires, and to determine the best way to price, promote, distribute, and service the customer

(Stanton 1978). This is often referred to as the marketing concept. Sinclair (1992) states that a

marketing-oriented firm designs its products and service offerings to meet customer needs at a profit.

Marketing is the income-generating activity of the firm, the process by which the organization

reaches out to its customers, and the means by which customers reach in to the firm.

Management often utilizes decision-analysis tools to more effectively run their organizations. Marketing departments rely upon research methods involving customer surveys, purchasing activities, or

demographics to understand and meet customer needs. Yet, as Drucker ( 1984, p. 1) states, “Marketing is so basic it cannot be considered a separate function...It is the whole business seen from the

point of view of its final result, the customer. ” Following on this idea, this study crosses the boundary between modern management decision analysis and the marketing concept. This study examines

how particular criteria affect material-selection decisions for rural bridges. Quantification of this

decision process should allow manufacturers of bridge materials to improve their ability to meet

design engineers’ and highway officials’ needs.

1
Timber Bridges

Highway officials and engineers across the United States have been asked to reevaluate their position

on the use of timber as a bridge material. In 1989, the Timber Bridge Initiative (TBI) began an

extensive promotion and training program (TBI 1990) to inform and educate bridge engineers and

highway officials concerning the benefits of the modern timber bridge. It is believed that with an

increase in the use of timber, local economies can be stimulated and the rural infrastructure rebuilt.

Since its inception, the TBI has sponsored the construction of over 272 modern timber bridges in 48

states and assisted in 17 million dollars of research, educational, and bridge-supported activities

(USDA 1993). However, the long-term viability of timber bridges will depend not only upon this

technology push, but the competitiveness and acceptance in the marketplace, the market pull.

Unfortunately, bridge engineers across the United States often have negative perceptions of timber as

a bridge material. Studies by Clapp (1990) and Luppold (1990) have confirmed that engineers are

not ready to place timber in the same bridge material classification as prestressed concrete, steel, or

reinforced concrete. Engineers have stated that timber is short lived, difficult to inspect, expensive,

high in maintenance, and low in strength. Yet, numerous factors are known to affect the performance of bridge materials. Ritter (1990) states that poor design, poor construction, and poor management practices lead to performance problems with timber in bridges. Yet, little is known about

non-structural factors influencing the bridge selection process and their effect upon the final decision.

The choice of a material is the most important decision bridge designers make, and it has long-term

consequences for the owner of the structure (Johnson 1990). Bridge material selection is a complex

decision, with many individuals involved, and many factors of bridge design, use, and maintenance

to be considered. It is not uncommon to have state Department of Transportation (DOT) officials,

private consultants, and local officials work together on a bridge replacement decision. Each of

these groups may have their own preferences concerning bridge materials. Often a consensus is

necessary to determine the best material to use at a given location.

Ellen et al. (1991) indicate that humans or organizations tend to avoid change by favoring the current

situation or status quo, unless an alternative is presented which is excessively attractive or very

pressuring. Decision making tends to be based upon previous solutions and past successes, unless

they are no longer viable. With over 93% of recent bridges being built out of prestressed concrete,

reinforced concrete, or steel (FHWA 1992), decision makers appear reluctant to try timber in rural

bridge replacement.

One of the primary reasons for new product failure is inadequate market research. Three studies

undertaken over 25 years reveal that inadequate market analysis remains the primary cause of new

product failure (National Industrial Conference Board 1964, Hopkins and Bailey 1971, Hopkins

1980). Cooper (1988) states that the list of reasons for failure includes lack of thoroughness in

identifying real needs of the customer and competitors’ reactions in the marketplace. The modern

timber bridge can be classified as anew product. To effectively market this new product, the timber

bridge industry must understand the current bridge material selection process. Once this decision

process has been explained, customers’ needs can be effectively addressed.

2
Scott and Keiser (1984) state that much of the research that is done in industrial markets to identify

and evaluate new opportunities is quantitative and unstructured. We demonstrate in this study that

qualitative and structured analysis of decision makers can be a useful tool for understanding customers and their perceptions. We develop a behavioral model of bridge material selection for several

states and for several levels of decision makers.

In this study, important non-structural factors (criteria) in the bridge material selection process were

solicited from highway officials in 28 states. We used the highest rated six factors in the Analytic

Hierarchy Process (AHP) to determine the placement preference for steel, prestressed concrete,

reinforced concrete, and timber by bridge design engineers and highway officials in four selected

states. The AHP model helped us analyze how important decision criteria directly influence the

overall bridge material decision. From this developed recommendations for marketing strategies

that can be used to increase the knowledge and application of timber as a bridge material.

BACKGROUND

The Bridge Decision Problem

Many factors are known to affect the choice of a bridge material. Physical characteristics or sitespecific factors include: roadway alignment, length of clear span, clearance above waterway, hydraulic capacity requirements, and required loading capabilities. There are numerous non-structural

characteristics of bridge materials such as initial cost and maintenance (Table 1) that may also

influence this decision. These are the areas which manufacturers can address in trying to influence

the choice of bridge material by design engineers. The four principal bridge materials, which,

according to the FHWA (1992) represent 99% of the bridges built in the United States since 1982,

are prestressed concrete, steel, timber, and reinforced concrete.

The Analytical Hierarchy Process (AHP)

Although various techniques exist for modeling decision making, the AHP was chosen for this

study. The AHP can be used as both a behavioral and normative model of decision making. That is,

it can be used to model an existing decision-making process or to prescribe what decision one

should make in a particular situation. It has the advantage of utilizing qualitative, as well as quantitative data. Qualitative data is important in this research because of the necessity to determine the

underlying (i.e., subjective) reasons for the decline in timber bridges in the United States and the

slow rate of adoption of current timber bridge technology. Only by asking the people who make the

decisions can appropriate answers be discovered and strategies formulated to change the current

patterns. Qualitative inquiry cultivates the most useful of all human capacities—the capacity to

learn from others (Patton 1990).

The Analytic Hierarchy Process, developed by Thomas Saaty in the early 1970s, allows us to quantify and aggregate subjective opinions. Saaty (1980) states that the practice of decision making is

concerned with weighing alternatives which fulfill a set of desired objectives. This multi criterion,

3
multiperson model structures the decision process into a hierarchy. Through a set of pair wise

comparisons at each level of the hierarchy, a matrix can be developed, where the entities indicate the

strength with which one element dominates another with respect to a given criterion.

Harker and Vargas (1987) indicate that there are three principles used in the AHP for problem

solving: (1) decomposition - structures the elements of the problem into a hierarchy, (2) comparative judgments - generates a matrix of pair wise comparisons of all elements in a level with respect to

each related element in the level immediately above it where the principal right eigenvector of the

matrix provides ratio-scaled priority ratings for the set of elements compared, and (3) Synthesis of

priorities - generates the global or composite priority of the elements at the lowest level of the

hierarchy, i.e., the alternatives. The four basic axioms that the AHP is based upon is summarized by

Harker (1989) as follows:

Axiom 1. Given any two alternatives (or sub-criteria) i and j out of the set of alternatives A, the

decision maker is able to provide a pair wise comparison aij of these alternatives under any criterion

c from the set of criteria C on a ratio scale which is reciprocal; i.e., aji = 1 / aij for all i, j, & A.

Axiom 2. When comparing any two elements i, j, & A, the decision maker never judges one to be

infinitely better than another under any criterion c & C; i.e., aij ≠ for all i, j, & A.

Axiom 3. One can formulate the decision process as a hierarchy.

Axiom 4. All criteria and alternatives which impact the given decision problem are represented by a

hierarchy. That is, all the decision-maker’s intuition must be represented, or excluded, in terms of

criteria and alternatives in the structure and be assigned priorities which are compatible with the

intuition.

METHODS

Data Collection

Primary data

A disguised mail questionnaire was sent to over 1300 decision makers to collect primary data concerning important non-structural factors (criteria) that influence the bridge material decision. Participants were asked to assume the bridge site allowed for equal choice of material. This was meant

to eliminate physical or site-specific characteristics that may influence the material choice.

Decision makers in 28 states were classified into five distinct geographic regions (Table 2) and three

decision-making groups. The groups were state DOT engineers, private consulting engineers, and

local highway officials. Survey respondents were asked to rate 23 non-structural criteria in the

selection of a bridge material (Table 1). The questionnaire used rating scales from 1 to 7 to measure

the importance of the criteria. Criteria were selected by an extensive secondary literature search,

discussions with civil engineers across the United States, and interviews with University personnel.

4
A pretest was conducted with bridge engineers in various decision groups in Virginia, Wisconsin,

and Minnesota. After minor clarification of question wording, the questionnaire was sent out in

April 1993. No correspondence stated that the study was being conducted by the Department of

Wood Science at Virginia Tech since it was believed this may bias some results or have an undesirable effect on the response rate. After two mailings, a total of 848 surveys were returned, 751 of

which were usable, resulting in an adjusted response rate of 61%.

Non-Response - In order to test for non-response bias, 50 non-respondents were contacted by telephone and asked to answer selected questions. These individuals represented the three primary

decision-making groups. They were asked questions concerning material preference, ratings of

important bridge material factors, timber design education, and job duties. Multivariate Analysis of

Variance (MANOVA) was utilized to determine if significant differences existed between respondents and non-respondents on the selected parameters. In no case could the hypothesis of no difference between respondents and non-respondents be rejected = .05).

Personal Interviews

During August, September, and October of 1993, semi-structured interviews were conducted with

73 design engineers and highway officials in four selected states: Mississippi, Virginia, Washington, and Wisconsin. These states were chosen based on their geographic differences, timber resources, and bridge decision-making protocol. Participating in this were state department of transportation engineers involved in preliminary design or local bridge maintenance/replacement decisions, private consulting engineers involved with local bridge design, and county highway officials.

Interviews with county officials and private consultants were limited to one engineer per location.

Composite AHP models were developed for each group of decision makers in the four separate

states (Figure 1). A questionnaire was designed for participants to use for completion of the AHP

model. This questionnaire consisted of paired comparisons among the six highest ranked criteria

involved in the decision process (Table 3) as determined by the initial survey. It also included

comparisons among the different types of bridge material with respect to each criteria. A rating

scale from 1 to 7, as recommended by Saaty (1980), was used for the paired comparisons. The

Number 1 value indicated that compared factors were equal in importance and Number 7 indicated

that one factor was extremely more important than another. This questionnaire was reviewed by

qualified personnel at Virginia Tech and pretested with private consultants and state DOT engineers

in Virginia.

Each decision maker made 51 paired comparisons to complete their individual AHP model. The

computer program, Expert Choice (1992), assisted in development and analysis of the models. A

lap-top computer was used to input the data to Expert Choice as each official responded to the

questionnaire. This allowed immediate feedback to the decision maker on his/her preferences and

overall choice of a bridge material. Individual results were then combined as geometric means to

produce group decisions representing the separate decision-making groups in each state.

5
The balance of the interview was exploratory in nature. Responses were recorded for interpretation

and analysis. Specific areas of interest included: bridge costs, best locations for timber bridges,

concerns with timber as a bridge material, guidelines on timber use, amount of bridge work in state,

best material for short span bridges, reasons the state doesn’t use more timber in bridges, bidding

processes within the state, and factors that would allow the state to use more timber bridges.

States

Mississippi - This state is located in the heart of the southern pine resource and is one of the states

having the highest number of timber bridges (more than 3,500) (FHWA 1993). Design decision

makers in Mississippi include state DOT and county engineers. The county engineer is a private

consultant hired by the county board of supervisors for a 4-year term. This consultant sometimes

serves as many as five different counties. All bridges utilizing Federal Highway bridge replacement

funds or state funds must be designed by the county engineer. The Mississippi DOT, which administers funding and reviews bridge plans, is divided into two sections: (1) the secondary roads division which directs the local roads program and (2) the DOT which directs state and federal highway

programs. Both divisions are strong supporters of standardized bridge plans, which at the time of the

interviews did not include plans for timber. More than 70% of the nearly 12,000 state bridges fall

under local/county jurisdiction (USDA 1989).

Virginia - This Mid-Atlantic state has a large eastern hardwood and southern pine timber resource

base and is one of the states with the lowest number of timber bridges (less than 60) (FHWA 1993).

The state DOT maintains more than 97% of the state’s bridges. Virginia is divided into nine highway districts, with a chief bridge engineer directing maintenance and replacement activities within

each district. Private consultants are used occasionally when the work load is too great for the

district engineers to handle. Virginia utilizes standard bridge plans that do not include complete

plans for timber bridges. Temporary structures and timber plank on steel stringers are the only

standard type plans available.

Washington - Located in the Pacific-Northwest, Washington has a large softwood timber resource.

Yet, only 600 of the state’s nearly 7,000 bridges are timber. Three decision-making groups are

involved in bridge replacement in Washington. The state DOT has a local program engineer who

works with counties on bridge replacements, and a staff of engineers in the central office that design

state and federal highway bridges. Private consulting engineers are often hired by counties to design

their rural bridges. Each county in Washington is required to have a registered civil engineer on staff

to oversee local highway maintenance. This engineer or his/her assistant will often design a rural

bridge. Sixty-five percent of the state’s bridges fall under local control. Washington utilizes standard plans; however, the only plans for timber are for temporary structures, such as detours.

Wisconsin - Located in the upper Midwest, Wisconsin is one of only five states that has shown an

increase in timber bridges from 1986 to 1992 (FHWA 1992). Over 500 of the state’s nearly 12,000

bridges are classified as timber. Three groups of decision makers are involved in design decisions in

Wisconsin. The state Department of Transportation (DOT) is divided into eight highway districts,

each with a bridge engineer that works with counties on maintenance and replacement. Private

consultants are hired by counties to design rural bridges. County highway commissioners are re6
RESULTS

sponsible for maintenance of local, state, and Federal highways within their county boundaries. The

county highway commissioner does not have to be an engineer, but the trend is to hire engineers in

that position. The commissioner, in most cases, is appointed by the board of supervisors for a 2- or

4-year term. Wisconsin has standard bridge plans that do include plans for timber bridges.

The most important non-structural factors (criteria) rated by all decision-making groups across every

region of the United States include: expected life of material, material’s past performance, maintenance requirements, resistance to natural deterioration, initial cost, and life-cycle cost of material

(Table 3). Six criteria were chosen because of their importance, statistical significance from the

remaining factors (p < .01), and to keep the number of paired comparisons (51) for the respondents

to a minimum. These decision criteria are areas in which timber manufacturers need to address their

efforts to promote timber bridges more successfully. All six criteria were used in the models subsequently developed using the AHP.

To determine if the four selected states (Mississippi, Virginia, Washington, and Wisconsin) were

representative of their respective geographic regions, a Multivariate Analysis of Variance

(MANOVA) was run on the selected criteria between the individual state and its region. No significant difference = .05) between each state and its region on these six factors was apparent. Analysis

of Variance was used to determine if the states differed from others in the respective regions based

on perceptions of timber as a bridge material. Again, no statistical differences could be shown.

These results indicate that each state is representative of the region in which it is located and should

provide a good indicator of bridge decision making in that region.

The AHP for Wisconsin Counties

To demonstrate how an AHP model is developed, an example based on county decision makers in

Wisconsin is provided. In August 1993, nine county highway commissioners/engineers agreed to

participate in completing the paired comparison questionnaire. The counties were geographically

dispersed across Wisconsin, and respondents were either county engineers or county highway commissioners. The purpose of the interview was explained and as the official filled out the questionnaire, the responses were entered into a personal computer using the program, Expert Choice. First,

paired comparisons were made among the six important bridge criteria. Under each criteria, paired

comparisons were made for preferences of bridge materials. Exploratory questions regarding bridge

replacement decisions were discussed at that time. At the completion of the nine interviews, individual results were geometrically averaged, and one composite matrix was developed (Table 4)

representing county decision makers in Wisconsin.

Calculation of a final priority vector for bridge material preference proceeds in the following way.

First, the data in the bridge criteria matrix are normalized by column. Second, the values in each row

are averaged to produce a vector of priorities for each bridge criterion (Table 5). Third, similar

calculations are then repeated for each matrix of material preference under a given bridge criterion

7
(Tables 6-7). Upon completion of these steps, the final composite preference vector for bridge

material is the matrix product of (1) the matrix composed of bridge material preference vectors and

(2) the vector of bridge criteria (Figure 2). This is the choice of bridge material for the decision

maker (in this case, county highway commissioners/engineers in Wisconsin) based upon the criteria

measured (Figure 3).

This process was repeated with engineers and highway officials in the four selected states. Composite models were developed for each group in each state. Overall material decisions were calculated

for each decision maker by state (Figure 4). Expert Choice also calculates an inconsistency ratio,

which is a measure of how consistent a respondent is with the paired comparisons. That is, for

comparisons among entities A, B, and C, the preference of A over C should equal the product of the

preference of A over B and the preference of B over C, for the judgments to be consistent. Saaty

(1980) states that an inconsistency ratio of less than 0.1 is excellent. Nevertheless, some inconsistency is inherent in most decision processes and should not necessarily be eliminated. The inconsistency ratios for aggregate responses of these decision-maker groups were all much less than 0.1.

Table 8 summarizes the results of each state’s models.

Individual decision models can be combined arithmetically to perform statistical analysis (Saaty

1993). To determine if differences existed between states or decision-making groups, non-parametric statistical procedures were utilized. Non-parametric procedures are recommended when sample

size is small or the distribution of the population from which the data is obtained is uncertain (Hollander and Wolfe 1973). The importance of the six major criteria in the bridge decision are quite

uniform across decision-making groups and between states (Table 9). Only for the criteria of maintenance did significant differences < .05) exist between the four states. This agrees with earlier

findings by the authors that major criteria are similar by groups and regions.

Among the three major decision groups (DOT, private engineers, and local officials) aggregated

across the four states, differences existed in the choices of steel and timber. Among the four states

and the three decision groups, only reinforced concrete was not statistically different. In the states of

Virginia and Wisconsin, differences existed between decision makers’ preferences for timber. Both

prestressed concrete and reinforced concrete were deemed to have different preferences across

decision groups in Mississippi. Only in Washington were the preferences for bridge materials not

statistically different by decision group. These results indicate that even though decision criteria are

viewed similarly, the extent to which various bridge materials are perceived as meeting those criteria

varies between states and between decision-making groups.

Sensitivity analysis was run on each model’s bridge decision criteria to determine if increasing

efforts in one or more areas would affect the bridge decision. Department of Transportation engineers favored prestressed concrete. This may be attributed to their exposure to state and federal

highway bridges and a lack of familiarity with timber design. Private consultants and county officials favored prestressed and reinforced concrete for rural bridges.

In Mississippi, only if initial cost became extremely important would county engineers consider

using timber instead of steel in their decisions. No changes would affect the Mississippi DOT

engineers’ decisions concerning timber. Virginia private consultants would choose timber above all

other materials if initial cost became very important. Nothing would affect the decision of DOT

8
engineers in Virginia. In Washington, as initial cost became more important, local engineers and

private consultants favored timber over steel, but never over concrete. Again, no changes would

affect the decision of Washington DOT engineers. Wisconsin local engineers would prefer timber

as initial cost became very important, and DOT engineers favored timber over steel when maintenance became increasingly important. Nothing affected Wisconsin private consultants’ decision.

CONCLUSIONS

Decision-making applications of this research indicate that the Analytic Hierarchy Process can be

utilized in a group situation to assist highway officials in their choice of a bridge material. This

model reflects the current bridge situation in the United States, with prestressed and reinforced

concrete being the major bridge material chosen over seventy percent of the time by highway

officials.

Decision makers are in agreement about criteria that are important in the design decision from the

list provided. Across the United States, these individuals rated the most important criteria similarly

by region and decision group. Maintenance requirements, initial cost, and past performance were

the most influential criteria in choosing a bridge material. However, these criteria, when applied to

the AHP decision models, influenced the choice of bridge material differently. Nevertheless, prestressed concrete and reinforced concrete were the materials of choice by every group in each state.

These results indicate that initial cost may be a competitive advantage for timber in bridge design.

However, timber is rated so low, based upon the other five criteria, that it will very seldom be

chosen as a rural bridge material. AS little can be done with the criteria of past performance of a

bridge material, educational efforts are needed emphasizing that modern designed timber bridges are

not the same as timber bridges built 40 to 50 years ago. Modern prestressed composites of steel and

timber can perform as well, if not better, than other materials. In addressing the criteria of maintenance, modern composites of steel and wood should reduce deflection and movement in timber

bridges, which may have caused many of the past problems. Resistance to natural deterioration can

be improved by building structures with water-shedding joints, good preservative treatments, and

waterproof surfaces. Stressed-type timber bridge systems should reduce the amount of water movement between wood members. Realistic comparisons of all bridge materials need to be made based

on past design and construction practices. Concrete and steel structures may be performing better,

because more of them have been built to modern standards than timber. Life span and life-cycle cost

will both improve as timber lasts longer and becomes more competitive in the marketplace.

During interviews, questions were also asked about the problems with timber. In Mississippi,

Virginia, and Washington a primary concern was life span. Engineers in each state indicated that

treated timber is being replaced after 25 to 30 years in service. Initial cost of timber was a factor in

most states. Timber is not perceived as cost competitive. Cost of timber, therefore, cannot influence the decision over other bridge materials. Because timber decays from the inside to the outside,

inspection is more difficult for untrained engineers. This also increases the risk of using timber in

bridge design as perceived by the highway official. The maintenance requirements of timber com9
pared to the other materials were seen as a deterrent to its use. Environmental concerns with wood

preservatives and the timber resource supply were raised by Washington and Mississippi highway

officials.

With state DOT engineers controlling the allocation of Federal highway funds, efforts must be made

to convince the opinion leaders in this group that timber is a viable bridge material. Since this group

chose timber the least in the bridge decision, every effort is needed to demonstrate the attributes of

timber used in modern designs for rural bridges. To improve timber’s perception by engineers,

manufacturers need to address timber’s short life span and maintenance requirements in a bridge

situation.

Marketing applications of this work indicate that timber manufacturers may need to address other

criteria besides those measured in this study to increase timber’s market share. Other important

bridge criteria that timber may compete on include ease of repair, time of traffic interruption, resistance to deicing chemicals, and aesthetics. Rural roads with county control offer the greatest opportunity for timber use, since individuals responsible for making these bridge decisions choose timber

more often than DOT engineers. Manufacturers may want to look at other areas in which timber

may be successful. Railroads, footbridges, light traffic bridges, and scenic covered bridges may offer

further opportunities for timber in bridge applications.

LITERATURE CITED

Clapp, V., 1990. “Timber Bridges in the Real World,” Wood Design Focus, Fall: 19-20.

Cooper, R., 1988. “Predevelopment Activities Determine New Product Success,” Industrial

Marketing Management 17:237-247.

Day, R. and Herbig, P., 1990. “How the Diffusion of Industrial Innovations is Different from New

Retail Products,” Industrial Marketing Management, 19:261-266.

Drucker, P., 1984. Marketing Management, 5th Edition, Englewood Cliffs, NJ: Prentice Hall, p. 1.

Drucker, P., 1983. “Managing for Business Effectiveness,” Strategic Management, New York, NY,

John Wiley and Sons Inc., pp. 64-77.

Ellen, P., Bearden, W., and Sharma, S., 1991. “Resistance to Technological Innovations: An

Examination of the Role of Self-Efficacy and Performance Satisfaction,” Journal of

Marketing Research, Vol. 19(4):297-307.

Expert Choice, 1992. Decision Support Software, Expert Choice Inc., Pittsburgh, PA, Version 8.

10
FHWA, Federal Highway Administration, 1992. “National Bridge Inventory Data,” U.S. Department

of Transportation, Federal Highway Administration.

FHWA, Federal Highway Administration, 1993. “National Bridge Inventory Data,” U.S. Department

of Transportation, Federal Highway Administration.

Harker, P. and Vargas, L., 1987. “The Theory of Ratio Scaled Estimation: Saaty’s Analytic

Hierarchy Process,” Management Science, Vol.33 (11):1383-1403.

Harker, P., 1989. “The Art and Science of Decision Making: The Analytic Hierarchy Process,” The

Analytic Hierarchy Process: Applications and Studies, New York, NY: Springer-Verlag, pp.

2-28.

Hollander, M., and Wolfe, D., 1973. Nonparametric Statistics, New York, NY: John Wiley and

Sons Inc.

Hopkins, D.S. and Bailey, E. L., 1971. New Pressures, Conference Board Record 8, pp. 16-24.

Hopkins, D. L., 1980. New Products Winners and Losers, Report #773, The Conference Board.

Johnson, K., 1990. Timber Bridge Design, Engineering and Construction Manual produced by

Wheeler Consolidated, St. Louis Park, MN. 4th edition.

Levitt, T., 1991. Thinking About Management, New York, NY: The Free Press.

Luppold, H.M. and Associates, 1990. Southern Pine Usage and Timber Bridge Status of Ten

NCIB,

Southeastern State Highway Departments, Holly Hill, SC.

National Industrial Conference Board, 1964. Why New Products Fail, The National

Industrial Conference Board Record, New York.

Patton, M., 1990. Qualitative Evaluation and Research Methods, Sage Publications, 2nd edition,

Newbury Park.

Ritter, M., 1990. Timber Bridges: Design, Construction, Inspection, and Maintenance, Washington

DC., p. 13-1.

Saaty, J., 1993. Expert Choice: Decision Support Software User Manual, Expert Choice Inc.,

Pittsburgh, PA, Version 8, p. 90.

Saaty, T., 1980. The Analytic Hierarchy Process, New York, NY: McGraw Hill.

Scott, J. and Keiser, S., 1984. “Forecasting Acceptance of New Industrial Products with Judgment

Modeling,” Journal of Marketing, 48, (Spring) :54-67.

Sinclair, S., 1992. Forest Products Marketing, New York, NY: McGraw Hill, Inc., 403p.

11
Stanton, W. J., 1978. Fundamentals of Marketing, New York, NY: McGraw-Hill.

TBI, Timber Bridge Initiative, 1990. Crossings Newsletter, sponsored by the Timber Bridge

Information Resource Center, Morgantown, WV.

USDA, United States Department of Agriculture, 1993. The National Timber Bridge Initiative - A

Status Report, U.S. Dept. of Agriculture, Forest Service, Washington, DC.

USDA, United States Department of Agriculture, 1989. “Rural Bridges: An Assessment Based

upon the National Bridge Inventory,” Office of Transportation, Transportation Report,

Washington DC.

12
Table 1. Criteria in Determination of Bridge Material

Government research

efforts

Life-cycle cost of

material

Resistance to natural

deterioration

Expected life of material

Length of traffic

interruption

Maintenance requirements

Initial cost of material

Bridge loading variations

Standards specified

by AASHTO

Past performance of the

material in bridges

Contractor’s familiarity

with material

Bridge ownership

(state, county, town)

Designers familiarity

with material

Industrial promotional

efforts

Aesthetics

Daily traffic count

Material preference

of local officials

Availability of design

information

Resistance to

de-icing chemicals

Regular inspection

requirements

Impact on local economy

Environmental

considerations

Ease of repair

Table 2. States Surveyed for Important Bridge Factors

West South Mid-Atlantic Northeast Midwest

California Alabama Kentucky Maine Indiana

Idaho Arkansas North Carolina Massachusetts Illinois

Montana Florida Tennessee New York Iowa

Oregon Louisiana Virginia Pennsylvania Michigan

Washington Mississippi West Virginia Vermont Minnesota

Texas Ohio

Wisconsin

13
Table 3. Importance of Criteria in the Bridge Material Decision Process

DECISION LEVEL

(Mean Rating)

Bridge Material Factor

Life span (1)

Past Performance (2)

Maintenance (3)

Natural

Deterioration (4)

Initial Cost (5)

Life-cycle Cost (6)

Ease of repair (7)

AASHTO (8)

Time of Traffic (9)

Designer’s

Familiarity (10)

Design Information (11 )

De-icing Chemicals (12)

Environmental

Concerns (13)

Inspection

Requirements (14)

Loading Variations (15)

Contractors

Familiarity (16)

Daily Traffic (16)

Aesthetics (18)

Local Officials (19)

Local Economy (20)

Bridge Ownership (21 )

Gov. Research (22)

Promotional

Efforts (23)

Overall

5.95

5.92

5.84

5.82

5.54

5.51

5.25

5.24

5.08

4.86

4.85

4.84

4.66

4.65

4.56

4.41

4.41

4.34

4.23

4.11

3.98

3.82

2.81

Local

6.17

5.93

5.98

5.92

5.60

5.62

5.41

5.15

4.98

4.91

4.92

4.38

4.74

4.68

5.05

4.61

4.58

4.20

4.16

4.59

4.07

3.76

2.88

State

5.89

5.98

5.85

5.72

5.48

5.45

5.19

5.14

5.26

4.70

4.69

5.03

4.68

4.66

4.34

4.16

4.41

4.27

3.71

3.80

3.72

3.85

2.76

Private

5.82

5.83

5.67

5.82

5.49

5.51

5.16

5.42

5.01

4.92

4.92

5.05

4.53

4.62

4.38

4.47

4.24

4.51

5.01

4.07

4.24

3.74

2.76

Rating Scale: 1 (below average) to 7 (above average), average = 4

14
Table 4. Geometric Mean of Paired Comparisons of Bridge Factors as Rated by Nine Wisconsin

Highway Officials

Past perf Life span Maintence Resistance Initial Life cycle

Past perf 1.0 1.10 .71 1.0 .53 1.0

Life span .91 1.0 .71 1.4 .83 1.5

Maintence 1 . 4 1.4 1.0 1.7 1.3 1.6

Resistance 1 . 0 .71 .59 1.0 .67 .40

Initial 1.9 1.2 .77 1.3 1.0 1.2

Life cycle 1.0 .67 .63 2.5 .83 1.0

Total 7.21 6.08 4.41 8.90 5.16 6.70

Normalized Matrix of Paired Comparisons for Wisconsin Counties

Past perf .139 .181 .161 .112 .103 .149

Life span .126 .164 .161 .157 .161 .224

Maintence .194 .230 .227 .191 .252 .239

Resistance .139 .117 .134 .112 .130 .06

Initial .264 .197 .175 .146 .194 .179

Life cycle .139 .110 .143 .281 .161 .149

Table 5. Vector of Priorities for Wisconsin Counties

Total of

Normalized Row

Past performance .842

Life span .993

Maintence 1.33

Resistance .692

Initial 1.16

Lifecycle .983

Average of Vector of Priorities

Normalized Row

.842/6 .140

.993/6 .166

1.33/6 .222

.692/6 .115

1.16/6 .193

.983/6 .164

15
Table 6. Matrix of Paired Comparisons for Preferences of Bridge Materials Under the Bridge

Factor (Past Performance) for Wisconsin Counties

.20

.71

4.9 1.4

Prestressed Steel Timber Reinforced

Concrete Concrete

Prestressed

Concrete 1.0 .71

Steel

Timber

Reinforced

Concrete 1.4 4.1 1.8 1.0

1.0

1.8

.56

1.0

.24

.56

Table 7. Vector of Priorities for Bridge Materials under Past Performance for Wisconsin Counties

Total of Normalized Row Vector of Priorities

Prestressed Concrete 1.29 .325

Steel .35 .089

Timber .80 .202

Reinforced Concrete 1.55 .384

16
Table 8. Summary of AHP Models by State and Decision-Making Level

State

All states in study

State DOT

Private

Engineers

County

Engineers

Mississippi

State DOT

County

Engineers

Virginia

State DOT

Private

Engineers

Washington

State DOT

Private

Engineers

County

Engineers

Wisconsin

State DOT

Private

Engineers

County

Commissioners

Legend

Samp. Incon.

Size

29

20

24

5

8

12

7

4

7

7

8

6

9

Ratio PRE STL TMB REF PP LS MN RS IC LC

.01

.01

.O1

.05

.04

.01

.03

.03

.04

.05

.02

.02

.02

Incon.Ratio - Inconsistency Ratio

IC - initial Cost

LS - Life span

LC - Life cycle Cost

MN - Maintenance Requirements

.442 .154 .073

.383 .147 .122

.397 .125 .116

.527 .150 .048

.370 .141 .076

.333 .204 .090

.326 .263 .145

.496 .134 .069

.466 .128 .080

.491 .112 .074

.406 .125 .098

.335 .086 .130

.311 .112 .260

.331 .164 .170 .201 .164 .129 .171

.348 .191 .138 .215 .153 .139 .165

.362 .127 .170 .202 .152 .181 .168

.275 .123 .194 .218 .160 .170 .135

.413 .145 .186 .167 .190 .191 .122

.374 .171 .153 .266 .157 .093 .160

.266 .239 .115 .262 .112 .082 .190

.301 .184 .149 .166 .144 .163 .194

.326 .134 .119 .231 .212 .126 .178

.324 .093 .164 .208 .164 .143 .228

.371 .179 .177 .165 .183 .102 .194

.449 .199 .171 .147 .141 .223 .119

.316 .140 .166 .221 .114 .195 .163

PP - Past Performance

PRE - Prestressed Concrete

REF - Reinforced Concrete

RS - Resistance to Natural Deterioration

STL - Steel

TMB - Timber

17
Table 9. Statistical Comparisons between Decision-making Groups and States

Kruskal-Wallis Paired Sample or Oneway ANOVA P-Values

Comparison Decision States 2 Decision- Decision- Decision- Decision-

-Groups 1 Groups Groups Groups Groups

within within within within

Criteria Missis- Virginia Wash- Wisconsin

sippi ington

Past performance .09 .10 .88 .08 .63 .67

Life span .09 .29 .88 .44 .39 .74

Maintenance .59 .05 .56 .86 .79 .67

Resistance to

natural

deterioration .68 .90 l.O .61 .63 .27

Initial cost .60 .23 1.0 .93 .86 .08

Life-cycle cost .56 .08 .66 .55 .69 .42

Material Preference

Prestressed

concrete .86 .00 .03 .80 .42 .43

Reinforced

concrete .88 .47 .03 .18 .74 .06

Steel .01 .00 .24 .20 .80 .08

Timber .07 .00 .38 .04 .92 .00

1. Comparison between 3 decision-maker groups: state DOT, private engineers, and local

officials

2. Comparison between 4 states’ decision makers: Mississippi, Virginia, Washington and

Wisconsin.

18
AHP Model of

the Bridge Decision

Over-all Decision

Figure 1. Analytic Hierarchy Model for the Choice of a Bridge Material

19
AHP Computation of Final Preference

Vector for the Bridge Decision

Figure 2. AHP Computation of the Choice of a Bridge Material

20
Final Decision Model for

Wisconsin Counties

Prestress

Steel

Timber

Reinforced

.325

.089

.202

.384

.269

.158

.233

.340

Goal

.418

.081

.227

.274

.315

.095

.261

.329

.233

.151

.324

.292

.283

.092

.311

.314

Figure 3. Decision Model for Wisconsin County Decision Makers

21
AHP Estimation of Preferences for Bridge Materials

Mississippi Counties

Mississippi DOT

Virginia Consultants

Virginia DOT

Washington Counties

Washington Consultants

Washington DOT

Wisconsin Counties

Wisconsin Consultants

Wisconsin DOT

Vector of Priorities, Scale 0 to 1

Figure 4. Choice of a Bridge Material by State and Decision Level

22
For more information contact:

Program Director

USDA Forest Service

Timber Bridge Information Resource Center

180 Canfield Street

Morgantown, WV 26505

Phone: 304-285-1651