digital imaging for bridge deflection measurement of a...

Digital Imaging for Bridge Deflection Measurement of a Steel Girder Composite Bridge 1

2

Corresponding Author: 3

Erin Santini-Bell, Ph.D., P.E. 4

Department of Civil Engineering 5

University of New Hampshire 6

Durham, NH 03824 7

Phone: (603) 862-3850 8

Email: [email protected] 9

10

Philip A. Brogan 11

Former Graduate Research Assistant 12



Durham, NH 03824 15

16

Paul J. Lefebvre 17

Former Graduate Research Assistant 18



Durham, NH 03824 21

22

Jason Peddle 23

Graduate Research Assistant 24



Durham, NH 03824 27

28

Brian Brenner, P.E. 29

Vice President 30

Fay, Spofford & Thorndike, INC 31

5 Burlington Wood 32

Burlington, MA 33

34

Masoud Sanayei, Ph.D. 35

Department of Civil and Environmental Engineering 36

Tufts University 37

308 Anderson Hall 38

Medford, MA 02155 39

40

August 1, 2010 41

42

Word count: 3,356 words (12 figures, 0 tables) 43

Adjusted word count: including figures and tables: 6,356 44

with footnotes & endnotes:6,983 45

46

Keywords: Structural Health Monitoring, load testing, full-scale bridge, non-destructive testing, 47

deflection measurement, digital image correlation 48

49

Abstract 1

Bridge managers have historically relied on visual inspection reports and field observation, 2

including photographs, to assess bridge health. The inclusion of instrumentation including strain 3

gauges, tiltmeters, linear variable differential transformers (LVDT), and accelerometers along 4

with a structural model can enhance bridge management. This combination of instrumentation 5

and modeling is commonly classified as structural health monitoring (SHM). Traditional SHM 6

measurements are reference-independent, such as strain gauges and tiltmeters. These sensors can 7

be easily installed during the initial construction of new bridge but installation is significantly 8

more difficult once the bridge is in service. The collection of global deflections of a bridge, a 9

reference-dependent measurement, is even more difficult to collect as the connection between 10

the bridge structure and a fixed reference is geometrically challenging. A measurement technique 11

that alleviates both of these issues is digital image correlation. 12

Through recent advances in digital photography and the computational capability of personal 13

computers, digital image correlation (DIC) is a non-contact measurement technique that can be 14

cost-effectively deployed to collect global deflection measurements of a bridge structure. DIC 15

uses multiple digital cameras to photograph a target object to provide structural response 16

information. This project incorporates a DIC system and LVDT sensors into the bridge 17

instrumentation, testing and modeling program for a concrete deck-steel girder composite bridge 18

in Barre, Massachusetts. This paper presents a collaborative research project partially funded by 19

the CAREER, Major Research Instrumentation and the Partnerships for Innovation Programs at 20

the National Science Foundation to develop a framework for bridge condition assessment 21

integrating instrumentation and structural modeling for bridge decision-making and 22

management. 23

INTRODUCTION 1

An high profile bridge failures or near failures in the past several decades has highlighted the 2

deteriorating condition of bridge infrastructure in America and the need for additional and 3

advanced management and monitoring techniques. According to the American Society of Civil 4

Engineers (ASCE) infrastructure report card, America’s bridges have consistently received a 5

rating of “C” for the past ten years (ASCE, 2010). Meanwhile, construction costs have inflated 6

by at least 50 percent and neither federal nor state transportation budgets have been able to keep 7

pace with the increasing expenses (AASHTO, 2008A). 8

Traditionally, bridge health has been monitored by visual inspections according to the 9

National Bridge Inspection Standards (NBIS) and load rating. Load ratings are calculated based 10

on the results of inspection condition ratings (AASHTO, 2008B). Load ratings are a more 11

objective measure of bridge health, but are still calculated based on the results of subjective 12

observations collected via visual inspections. An “initial inspection” is conducted immediately 13

following construction completion to establish a baseline for future inspections. After this, 14

“routine inspections” take place at least every two years, with “special inspections” to investigate 15

any anomalies in the structure. Although these inspections are standardized for each state, the 16

inspection process is subjective, with each inspector using his own judgment and personal 17

expertise to assess the condition of each bridge component (Phares et. al. 2001). 18

This ambiguity, along with the collective deterioration of bridge infrastructure, has lead to 19

the realization that a more long-term, objective approach needs to be applied to bridge 20

management. This realization has initiated advancements and increased interest in structural 21

health monitoring (SHM). The International Society for Structural Health Monitoring of 22

Intelligent Infrastructure (ISHMII) defines SHM as “a type of system that provides information 23

on demand about any significant change or damage occurring in the structure” (ISHMII, 2010). 24

SHM typically includes data collection through sensor based instrumentation, post-processing 25

the collected responses with respect to predicted responses from both hand calculations and 26

structural modeling and then monitoring of any changes to the structure. 27

Structural Health Monitoring 28

The goal of a SHM system is to employ sensing instruments to provide information 29

pertaining to the condition of the structure (Chang 2003). Recent advancements in technology 30

have made bridge structure instrumentation very popular (Santini-Bell and Sipple, 2009, Riad et 31

al , 2006 and Wang, 2008). The Federal Highway Administration (FHWA) envisions sensing and 32

measurement capabilities fully integrated into the design, construction and operation of the 33

bridge of the future (ISHMII, 2006). For structural evaluation of bridges, various types of sensor 34

information, such as strain distribution, vibration and natural frequencies, and deflection 35

measurements are used to generate data that provides information relating to the health and load 36

carrying capacity. The collected data is used with a structural model of the bridge to determine 37

the bridge performance parameters by solving an inverse problem (Santini-Bell et al 2007). 38

In current practice, these test methods generally require mounting transducers, such as strain 39

gauges, on the underside of the structure on the bending members. For bridges over water, the 40

problem of sensor system installation is further compounded. Static testing is typically 41

conducted using truck with a known load that is traveling across the bridge along a marked path 42

while stopping at predetermined locations to induce the maximum response. New non contact 43

measurement techniques facilitate the inclusion of deflection measurements for SHM. For 44

example, Benmokrane et al (2006) measured global bridge deflection with a theodolite to 1

supplement internal temperature and fiber-optic strain gauges on the Morristown Bridge in 2

Vermont. 3

BRIDGE DEFLECTION MEASUREMENTS 4

Traditional deflection measurement methods such as linear variable differential transformers 5

(LVDT), string potentiometers and GPS are difficult to implement in the field for the majority of 6

bridges. The geography and environment of most bridges limit access to crucial bridge elements 7

for deflection measurements. Because of rapid advancements in the resolution and maximum 8

frame rate of digital imaging equipment recently, digital image correlation techniques (Lee and 9

Shinozuka, 2006A and Wahdeh et. al., 2003) permit the determination of the transformation 10

between equivalent features in two or more images. DIC is suitable for global linear 11

transformations as well as regional non-linear distortion. Optical techniques offer the potential to 12

acquire bridge performance data without the need for installation of sensors, targets, or other 13

devices on the bridge, and without the need to disrupt traffic during measurements. Such optical 14

measurements can, in conjunction with the crossing of a design load, produce whole bridge 15

deflection curves from one or both sides of the structure. 16

Increasingly sophisticated non-contact measurement techniques do exist, such as 17

interferometric radar (Mayer et al 2010) and laser-based instrumentation (Fuchs et al, 2004). 18

Both systems can be set up under the bridge, limiting the traffic impact and have the capability to 19

accurately capture bridge response. These systems can measure bridge response from longer 20

distances and at more locations than DIC. Therefore, they are more appropriate for longer span 21

and bridges over water. The objective of this work is to assess the utility and cost effective 22

digital imaging systems and the practical limits of the deflection measurement application to 23

bridge testing. The collected structural deflections of the bridge collected via DIC are then 24

compared to a structural model that has been calibrated with strain data collected from a 25

controlled load test 26

Digital Image Correlation 27

Video monitoring and digital imaging has been used since the 1980’s for traffic management 28

(Rodriguez and Garcia, 2010). Recent works have integrated the video information with 29

structural health monitoring of highway bridges (Fraser et al 2010, Catbas et al, 2004). These 30

platforms were developed to be able to use ambient traffic data as a source for dynamic 31

excitation. This project will expand on this work, specifically the image analysis, for adaptation 32

to bridge management. 33

Those in the material science field also use digital image correlation extensively to determine 34

material properties and behavior. Mechanical engineers have been using DIC for many years to 35

analyze the stresses and strains in machine parts (McLaughlin et al, 2009). These analyses are 36

usually conducted in two dimensions, where a flat plane is stretched and stresses and strains are 37

measured along that plane. DIC has also been used in limited civil engineering applications for 38

bridge testing (Lee and Shinozuek, 2006B and Gamache and Santini-Bell, 2009). 39

Required hardware for the entire process is as follows: one or more high-resolution digital 40

cameras; a tripod; a portable computer with a suitable video card, processor, and large memory 41

bank; a calibration target; and a target with a sufficient texture. The software for both image 42

capture (Vic-Snap) and post-processing (VIC-3D) used for this project was provided by 1

Correlated Solutions Inc. (CSI) of Columbus, South Carolina. 2

Bridge testing adds challenges to DIC that would not normally be encountered in the 3

laboratory such as environmental effects. A significant increase in temperature, solar glare or 4

wind can cause the camera tripod to expand or shift, altering the focus of the cameras and 5

causing an apparent movement of the target object. In fact, the greatest challenge to DIC in 6

general is maintaining the position of the cameras. Change in solar glare can also impact the 7

image capture by creating glare across the lenses. The geography and soil conditions of the test 8

site also limits placement of the cameras with respect to maintaining camera position throughout 9

the data capture. These challenges can be address by using DIC in night testing where artificial 10

lighting is used and the temperature change during the test is minimal. 11

Accurate post-processing of digital imaging for structural response depends on the ability to 12

identify the intensity and location of each image pixels from the first image to the last. The 13

differential position of each individual pixel can be identified provided that the configuration of 14

the cameras, including the relative distance between cameras and relative angle of the cameras in 15

all directions was successfully calibrated. Any digital image post-processing is dependent upon a 16

robust calibration and differential in pixel intensity. 17

DIC Limitations 18

Value of data collected via DIC is directly related to the resolution of the camera, material 19

texture, light, and distance to the target area. Depending on the focal lens and proximity of the 20

camera to the target area, camera shake from passing vehicles or in some cases pedestrian can 21

significantly impact the collected images (Gamache and Santini-Bell, 2009). The goal of this 22

project is to assess the effectiveness of DIC to supplement SHM instrumentation plans for bridge 23

management. 24

CASE STUDY: VERNON AVENUE BRIDGE IN BARRE, MASSACHUSETTS 25

The Vernon Avenue Bridge (VAB) was constructed during the summer of 2009 and opened 26

to traffic in September 2009. This bridge was constructed by E.T.&L. Corporation for the 27

Massachusetts Highway Administration for the Town of Barre, Massachusetts. This bridge is a 28

150-foot (45.7 km) three-span continuous bridge. It is comprised of a concrete deck supported 29

by six weathering steel girders, as shown in Figure 1. This three-span continuous bridge spans 30

the Ware River, which flows at a maximum depth of approximately 10 feet (3.05m). The eight 31

inch (20.3 cm) concrete deck was cast-in-place (CIP) during a single continuous pour. VAB was 32

instrumented during fabrication and erection as part of a collaborative research project funded by 33

the National Science Foundation (Santini-Bell et al, 2010). 34

The instrumentation plan includes 100 strain gauges, 16 accelerometers, 24 tiltmeters as well 35

as temperature sensors on the girders and in the concrete deck. On September 3, 2009, a static 36

load test was performed prior to the bridge being opened to the traveling public. DIC was 37

included in this load test to supplement the instrumentation with deflection measurements. The 38

collected strain measurements were used to calibrate a structural model of the VAB. The DIC-39

collected deflection measurements were then compared with the predicted deflection from this 40

calibrated structural model (Lefebvre, 2010). Prior to erection, the web of the exterior girder on 41

the east side of the VAB was speckled with white magnets to increase pixel intensity for DIC 42

testing, as shown in Figure 2. 43

1

FIGURE 1 Vernon Ave Bridge in Barre, Massachuetts. 2

3

FIGURE 2 East Exterior Girder of the Vernon Avenue Bridge with speckle pattern during 4

erection. 5

Strain sensors and thermistors are distributed along the length of each girder on both sides of 6

the web with the exception of the exterior girders, which only have instrumentation in the 7

interior face. All sensors were connected to 20 iSite data loggers provided by Geocomp, 8

Incorporated for data acquisition. By design, all sensors installed in the bridge were calibrated to 9

a ±0.002% accuracy. All data acquisition modules were clustered on the south abutment for 1

access to a power supply and communication lines. 2

Digital Image Correlation Verification 3

Since DIC is not widely used for bridge deflection measurement, several verification tests 4

were completed. Deflection data was collected during the concrete pour. The results from this 5

test showed that the speckle pattern shown in Figure 2 was not appropriate for the range of 6

deflection and distance between target and camera required that location. Therefore the speckle 7

pattern was refined with smaller magnets, shown in Figure 6. Further testing showed that from 8

distances of approximately 15 feet (5 m) or less, sidewalk chalk applied to the girder face 9

provided sufficient pixel intensity variation, see Figure 3. For the VAB the target surface is 10

weathering steel, Gamache and Santini-Bell (2009) showed that DIC can be used without targets 11

or additional speckle patterns on concrete bridge element using the natural variation in the 12

concrete surface. 13

14

FIGURE 3 Chalk pattern for DIC on the South Span of the VAB. 15

Given that there was no easy way to collected deflections at the mid-span, verification tests 16

were conducted on the south abutment. At this location, alternative deflection sensors could be 17

easily deployed. An accepted method for collecting bridge deflection measurement is the use of 18

LVDTs (Yost et al, 2005). The verification test setup consisted of 2 LVDTs mounted on tripods 19

connected to a data acquisition system and two sets of digital cameras as shown in Figure 4. 20

1

FIGURE 4 LVDT and DIC Verification Setup for VAB. 2

Using ambient traffic excitation, bridge deflections were captured using both the LVDTs and 3

DIC at the same locations. Sampling rates of the LVDT and DIC were 60 Hz and 1 Hz, 4

respectively. Figure 5 shows correlation of captured data for three minutes during a typical 5

weekday afternoon at the VAB. These results support the use of DIC for bridge deflection 6

measurements. The placement of the camera with respect to each other and target also play a 7

large role in the veracity of the collected information. These parameters were studied in the 8

laboratory at the University of New Hampshire. These laboratory experiments showed the DIC 9

was most reliability when a 90-degree angle was maintained between the camera set-up and the 10

target. This configuration can be difficult to achieve during a bridge test. This information is 11

used to evaluate measurement collected with configurations possible given the bridge site 12

geometry. 13

1

FIGURE 5 Comparison of deflection measurement collected via LVDT and DIC for the 2

Vernon Ave. Bridge. 3

Static Load Test 4

The September 3, 2009 load test used a 72-kip (320 kN) dump truck driving across the bridge 5

several times at various locations and speeds. Two different types of pseudo-static tests were 6

conducted, a crawl speed and stop location. In the crawl speed tests, the truck simply drove 7

across the bridge at low speed, approximately 4 mph. In the stop location tests, the truck stop for 8

30-seconds as each of the 14 stations along the length of the bridge, marked on Figure 5. For 9

each test type, the truck followed three different lanes down the length of the bridge: one on the 10

west side, one in the center, and one on the east side with 14 stations or stop locations, as shown 11

in Figure 5. 12

For each test, 200 Hz data acquisition was initiated when the truck was at station #0 and 13

continued unti lthe truck was past station #14, as marked on Figure 6. During the stop location 14

test, the truck stopped for approximately 10 seconds at each station before traveling to the next 15

one to allow any dynamic excitation to dissipate. After 10 seconds at the final stop (#14), the 16

truck rolled forward until the rear axles were clear of the bridge. In addition, to the installed 17

instrumention, two DIC set ups were used to collect deflection at the mid-span (station #8) and 18

south span (approximately station #4). 19

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

0 30 60 90 120 150 180

Dis

pla

cem

ent

(in

)

Time (s)

LVDT - Digital Image Correlation Verification for the Vernon

Ave Bridge

Beam 2 LVDT

Low Speed DIC

Mid-Span DIC 1

The cameras were mounted on separate tripods about t10-feet (3.04 m) apart to allow enough 2

separation distance to get data in all three directions. The camera set up was located 3

approximately 50-feet (15.2 m) from and 10-12 feet (3.04- 3.66 m) below the face of the girder. 4

Although the girder was in the shadow of the concrete deck above, the ambient light was 5

sufficient to acquire data without lamps being used. There are two speckle patterns on the center 6

span of the bridge. The smaller pattern was used for given the level of deflection expected and 7

the distance of the cameras from the target area, as shown in Figure 7. 8

FIGURE 6 Plan View of the VAB with Load Test Paths.

1

FIGURE 7 Mid-Span DIC Setup on the VAB. 2

South Span DIC 3

The measurement location at the south span was over dry land, therefore the cameras very 4

setup approximately 10 feet (3.04 m) from the target along the same horizontal line of sight. The 5

cameras were set up on the same tripod about 18 inches (45.7 cm) apart, still allowing for three-6

dimensional data acquisition, see Figure 8. Given the close proximity of the DIC set up, chalk 7

was used for the speckle pattern. The white chalk provided a high-contract pattern on the 8

weathering steel surface. 9

10

FIGURE 8 DIC Setup at the South Span of the VAB 11

Digital Image Correlation Results 1

As part of the project, a structural model was created in SAP2000®, see Figure 9. This model 2

was calibrated with respect to the collected strain data (Lefebvre, 2010). The model updates for 3

the calibration included using concrete material properties from test cylinders collected during 4

the deck pour, linear springs to represent the neoprene bearing pad boundary conditions, the 5

inclusion of the safety curb on the east side of the bridge and the inclusion of reinforcing steel in 6

the concrete deck. Each one of these updated were based on engineering judgment and 7

observations made during the bridge construction. 8

9

FIGURE 9 Structural Model of the VAB created in SAP2000. 10

The collected deflection measurements were evaluated with respect to the predicted 11

deflection from the structural model. Given that both DIC set ups were located on the east side 12

of the bridge, only the results from the western-most load path, Path 1, are included in this paper, 13

see Figure 10. The structural model was calibrated using strain data crawl speed tests from all 14

three truck load lanes. 15

16

17

FIGURE 10 SAP2000 Structural Model of the VAB with Truck Load 18

Deflection measurements are reported directly on the SAP2000® GUI, which made for a 19

quick comparison of modeled to measured data. The comparison between DIC deflections and 20

modeled deflections show a reasonable correlation. The comparison also improves with the 1

updated model. Figure 11 and Figure 12 give a comparison between collected vertical 2

deflections collected during the Lane 1 truck stop test using DIC and both the original and final 3

updated model for the mid-span and south span respectively. Each truck stop test took 4

approximately six minutes (360 seconds) including 45 seconds of ambient data prior to the truck 5

driving onto the bridge. This time is shown on the x-axis for Figures 11 and 12. In the collected 6

data, the “plateaus” of data in the time domain are visible, evidence of the truck stops. There is a 7

single deflection value for each truck stop from the structural models, original and updated. 8

9

FIGURE 11 Comparison of Vertical Deflections near midspan of west exterior girder for 10

truck in Lane 1 11

12

13

FIGURE 12 Comparison of Vertical Deflections in south span of west exterior girder for 14

truck in Lane 1 15

-0.25

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0

0.05

0 50 100 150 200 250 300 350

De

fle

cti

on

(in

)

Time (s)

Vertical Deflection of Vernon Ave Bridge near MidspanTruck in West Lane

DIC Data

Original Model

Updated Model

-0.05

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0

0.01

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0 50 100 150 200 250 300 350

De

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in)

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Vertical Deflection of Vernon Ave Bridge at South SpanTruck in West Lane

DIC Data

Original Model

Updated Model

Remarks 1

These collected results are encouraging. The DIC system is clearly able to collected data that 2

is reflective of the predicted bridge behavior. The accuracy of this collected is far less than the 3

accuracy of DIC when used in a controlled laboratory setting. Additional field applications of 4

this DIC system are planned for the VAB as well as multiple bridges in the Northeast United 5

States during summer 2010. 6

CONCLUSIONS 7

Digital image correlation has many benefits that make it an attractive choice for a method 8

of non-contact structural health monitoring. DIC requires a relatively short period of time for 9

setup and break down and can easily be operated by a crew of two without any special 10

equipment. Another advantage is that DIC can be used without the interruption of traffic. As 11

methods are improved upon DIC deflection data may be used for the calibration of computer 12

models. Deflection data is a measure of the global response of the bridge. Therefore the 13

information collected is highly valuable in the structural model calibration process. This type of 14

global measurement limits the local behavior concerns that are associated with other traditional 15

SHM measurements such as strain. 16

Structural modeling can be combined with SHM for many purposes, including 17

instrumentation placement, data quality assessment, design verification, bridge assessment and 18

management, and load rating. Load ratings based on calibrated structural models can 19

significantly mitigate the level of subjectivity that exists in conventional load ratings (Yost et. al, 20

2005). Global measurements of bridge performance, such as deflection, can be used to calibrate 21

a structural model and create a structural baseline model. This structural baseline model can be 22

used as a signature to capture changes in the bridge structural performance. The ease of 23

application of DIC makes this technology very attractive for the periodic noncontact capture of 24

bridge performance. 25

26

ACKNOWLEDGEMENTS 27

This research was partially supported by the CAREER program at the NSF (Grant 28

#0644683), the Partnerships for Innovation program at the NSF (Grant #0650258), the Major 29

Research Instrumentation Program (Grant #0821517). The authors would like to thank the 30

Geocomp Corporation for their efforts installing the instrumentation system, the Massachusetts 31

Highway Department for allowing us to work on this project, and the Massachusetts Department 32

of Transportation for their continued support with the instrumentation system and load testing. 33

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