Development of a 

Smart Timber Bridge—

A Five-Year Plan 

Brent M. Phares

Terry J. Wipf

Ursula Deza

James P. Wacker

United States

Department of

Agriculture

Forest Service

Forest

Products

Laboratory

General

Technical

Report

FPL–GTR–195
Abstract

This paper outlines a 5-year research plan for the development of a structural health monitoring system for timber 

bridges. A series of studies identify and evaluate various 

sensing technologies for measurement of structural adequacy and/or deterioration parameters. The overall goal is 

to develop a turn-key system to analyze, monitor, and report 

on the performance and condition of timber bridges. The 

introduction of structural health monitoring technologies 

for timber bridges should result in improved safety, longer 

service life, and improved load ratings.

Keywords: structural health monitoring, deterioration, 

long-term performance, timber bridge, glulam, sensors 

SI conversion factors

Inch–pound unit Conversion factor SI unit

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

Contents

Background...........................................................................1

Conceptual Smart Timber Bridge .........................................1

 Conceptual Structural System...........................................1

 Measurement Attributes of the Smart Timber Bridge.......2

 Anticipated Sensor Types To Be Used..............................4

 Communication and Reporting.........................................4

Problem Statements ..............................................................4

 Development of FBG Sensors to Detect Moisture 

 Content in Timber.............................................................4

 Development of FBG Sensors to Detect Ferric 

 Ions....................................................................................5

 Development of Sensors to Detect Degradation in 

 Wood Lignin .....................................................................5

 Evaluation of Techniques for Embedding and 

 Attaching FBG Sensors to New Glulam Bridge 

 Members ...........................................................................5

 Development of Data Processing Techniques for 

 Determining Structural Adequacy Parameters..................6

 Development of Data Processing Techniques for 

 Determining Changes in Structural Stiffness....................7

 Development of Data Processing Techniques to 

 Determine Vehicle Characteristics....................................7

 Development of Smart Timber Bridge Software 

 Application........................................................................7

 Demonstration of the Smart Timber Bridge......................8

Concluding Remarks.............................................................8

Literature Cited .....................................................................8

Other References...................................................................9

March 2011

Phares, Brent M.; Wipf, Terry J.; Deza, Ursula; Wacker, James P. 2011. 

Development of a smart timber bridge—A five-year plan. General Technical Report FPL-GTR-195. Madison, WI: U.S. Department of Agriculture, 

Forest Service, Forest Products Laboratory. 10 p.

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and employer.
Development of a Smart Timber Bridge—

A Five-Year Plan

Brent M. Phares, Associate Manager

Terry J. Wipf, Director

Ursula Deza, Graduate Student

Bridge Engineering Center, Institute for Transportation—Iowa State University, Ames, Iowa

James P. Wacker, Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

Background

The critical deterioration of bridges nationwide has initiated 

the search for new methods to rehabilitate, repair, manage, 

and construct bridges. In this context, smart structures have 

recently emerged as a new technology to help improve future management. This approach can be thought of as a 

potential replacement for, or complementary to, on-site 

inspections currently specified by the National Bridge Inspection Program. In general, research on smart structures 

involves materials, structural mechanics, electronics, signal 

processing, communication, and control (ISIS 2001, Bruhwiler 2003). In practice, a smart structure would typically 

incorporate the use of sensors, data reduction techniques, 

and remote systems that allow for monitoring of the structure (Maalej and others 2002, Muthumani and Sreekala 

2003, Koh and others 2003). With these elements, the smart 

structure is able to monitor the in situ behavior of the structure, to assess its performance under service loads, to detect 

damage and deterioration, and to determine its current condition (Udd and others 2000, Li and others 2004).

To date, fiber optic sensors (FOSs) have been used in concrete and steel structures (Merzbacher and others 1996, 

Doornink and others 2005, Kim and Cho 2004, Ansari 2005, 

Sumant and Maiti 2005). Fiber Bragg Gratings (FBGs), a 

type of diffraction grating of an optical fiber that filters out 

particular wavelengths of light, have many advantages over 

other FOS types. Besides being absolute, linear in response, 

interrupt immune, and readily multiplexed, these sensors 

have the ability to be both embedded and surface mounted. 

In laboratory and field tests, good agreement with foil strain 

gages has been demonstrated in both steel and concrete 

structures.

In recent research efforts involving existing steel and concrete bridges, health monitoring systems have been applied 

with the purpose of detecting early damage and deterioration and prolonging service life. Health monitoring systems 

have also been applied in newly constructed bridges with 

the goal of reducing the total life maintenance cost and 

improving bridge management (Koh and others 2003). The 

purpose of using structural health monitoring (SHM) systems is to provide real-time monitoring of various structural 

and serviceability changes through measurement metrics 

such as strain and temperature, among others. In the case 

of bridge structures, the data provided by these sensors are 

usually transmitted to remote data acquisition centers. Thus, 

an active structural control based on the collected information is possible.

In the past, timber bridge evaluation and maintenance efforts have principally focused on the internal integrity of 

timber components and improving day-to-day performance 

(Ritter 1992, FPL 1999, Kainz and others 2001). The goal 

of the research plan that is the subject of this report is to develop a smart timber bridge that improves the long-term performance, maintenance, and management of timber bridges 

through the development of smart timber bridge concepts. 

The development of the smart timber bridge will utilize current and new sensors, health monitoring technologies, and 

bridge management approaches that will be integrated in 

one turn-key SHM system. Thus, the work described herein 

is to develop a system to monitor, analyze, and report on 

the performance and condition of the most commonly constructed timber bridge. This work will be accomplished over 

a 5-year period.

Conceptual Smart Timber Bridge

Conceptual Structural System

Not surprisingly, the material used in the smart timber 

bridge will be glued-laminated timber (glulam) members 

(Ritter 1992, FPL 1999, Russell 1997). In contrast to the 

variable range of solid wood, glulam is an engineered wood 

product that provides distinct advantages over solid-sawn 

timber. Glulam consists of (2-in. nominal) lumber laminations that are bonded together on their wide faces with 

structural adhesive. Fundamentally, glulam members can be 

manufactured from any softwood or hardwood lumber. In 

the United States, glulam has been applied successfully in 

buildings and bridges since the 1930s.

The superstructure of the conceptual bridge is composed 

of a series of transverse glulam deck panels supported by 

longitudinal glulam girders. In general, these superstructures 

can span 20 to 80 ft. Based on the Standard Plans for Timber Bridge Superstructures (Wacker and Smith 2001), the 
General Technical Report FPL–GTR–195

2

baseline smart timber bridge considered during development 

will be a simply supported 60-ft-long span with a 24-ft roadway width with zero skew supported on concrete abutments.

The baseline superstructure system consists of three primary 

components: seven girders spaced at 44 in., four sets of diaphragms spaced at 18 ft, and bearings (Figure 1). The girders will be assumed to be Southern Pine 24F-V3 designed 

for a HS25-44 truck loading. The resulting girder cross section is 8-1/2 in. wide and 45-3/8 in. deep. The diaphragms 

are glulam sections 5-3/8 in. thick, 37-3/8 in. deep, and 35-

1/2 in. long with the same denomination as the deck panels 

and beams (Southern Pine combination 48), and placed 

perpendicular to the girders. The diaphragms are attached to 

the girders with 7/8-in.-diameter tie rods that extend through 

the diaphragms at the third glue line from the top and bottom of the diaphragm. The bearing connections for the 

glulam beams are fixed to prevent longitudinal movement. 

The bearing shoe consists of two 4- by 8- by 1/2-in. steel 

angles that connect the beams to the substructure (concrete 

backwall) with 3/4-in.-diameter anchor bolts and to the 

glulam beams with 3/4-in.-diameter through bolts. A 3/4-in.-

thick bearing pad made of elastomeric rubber will also be 

assumed.

The baseline bridge deck consists of transverse glulam deck 

panels with vertical laminations (Figure 2). The glulam 

deck panels are 5-1/8 in. thick. All standard interior and end 

panels are 4 ft wide. The panels are attached to the beams 

with 5/8-in.-diameter dome head bolts and cast aluminum 

alloy deck brackets. The deck brackets are connected to the 

glulam beam sides in 3/4- by 8-in. slots. For the interior 

panels, two brackets per beam side are connected to the 

deck panel with bolts placed 6 in. from the panel edge and 

spaced at 2 ft and not coincident with the other side. In the 

end panels, the brackets are connected to the deck panel 

with bolts placed 6 in. from the panel edges.

Measurement Attributes of the Smart 

Timber Bridge

Development of the smart timber bridge focuses first on 

understanding bridge-specific behaviors and deterioration 

modes (Ritter 1992, FPL 1999, NFPA 2005) and then designing an instrumentation and data processing plan that 

allows extraction of the needed information. Assessment of 

structural adequacy and deterioration of a structure is key to 

the success of a smart timber bridge. The needed variables 

thought to define the attributes of the smart timber bridge 

constructed with glued laminates are described below.

Structural Adequacy

Structural behavior attributes, which are related to the load 

side of structural capacity, will be evaluated with respect to 

three engineering metrics: lateral load distribution, dynamic 

load allowance, and fatigue life usage (Hosteng 2004, Le 

and others 1998, Ansell 2003, Clorius and others 2000, 

Davids and others 2005). Lateral load distribution obtained 

from the live loads is an important parameter that indicates 

how loads are being resisted by the structure and may be an 

indicator of changes in stiffness and possible deterioration 

of the bridge over time. Also, quantification of the dynamic 

load allowance will give a similar indicator. Finally, fatigue 

life usage (which can be used in bridge management and 

in other ways) of critical members (such as girder-to-deck 

and girder-to-bearing connections) will be assessed through 

stress cycle counting.

Another structural adequacy requirement is serviceability of 

the structure, which focuses on ensuring functionality and 

appearance of the structure. In the smart timber bridge, live 

load deflection, or measurements related to deflection (such 

as strain) will be monitored to investigate functionality and 

changes in stiffness of the bridge superstructure. Somewhat 

related, a technique to assess configuration and loading of 

vehicles will be obtained from measurement responses. 

Evaluation of all assessed parameters, in the light of the 

actual response of the structure, would provide information 

about usage of the structure and could be used to predict 

remaining service life of the smart timber bridge.

Structural damage is an equally important characteristic 

of timber bridges. The SHM system would need to assess 

changes in stiffness resulting from factors, such as delamination, vehicular collision, and other sources. This will most 

Figure 1. Cross-sectional view of the baseline bridge, with a 

24-ft roadway width.
Development of a Smart Timber Bridge—A Five-Year Plan

3

likely be accomplished through statistical evaluation of the 

above parameters.

Deterioration

When properly engineered throughout the design, fabrication, and installation process, wood is a durable bridge 

material (Ritter 1992, FPL 1999, NFPA 2005). However, 

over extended periods under service, wood may decay due 

to exposure to biologics and other deleterious environment 

factors. To preserve the utility of the bridge and to ensure 

the safety of road users, the smart timber bridge will need 

to assess the condition of the structure by measuring factors 

that are associated with decay and deterioration. Moisture 

content, corrosion, and ultraviolet light degradation are the 

primary metrics to be monitored by the smart timber bridge. 

Proactive maintenance measures triggered by monitoring 

these metrics may prevent future problems related to performance and integrity of the structure.

Moisture has a detrimental influence on the service life of 

many structures, especially in wood, causing several deterioration processes. In the presence of water, the microstructure of wood swells until the fiber saturation point, which 

can make the cellulose more accessible to fungal enzymes, 

thereby enhancing rate of decay. Repeated wetting and 

drying or continuous exposure to moisture can result in 

leaching of toxic heartwood extractives and some preservatives, reducing resistance to decay (Ritter 1992). Currently, 

early assessment of moisture content and resulting preventive maintenance have proven to effectively control and 

prevent decay in timber bridges. Thus, moisture content 

monitoring will aid in the development of an active 

management program.

Ultraviolet light is a source of deterioration often found 

in timber bridges. Ultraviolet light chemically degrades 

the lignin near the wood surface of members such as deck 

edges, exterior girders, and barriers. Continued removal of 

the damaged wood eventually reduces member dimensions, 

affecting both stiffness and strength parameters. Wood lignin degradation will be detected by FOSs in exposed smart 

timber bridge members.

Finally, wood degradation due to metal corrosion will be 

assessed. Corrosion in timber bridges is an extreme case 

of bridge deterioration. Marine environments and deicing 

of roadways may cause deterioration in exposed bolts and 

other steel components such as diaphragms and truss ties. 

Early detection of corrosion in exposed bolted connections, 

steel trusses, and diaphragms will be monitored by detecting 

ferric ions in susceptible areas.

Figure 2. Plan view of baseline bridge, showing the transverse glulam deck 

panel layout for a 60-ft-long bridge.
4

General Technical Report FPL–GTR–195

Anticipated Sensor Types To Be Used

In light of their attractive attributes, the sensors of choice for 

the smart-timber bridge are likely to be FOSs. In particular, 

FOS based on FBG technology, which can measure different 

parameters such as strain, temperature, pressure, vibrations, 

acceleration, and potentially other metrics (Swart and others 

2001, Wiese and others 1999, Yeo and others 2006, Furh and 

Houston 1998, Maalej and others 2002), will be the primary 

focus of investigation. In a properly designed FBG sensor, 

a change in the desired parameter will induce a change in 

the wavelength measured by the sensor. In comparison with 

electrical sensors, FBG sensors are free from electromagnetic interference and time-related drift typically encountered with electrical sensors. FBG sensors are lightweight 

and small and can be embedded and integrated in virtually 

any structure (Kuang and others 2001, Measures and others 

1995, Ritdumrongkul and others 2003). Also, only a limited 

number of lead wires are needed to transmit data obtained 

from a large number of FBG sensors.

Communication and Reporting

After the raw data are collected and cleansed, they are 

ready for processing, storage, and interpretation (ISIS 2001, 

Bruhwiler 2003). Data collected during continuous monitoring activities of the smart timber bridge will need to be 

processed before being stored. The developed performance 

algorithm would not only cleanse but process the obtained 

data (such as strain, temperature, moisture content, deflection, delamination, truck loads, ultraviolet light exposure, 

and corrosion). Following interpretation, a simple report 

would be developed by the smart timber bridge. The behavior of the superstructure would be summarized in the report, 

which would integrate all responses related to attributes of 

the smart timber bridge described in this section and report 

on overall bridge condition. This report would also serve as 

an alert to early damage or deterioration. This information 

would need to be translated into a clear language that the 

owner can interpret for prompt evaluation and programming 

of routine maintenance and/or rehabilitation of the bridge.

Problem Statements

Problem statements required to achieve the overall objective 

are summarized in Table 1. Sensor development tasks are 

in progress. The data processing and software development 

tasks can be accomplished concurrently. Field demonstration does not include costs for design, construction, or 

installation of a new bridge. Detailed project descriptions 

for each problem statement are provided in the following 

sections.

Development of FBG Sensors to Detect 

Moisture Content in Timber

Background: Moisture plays an important role in determining the life of wood and is known to cause several deterioration processes (Ritter 1992). Moisture can both reduce the 

decay resistance of wood and cause severe degradation of 

material properties. Also, moisture can accelerate fatigue 

life usage in compression members. Currently, several 

Table 1—Summary of research problem statements 

Phase Project focus 

Estimated 

cost 

(thousand

dollars) 

Total 

cost 

(thousand

dollars) 

Research plan Develop 5-year plan for development of a smart glulam bridge — Done 

Development of reliable 

sensors for timber bridge 

performance monitoring 

Embed sensors during manufacture of glulam bridge components 

(2 years) 

100

750

Develop new sensors for measuring in situ moisture contents 

(1-1/2 years) 

200

Develop new sensors for detecting ferric ions caused by metal 

fastener corrosion (1-1/2 years) 

200

Develop new sensors for detecting lignin loss caused by 

UV degradation (1-1/2 years) 

250

Development of an 

efficient structural health 

monitoring system

Data processing techniques for structural adequacy parameters 

(1 year) 

125

Field demonstration 

Data processing techniques for detecting systemic changes 

in stiffness (1 year) 

125

525

125

Data processing for determining truck loading characteristics 

(1-1/2 years) 

175

Bridge management software development (1 year) 100

Construct a new “smart bridge” with embedded sensors 

and 1 year data collection (1 year) 

125

Total 1,400 

Table 1—Summary of research problem statements 

Phase Project focus 

Estimated 

cost 

(thousand

dollars) 

Total 

cost 

(thousand

dollars) 

Research plan Develop 5-year plan for development of a smart glulam bridge — Done 

Development of reliable 

sensors for timber bridge 

performance monitoring 

Embed sensors during manufacture of glulam bridge components 

(2 years) 

100

750

Develop new sensors for measuring in situ moisture contents 

(1-1/2 years) 

200

Develop new sensors for detecting ferric ions caused by metal 

fastener corrosion (1-1/2 years) 

200

Develop new sensors for detecting lignin loss caused by 

UV degradation (1-1/2 years) 

250

Development of an 

efficient structural health 

monitoring system

Data processing techniques for structural adequacy parameters 

(1 year) 

125

Field demonstration 

Data processing techniques for detecting systemic changes 

in stiffness (1 year) 

125

525

125

Data processing for determining truck loading characteristics 

(1-1/2 years) 

175

Bridge management software development (1 year) 100

Construct a new “smart bridge” with embedded sensors 

and 1 year data collection (1 year) 

125

Total 1,400 

Table 1—Summary of research problem statements 

Phase Project focus 

Estimated 

cost 

(thousand

dollars) 

Total 

cost 

(thousand

dollars) 

Research plan Develop 5-year plan for development of a smart glulam bridge — Done 

Development of reliable 

sensors for timber bridge 

performance monitoring 

Embed sensors during manufacture of glulam bridge components 

(2 years) 

100

750

Develop new sensors for measuring in situ moisture contents 

(1-1/2 years) 

200

Develop new sensors for detecting ferric ions caused by metal 

fastener corrosion (1-1/2 years) 

200

Develop new sensors for detecting lignin loss caused by 

UV degradation (1-1/2 years) 

250

Development of an 

efficient structural health 

monitoring system

Data processing techniques for structural adequacy parameters 

(1 year) 

125

Field demonstration 

Data processing techniques for detecting systemic changes 

in stiffness (1 year) 

125

525

125

Data processing for determining truck loading characteristics 

(1-1/2 years) 

175

Bridge management software development (1 year) 100

Construct a new “smart bridge” with embedded sensors 

and 1 year data collection (1 year) 

125

Total 1,400 
5

Development of a Smart Timber Bridge—A Five-Year Plan

techniques to evaluate moisture are used during regular 

inspections. This practice has demonstrated that detection of 

pockets of elevated moisture content along with associated 

preventive maintenance of timber bridges can effectively 

control and prevent decay in timber bridges.

Objectives: To develop a FBG sensor that can reliably measure moisture content in timber over a range of 10% to approximately 30%.

Task 1: Develop FBG sensors capable of detecting moisture 

content in timber members. Recent studies have demonstrated that fiber-optic-based humidity sensors can measure 

moisture absorption in concrete by using FBG sensors with 

a moisture-sensitive polymer, indicating their potential use 

in timber members. The developed sensors should have 

accuracy levels similar to currently used technologies and 

should be able to be embedded within timber members.

Task 2: Test FBG sensors under various moisture and ambient temperature conditions including wetting and drying 

cycles. Small and full-scale glulam members are to be fabricated and instrumented with the developed FBG moisture 

sensor. The specimens should be exposed to accelerated 

environmental conditions and the FBG moisture sensor 

readings compared with traditional moisture content probe 

readings.

Task 3: Develop a final report that summarizes the work related to sensor development and laboratory tests. This report 

will include details on the FBG sensor materials and specifications for future installation.

Duration: Estimated project time is 18 months.

Cost: Estimated project cost is $200,000.

Development of FBG Sensors to Detect 

Ferric Ions

Background: Wood degradation due to metal corrosion is a 

type of timber bridge deterioration that is often observed in 

marine environments. A similar effect can also result from 

the application of deicing chemicals to a bridge roadway. In 

the presence of moisture, fasteners made of iron corrode and 

release ferric ions, which deteriorate wood cell walls. This 

occurs because acidity at the anode causes cellulose hydrolysis, which decreases wood strength.

Objective: To develop a FBG sensor that indirectly measures 

corrosion activity by detecting the presence of ferric ions.

Task 1: Develop FBG sensors that detect ferric ions released 

from bolts and other metal fasteners. A limited amount of 

documented research indicates that multi-parameter FBG 

sensors are capable of indirectly measuring steel reinforcement corrosion in reinforced concrete roadways and bridges 

by detecting changes in chemical composition or simply 

changes in material color. Such a sensor is to be developed; 

it is desirable that the developed sensor be able to detect 

both corrosion initiation and growth rate.

Task 2: Test FBG ferric ion sensors in typical saline environments, including marine water and in the presence of 

chloride-based deicing chemicals. Testing of the developed 

sensor should be conducted on small-scale specimens with a 

variety of fastener types. When possible, comparisons with 

electromechanical methods will be made.

Task 3: Prepare a final report that summarizes the FBG sensor development and testing. This report will include details 

on the FBG sensor materials and specifications for future 

installation.

Duration: Estimated project time is 18 months.

Cost: Estimated project cost is $200,000.

Development of Sensors to Detect 

Degradation in Wood Lignin

Background: Chemical degradation of wood lignin can be 

caused by ultraviolet sunlight exposure. This deterioration typically affects the surface and, over time, results in 

a reduction in member physical dimensions, which in turn 

reduces the associated member strength and stiffness. This 

deterioration becomes critical when the continued removal 

of material reduces the member capacity below the required 

demand or compromises the protective outer layer of 

preservative-treated material.

Objectives: To detect wood deterioration associated with 

ultraviolet light exposure, FBG sensors will be developed to 

detect the initial loss of wood lignin.

Task 1: Develop FBG sensors that detect the breakdown of 

wood lignin associated with ultraviolet light exposure. The 

developed sensor could be either surface mounted or embedded. In either case, it is desirable to detect the initiation 

of the breakdown and, if possible, to determine the rate of 

deterioration.

Task 2: Evaluate the performance of the developed FBG 

sensors under controlled ultraviolet light. The amount of 

exposure should be varied to simulate varying degrees of 

exposure and should be used to study the sensitivity of the 

sensor to deterioration. When possible, correlations to conventional detection techniques are to be made.

Task 3: Develop a final report that documents the sensor development and laboratory testing. The final report will detail 

the sensor materials and specifications.

Duration: Estimated project time is 18 months.

Cost: Estimated project cost is $250,000.

Evaluation of Techniques for Embedding 

and Attaching FBG Sensors to New Glulam 

Bridge Members

Background: FBG sensors have been embedded and attached to structural materials such as steel and reinforced 

concrete with proven success. These deployed systems are 
6

General Technical Report FPL–GTR–195

currently working under harsh climatic conditions and are 

reporting on the health condition of highway bridges around 

the world. However, no such applications exist for timber 

members.

Objectives: To measure the desired physical attributes of the 

smart timber bridge, techniques for embedding and attaching FBG sensors to timber members need to be developed 

and evaluated under static, cyclic, and sustained loads.

Task 1: Collect information on available structural adhesives 

for adhering FBG sensor to timber members. Existing and 

emerging adhesives for glulam are to be investigated. The 

most promising adhesives for attaching and embedding FOS 

sensors to timber will be selected for further evaluation.

Task 2: Develop potential techniques (such as packaging, 

configurations) for embedding and attaching FBG sensors to 

timber members. These techniques need to include designs 

for sensors that require structural adhesion as well as those 

sensors that need to be isolated from the structural behavior.

Task 3: Evaluate the performance of attached and embedded 

sensors within tension members under static loads. Prepare 

and test small-scale glulam specimens with various adhesives and attachment methods. Select adhesive types that 

show the best agreement with conventional instrumentation.

Task 4: Evaluate the performance of embedded and attached 

sensors within tension members under cyclic loads. Prepare 

small-scale glulam specimens with candidate adhesives and 

packaging. Determine the fatigue life of the adhesive and 

compare responses with conventional instrumentation.

Task 5: Evaluate adhesive responses under sustained tensile 

load and temperature variations. Prepare small-scale glulam 

specimens and test them under different sustained loads with 

temperature and humidity variations. Analyze failure modes, 

effect of the applied variables, and bond-slip behavior based 

on effects of adhesive thickness and protective coating 

materials.

Task 6: Select the embedment and attachment methods with 

best performance.

Task 7: Evaluate sensor and adhesive performance in fullscale laboratory specimens under static and cyclic loads. 

Where possible, make comparisons under both static and 

cyclic loads with conventional instrumentation.

Task 8: Prepare a final report that will document the developed techniques for embedding and attaching FBG sensors 

to timber members.

Duration: Estimated project time is 24 months.

Cost: Actual project cost was $93,000, as this study is 

funded and currently in progress.

Development of Data Processing 

Techniques for Determining Structural 

Adequacy Parameters

Background: Lateral load distribution between girders and 

dynamic load allowance, as defined in current bridge standards, are engineering metrics that indicate how loads are 

resisted by bridge superstructures. Processing techniques 

and associated instrumentation are needed to extract this 

information. In addition, fatigue life usage, when combined 

with other information (such as moisture content), gives a 

bridge owner a sense of how much usage a bridge has seen.

Objective: The objective of this work is to develop data 

processing techniques to determine lateral load distribution, 

dynamic load allowance, and fatigue life usage.

Task 1: Identify FBG strain sensor locations in the conceptual smart timber bridge that are needed for determining 

structural adequacy parameters. Identify alternative sensor 

locations in other possible positions.

Task 2: Review currently developed data processing techniques for determining structural adequacy (such as fatigue 

life usage in real-time). Select tools that may be implemented to the data processing techniques.

Task 3: Collect data from a selected highway timber bridge 

subjected under ambient traffics load for a period of time to 

be used in the beta testing of data processing techniques for 

evaluating structural adequacy. These data should be collected using conventional instrumentation.

Task 4: Develop data processing techniques for determining 

lateral load distribution characteristics in real time. These 

techniques should identify vehicle position such that the 

lateral load distribution factors may be “normalized.” The 

developed techniques should be tested with the data collected in Task 3.

Task 5: Develop data processing techniques for determining 

dynamic load allowance characteristics in real time. The developed techniques should be tested with the data collected 

in Task 3.

Task 6: Develop data processing techniques for determining 

fatigue life usage in real time. The developed techniques 

should be tested with the data collected in Task 3.

Task 7: Prepare a final report summarizing the data processing techniques for lateral load distribution, dynamic load 

allowance, and fatigue life usage.

Duration: Estimated project time is 12 months.

Cost: Estimated project cost is $125,000.
7

Development of a Smart Timber Bridge—A Five-Year Plan

Development of Data Processing Techniques 

for Determining Changes in Structural 

Stiffness

Background: In general, changes in structural stiffness 

imply that a change in member properties has occurred. 

Techniques for identifying both instantaneous and gradual 

changes in stiffness are needed.

Objectives: Develop techniques for assessing changes in 

structural stiffness through statistical evaluation of measured 

physical properties.

Task 1: Investigate statistical evaluation methods for evaluating changes in large and diverse population measurements. 

Select statistical evaluation methods most suitable for processing data of this type for the purpose of identifying statistical differences over short and long periods of time.

Task 2: Modify collected data from a highway timber bridge 

subjected to ambient traffics load to generate synthetic 

changes in structural stiffness. These modifications should 

include both gradual and significant variations in lateral load 

distribution and dynamic allowance characteristics.

Task 3: Develop techniques for evaluating gradual and significant changes in stiffness through statistical evaluation of 

lateral load distribution and dynamic allowance. It is desired 

that the technique will both identify and quantify the level 

of structural stiffness change. Beta test the developed techniques using the synthetic data obtained in Task 2.

Task 4: Develop data processing techniques and required 

instrumentation for detecting vehicle collisions. The data 

processing techniques and needed instrumentation should be 

capable of detecting collision both on and below the bridge. 

Validate the technique with analytical modeling.

Task 5: Prepare a final report with the developed data processing techniques for stiffness degradation. This report will 

document the defined damage levels in each case and the 

beta test results.

Duration: Estimated project time is 12 months.

Cost: Estimated project cost is $125,000.

Development of Data Processing Techniques 

to Determine Vehicle Characteristics

Background: Bridges are designed to carry truck traffic, and 

it is desirable to know the number, geometry, and weight of 

the passing trucks. By determining vehicle characteristics, it 

will be possible to better predict usage and induced damage 

or deterioration.

Objective: Develop a data processing technique for determining vehicle characteristics such as vehicle weight, configuration, and speed.

Task 1: Review previous work for determining vehicle characteristics. Select tools that may be integrated into other 

data processing technique development.

Task 2: Develop initial conceptual tools to determine vehicle 

characteristics and an associated instrumentation scheme.

Task 3: With the instrumentation scheme developed in Task 

2 in mind, collect response data from a typical timber bridge 

using conventional instrumentation.

Task 4: Using the data collected in Task 3 and the techniques 

developed in Task 2, determine the effectiveness of various 

tools at predicting vehicle characteristics. If required, refine 

the developed tools to improve overall performance.

Task 5: Prepare a final report that documents the development and resulting data processing tools.

Duration: Estimated project time is 18 months.

Cost: Estimated project cost is $175,000.

Development of Smart Timber Bridge 

Software Application

Background: Any effective SHM system must integrate data 

from many sources and evaluate it in a simple, turn-key 

application. Such a software application is needed for the 

smart timber bridge.

Objectives: Develop a software application to collect, reduce, extract, analyze, and report on the health condition 

of the smart timber bridge by using collected data and data 

processing techniques that evaluate and translate obtained 

responses in clear formats.

Task 1: Develop software to collect data obtained from FBG 

sensors. In this task, detection of possible data crossover 

will be conducted and revised. Also, this software will verify sensor performance and collected data consistency.

Task 2: Develop tools to cleanse the collected data from 

noise and undesired data record characteristics.

Task 3: Develop tools to preprocess the obtained data by extracting data related to loading events and discarding other 

insignificant data. Selected data will be saved in compressed 

form so that they may be retrieved and used for near-realtime analysis.

Task 4: Develop an application that analyzes the stored data. 

This analysis should include converting measured wavelengths to engineering parameters. The software should 

integrate all applications and procedures developed under 

separate work. The software should develop various bridge 

condition reports on a daily, weekly, monthly, and yearly 

basis that are clear and understandable to bridge owners.

Task 5: Prepare a final report that documents the developed 

software application.
8

General Technical Report FPL–GTR–195

Duration: Estimated project time is 1 year.

Cost: Estimated project cost is $100,000.

Demonstration of the Smart Timber Bridge

Background: Work conducted separately has resulted in 

the development of several new sensor types for measuring 

engineering parameters such as moisture content, degradation due to corrosion and ultraviolet exposure, and others. 

Further other work has led to the development of data processing techniques for evaluating collected behavior data in 

real time.

Objectives: Demonstrate all the developed components of 

the smart timber bridge in a field application.

Task 1: Prepare detailed construction plans and specifications for the assembly of the smart timber bridge members 

and installation of FBG sensors. Also, design the data acquisition system, including sensor positions, cabling routes, and 

storage cabinets.

Task 2: Work with selected construction contractors, equipment manufacturers, and timber fabricators to fabricate and 

construct the smart timber bridge.

Task 3: Test the SHM system under ambient traffic loading. 

Verify the performance of all sensors and calculations with 

known loads. Where possible, validate the performance of 

sensors and algorithms with conventional sensors and loading tests.

Task 4: Monitor the smart timber bridge and the monitoring system for 1 year. Complete any needed revisions to the 

system.

Task 5: Prepare a final report that documents the performance of the smart timber bridge. The report should include 

detailed construction specifications for future smart timber 

bridges.

Duration: Estimated project time is 12 months.

Cost: Estimated project cost is $125,000 plus bridge costs.

Concluding Remarks

This report outlines a 5-year research plan to develop a 

smart glulam timber bridge. New-sensor technologies will 

be developed to measure structural performance and deterioration parameters. A turnkey system will be developed that 

is capable of monitoring, analyzing, and reporting on the 

performance and condition of a timber bridge. This comprehensive effort will result in the first fully automated structural health monitoring system for timber bridges.

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