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Special Issue Article
Vision-based monitoring system for
evaluating cable tensile forces on a
cable-stayed bridge
Structural Health Monitoring
12(5-6) 440–456
Ó The Author(s) 2013
Reprints and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI: 10.1177/1475921713500513
shm.sagepub.com
Sung-Wan Kim1, Bub-Gyu Jeon1, Nam-Sik Kim1 and Jong-Chil Park2
Abstract
Because of the characteristics of cable-supported bridges, the cable tensile force is considered a critical item in their
maintenance. In particular, because the evaluation of the cable tensile force in a cable-stayed bridge is essential for understanding the general status of the structural system, identifying the initial values of this force in the construction of a
bridge and then accurately predicting and comparing its estimated values during traffic use are very important tasks for
the maintenance of a cable-stayed bridge. Therefore, in this study, a vision-based monitoring system that utilizes an image
processing technique was developed to estimate the tensile force of stay cables during traffic use. A remotely controllable pan-tilt drive was installed in the developed vision-based monitoring system to estimate the forces on multiple cables
using a single system. The use of a 203 electric zoom lens made it possible to achieve sufficient resolution to remotely
derive the dynamic characteristics of the stay cables.
Keywords
Cable-stayed bridge, cable tensile force, vision-based monitoring system, image processing technique
Introduction
With the continuous development of construction
materials and techniques, the construction of long-span
bridges in and outside Korea is increasing. Among such
bridges is the cable-stayed bridge, a high-order statically indeterminate structure that controls the stresses
on the pylons and girders by adjusting the tensile forces
of multiple cables. It is presently being applied to many
medium- to long-span bridges because it allows various
designs and is aesthetically pleasing. Cable-stayed
bridges can be monitored by continuously calculating
the cable tensile forces during the bridges’ construction
and traffic use. Thus, a maintenance plan for the longterm evaluation of the cable behaviors by installing a
monitoring system for the stay cables has been established and is being carried out.1,2,12
An accurate cable tensile force estimation method is
required to efficiently manage the tensile forces of the
stay cables during traffic use, and many studies on this
have been conducted.3–7 The tensile force estimation
methods that are frequently being used these days can
be largely divided into static methods of direct measurement and dynamic methods for indirect estimation
using the shape condition of the cable and the
measured natural frequency. The static methods
include the load cell method, hydraulic jack method,
and electromagnetic (EM) sensor method, which is currently being actively researched and applied.8,9 In the
load cell method, a load cell is attached to the tip of a
cable to instantly check the cable tensile force. This
method has the advantage of being more accurate than
the other methods, but it is mainly used during the construction of stay cables, because it is not cost-effective
to apply such a direct method in the maintenance stage
of cable-stayed bridges. The hydraulic jack method
measures the pressure on a hydraulic jack installed at
the end of a cable to measure the cable tensile force.
The hydraulic jack method is more economical than
1
Department of Civil and Environmental Engineering, Pusan National
University, Busan, Republic of Korea
2
Infrastructure Research Division, Expressway & Transportation
Research Institute Korea Expressway Corporation, Hwaseong-si,
Republic of Korea
Corresponding author:
Nam-Sik Kim, Department of Civil and Environmental Engineering, Pusan
National University, 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735,
Republic of Korea.
Email: [email protected]
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Kim et al.
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the load cell method, but it has the disadvantages of
low accuracy and the need for a workspace to install a
hydraulic jack. The EM sensor method estimates the
tensile force by measuring the speed of the waves generated by EM induction. However, although it can estimate the tensile force of cables exposed to air, it cannot
estimate the tensile force of cables filled with grouting
or grease. The dynamic methods are used for cable
maintenance because they are faster and more economical than the static methods. The dynamic methods for
tensile force estimation use the natural frequency
derived from a low-frequency accelerometer for the
cable. However, cable-stayed bridges can be several
tens to hundreds of meters in length, and it is more difficult to obtain reliable data from longer cables.
Moreover, the cable installation requires large quantities of manpower and time because all the sensors and
the data acquisition (DAQ) logger must be connected
to one another. Moreover, monitoring all the cables in
a cable-stayed bridge requires a separate accelerometer
for each cable, and periodic maintenance work must be
performed because their life span is short (only 5 years)
as a result of being attached to the cables, which are
subject to constant vibration. Therefore, there is a need
for a method for estimating the dynamic characteristics
of remote cables without attaching sensors to them.
The typical noncontact methods that can be used to
estimate the dynamic characteristics of stay cables
include the laser Doppler effect method, global positioning system (GPS) method, and image processing
method. The laser Doppler effect method10 has a relatively high accuracy, but it has not been commercialized, because it requires as many sensors as cables. The
GPS method11 is not universally applicable for estimating the dynamic characteristics of stay cables during
traffic use because of the error in the signals and the
limitation of the sampling rate. In contrast, the image
processing method13–19 is appropriate for estimating
the dynamic characteristics of inaccessible stay cables
because it has a longer life and its price is reasonable
compared to methods that use acceleration sensors,
because it is a noncontact method.
In this study, a method for estimating the dynamic
characteristics of stay cables using an image processing
method was developed, along with a vision-based monitoring system to measure the dynamic characteristics
of remote stay cables by considering the convenience of
use and economic efficiency. The developed visionbased monitoring system uses a remotely controllable
pan-tilt drive to measure multiple cables using a single
system. Furthermore, a 203 remotely controllable
zoom lens was installed, with a resolution that enables
the estimation of the dynamic characteristics of remote
cables. To verify the validity of this method for measuring the dynamic characteristics of stay cables during
traffic use through the above-described vision-based
monitoring system, it was applied to a pylon of the
Busan–Geoje Fixed Link in South Korea, which is a
three-span steel-concrete composite cable-stayed bridge.
Vision-based monitoring system
The following are the minimum requirements for the
vision-based monitoring system used to estimate the
dynamic characteristics of stay cables during traffic use:
Images with a minimum resolution of 640 3 480
pixels;
A camera and system with a minimum sampling
rate of 60 frames per second (fps);
A camera housing that is waterproof, heatproof,
and dustproof;
Data transfer from the camera to the image storage
server over a distance of 100–500 m;
A remotely controllable pan-tilt drive;
A zoom lens and focus lens that are remotely
controllable.
In this study, a system that can satisfy the requirements for the estimation of the dynamic characteristics
of stay cables during traffic use was developed. Figure 1
shows the composition of the developed vision-based
monitoring system. In general, the image storage server
should be located in a place where there are dynamic
and static data loggers, for convenient maintenance.
Because the wire distance between the image storage
server and the camera is 100–500 m, optical cables and
converters were used for data transfer. Furthermore, a
waterproof, heatproof, and dustproof housing was
installed for the camera to allow system operation even
under bad weather conditions. To measure multiple
cables with a single camera, a remotely controllable
pan-tilt drive was installed, and a function that made it
possible to save 60 locations was added to the control
program for the long-term monitoring of the cables. A
203 electric zoom lens was installed to achieve the
required resolution for the remote cables. The control
signals for the pan-tilt drive and electric zoom lens were
transferred through RS485 cables and repeaters.
Table 1 lists the specifications of the components of the
vision-based monitoring system.
Tensile force estimation algorithm for stay
cable using image processing technique
Image processing technique
The digital image correlation (DIC) technique20–27 is
widely used to measure the displacement and deformation of structures. Its principle involves finding the
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442
Structural Health Monitoring 12(5-6)
Camera Housing
Tx Control Box
Tx Optical Repeter
Lens Zoom & Focus Control
(RS485 Cable)
Lens
Camera
Data Transmission
(1394a cable)
Pan-tilt Drive
Optical Cable
(Max. 500m)
Pan-tilt Control
(RS485 Cable)
Main Control PC
Rx Control
Rx Optical Repeter
Ethernet Card
Pan-Tilt Control
Data Transmission
RS485
Card
RS485 Converter
Pan-tilt, Lens Zoom
& Focus Control
Lens Zoom Control
Lens Focus Control
Figure 1. Composition of vision-based monitoring system.
Table 1. Specifications of components of vision-based monitoring system.
Camera
Nile (IMX-5040FT)
Image sensor: 1/3$ CCD ICX424 AL/AQ
Active picture element: 640 3 480
Maximum frame rate: 86 fps
Video output: digital—12-bit camera link
Scanning system: progressive scan
Complementary metal-oxide-semiconductor
Dimension: 40 (W) 3 40 (H) 3 37 (D) mm
Image pickup device: 1/2$, 1/3$
Iris: auto iris (DC)
Lends mount: C-mount, magnification 203
Focal distance: 12–240 mm
Focus: 1.2 M ~ inf
Dimension: 116 (W) 3 135 (H) 3 223.5 (D) mm
Lends
Samsung Techwin (SLA-12240)
Material: aluminum casting, plate
Rotation angle: pan 0° to 350°, tilt –90° to + 20°6 5°
Receiver function:
- AUX1 ~ AUX5 (light, wiper, pump, heater)
- Camera control (zoom, focus)
- Pan tilt/camera preset
- Communication method: RS-485/RS-422
Dimension: 348 (W) 3 532 (H) 3 617 (L) mm
Pan-tilt drive and housing
YUSIN SYSTEM (EPT-6000s)
Speed: 400 Mbps (maximum)
Distance: up to 500 m
Power supply voltage: DC 8–40 V
Power supply current: 200 mA
Dimension: 100 3 70 3 25 (mm)
Optical converter
OPHIT (OFR-1394R)
Power supply voltage: DC 5 V
Power supply current: 600 mA
RS-232: DCE mode
485, 422: 4Pin terminal (4wire/2wire)
Speed: 2.5 Mbps (maximum)
Distance: up to 1.2 km
Dimension: 68 3 35 3 22 (mm)
RS485 repeater
Coms (485S/9P)
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Kim et al.
443
⎛ f ⎞
⎜ ⎟
⎝n⎠
2
slop = a
R( x – u, y – v)
b
T ( x, y )
n2
Figure 4. Tensile force calculation by vibration method.
Figure 2. Configuration for calculation of NCC coefficient.
deformation, a mathematical transform function25
between the deformed and undeformed images is found
through the coordinate change of the control point.
This transform function is used to rearrange the pixel
positions of the image with a deformation based on the
image with no deformation (Figure 3).
NCC: normalized cross correlation; ROI: region of interest.
Undeformed image
Control Point
Deformed image
x
Control Point
x′
x=
2
X
ai, j x9i y9j , y =
i, j = 0
y
y′
Figure 3. Control point coordinates of image before and after
deformation.
correlations between undeformed and deformed
images. This is a matching method for determining the
location of target window function T , which changes
over time within the pixel set of the region of interest
(ROI) window function R within two-dimensional
images. For example, as shown in Figure 2, assuming
that the target window is a 1 3 1-unit pixel and the
ROI window is a 3 3 3-pixel set, target window t(x, y)
in the ROI window moves by pixel, and the target
moves a total of nine times for matching through the
coordinate changes of (x u, y v). The optimal
matching position of the target window in the ROI window pixel set is calculated using the normalized cross
correlation (NCC) coefficient in equation (1). The computation to determine the NCC coefficient is carried
out within the common coordinates of R and T .
P T (x, y) T R(x u, y v) Ru, v
gu, v = qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2ffi
2 P T
(x,
y)
T
R(x
u,
y
v)
R
u,
v
xy
xy
ð1Þ
The structure is subjected to displacement and deformation by external factors. To correct the geometric
distortions caused by this displacement and
2
X
bi, j x9i y9j (i + j\2)
ð2Þ
i, j = 0
In this study, the quadratic polynomial function in
equation (2) was used to correct the geometric distortions caused by the displacement and deformation. To
obtain a total of 12 coefficient values, the coordinates
(x, y) of a minimum of six pixels with no movement and
the coordinates (x9, y9) of a minimum of six pixels with
movement, which indicate the movements of objects,
were used. These coordinates were obtained by marking
control points on the objects. The coordinates of these
control points were used to obtain the transform function that rearranges the pixel coordinates of the image
with a deformation based on the image with no deformation. In this study, subpixels were calculated by rearranging the positions using the coordinates of nine
pixels in the deformed and undeformed images.
Vibration method
In the vibration method, a sensor is attached to measure the dynamic response of a cable. Dynamic characteristics such as the natural frequency and mode shape
of the cable are acquired from the dynamic response of
the cable, to calculate the cable tensile force. This study
applied the method suggested by Shimada28 for calculating the tensile force: when the cable sag is not large,
the natural frequencies in each mode are extracted, as
shown in Figure 4, and each of them is divided by the
mode number, after which the primary regression line
for the square of this value ((fn =n)2 ) and the square of
the corresponding mode number (n2 ) is drawn. The
cable tensile force can be estimated using equation (4)
after finding b in the primary linear regression in
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Structural Health Monitoring 12(5-6)
Figure 5. Dynamic characteristics estimation algorithm for stay cable using image processing technique.
ROI: region of interest; PSD: power spectral density.
equation (3). Here, T and EI are the tensile
force and flexural rigidity of the cable, respectively, is
the weight per unit length, and L is the length of the
cable.
2
fn
Tg
n2 p2 EIg
=
+
= b + a n2
4wL2
4wL4
n
ð3Þ
T = 4(w=g)L2 b
ð4Þ
It is well known that the natural frequency of the first
mode measured in the field is usually higher than estimated because of the effective length. Therefore, in this
study, the natural frequency of the first mode is
excluded while calculating the tensile force of the stay
cable using the vibration method.
Summary of algorithm
Figure 5 shows the tensile force estimation algorithm
using the image processing technique, which consists of
a total of seven steps. The acquired video file is converted into image files, which are arranged in the order
of time, and a control point is specified, which is the
point at which the dynamic response of the stay cable is
to be determined from the initial images. To efficiently
set the ROI window, the correlation size is determined
by calculating the maximum movement of the stay
cable, and the NCC is calculated to provide information about the optimal matching point of the target
window, including the control point, to the ROI window. To correct the geometric distortion generated by
displacement and deformation, the pixel positions are
rearranged using a quadratic polynomial function, and
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Kim et al.
445
Figure 6. Dynamic characteristics estimation algorithm for stay cable developed in this study using MATLAB 7.0.
PSD: power spectral density.
the subpixels are calculated. The power spectral density
(PSD) function is applied to the analyzed displacement
response to extract the natural frequency in each mode,
and the extracted natural frequency in each mode is
applied to the vibration method to estimate the tensile
force. In this study, MATLAB7.0 was used to develop
an automated program using the dynamic characteristics estimation algorithm for a stay cable as explained
above, as shown in Figure 6.
Verification of vision-based monitoring
system through model experiment
Experiment outline
In this study, an experimental model with three modes
(first mode: 0.54 Hz; second mode: 4.97 Hz; and third
mode: 11.42 Hz) was produced to measure the displacement response of each target and to verify the validity
of the estimated displacement response through the
vision-based monitoring system. Figure 7 shows the
experimental model that was captured by the vision-
based monitoring system, and Table 2 lists the specifications of the experimental model.
To measure the dynamic characteristics of the
model, acceleration sensors and targets were installed
at the masses (Mass 1, Mass 2, and Mass 3), as shown
in Figure 8. To verify the accuracy of the displacement
response that was estimated by applying the image processing technique to the image captured through the
vision-based monitoring system, a laser triangulation
meter was installed at a position where the distorted
image was large at the center of the camera image.
In this experiment, 640 3 480 pixel images were captured at 60 fps using the vision-based monitoring system. The acceleration sensors (PCB 393BO4) and laser
triangulation meter took simultaneous measurements
at a sampling rate of 100 Hz. Before the start of the
experiment, the pixel value corresponding to the length
of the target was measured. Because the target size was
82 mm and the corresponding number of pixels was 34,
the resolution of a unit pixel was 2.41 mm.
The image processing technique was applied to estimate the displacement responses to the control point of
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Structural Health Monitoring 12(5-6)
Horizontal-Displacement [pixel]
446
9
Vision-based Monitoting System
6
3
0
-3
-6
-9
0
10
20
30
40
50
60
Figure 7. Experimental model captured by vision-based
monitoring system.
Horizontal-Displacement [pixel]
Time [sec]
9
Laser Triangulation Meter
6
3
0
-3
-6
-9
0
10
20
30
40
50
60
Time [sec]
Figure 9. Comparison of displacement responses for initial
displacement (6 mm).
Percent error =
n
X
(dm dc )2 =
i=1
n
X
(dm )2
ð7Þ
i=1
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n
X
RMS error =
(dm dc )2 =n
ð8Þ
i=1
Figure 8. Attachment positions for sensors and targets.
Table 2. Specifications of experimental model.
Quantity
Unit
Value
Young’s modulus
Density
Length
Thickness
Lumped mass
N/mm2
ton/m3
mm
mm
kg
kg
kg
2.1 3 105
7.85
1320
5
10
10
5
Mass 1
Mass 2
Mass 3
each target using the vision-based monitoring system.
To verify the accuracy and precision of the estimated
displacement responses, error analyses were performed
with the percent error shown in equation (7), the root
mean square (RMS) error shown in equation (8), and
the system error shown in equation (9).
SYSTEM error = RMS error=Max: displacement ð9Þ
Here, n is the number of measured data points, dm is
the displacement response measured by the laser triangulation meter, and dc is the displacement response estimated by the image processing technique through the
vision-based monitoring system.
Verifications of vision-based monitoring system
To verify the vision-based monitoring system, three
experiments were performed with the camera image
center fixed at Mass 3. Free vibration tests were
conducted with initial displacements of 10, 8, and 6
mm at Mass 3, and the displacement responses measured by the laser triangulation meter were compared
with those estimated by applying the image processing
technique.
Figure 9 compares the displacement response estimated by the image processing algorithm using the
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Initial
displacement
(mm)
Percent
error
(%)
RMS
error
(mm)
System
error
(%)
10
8
6
0.49
0.37
0.41
0.41
0.26
0.23
3.88
3.30
3.71
RMS: root mean square.
Horizontal-Displacement [pixel]
Table 3. Error analysis for initial displacement.
12
Vision-based Monitoting System
8
4
0
-4
-8
-12
0
Table 4. Position of laser triangulation meter relative to
camera image center position.
Laser triangulation
meters’ position
Mass 1
Mass 2
Mass 3
Mass 3
Mass 1
Mass 1
vision-based monitoring system for the free vibration
experiment case with the initial displacement of 6 mm
with the displacement response measured by the laser
triangulation meter. In Table 3, the rate of the percent
error for the displacement response is within 0.5%, but
the RMS error increases depending on the initial displacement. However, because the system error is almost
constant, the error seems to be very small. Therefore,
the displacement responses estimated by the image processing algorithm using the vision-based monitoring
system were found to be reliable.
Verification of dynamic characteristics of
experimental model using vision-based monitoring
system
To verify the dynamic characteristics of the experiment
model using the vision-based monitoring system, Mass
2 and Mass 3 were alternately excited to generate the
first, second, and third modes. Three experiments were
performed while varying the positions of the camera
image center and laser triangulation meter, as shown in
Table 4.
Figure 10 compares the displacement response estimated by the image processing technique using the
vision-based monitoring system with the displacement
response measured by the laser triangulation meter at
Mass 1 in the experiment case where the camera image
center was located at Mass 3. The noise band of the
displacement response measured by the laser triangulation meter was 60.8 mm, whereas the noise band of
the displacement response estimated by the image processing technique using the vision-based monitoring
system was 60.3 mm. Thus, even though the overall
20
30
40
50
Time [sec]
Horizontal-Displacement [pixel]
Camera-centered
projection
10
12
Laser Triangulation Meter
8
4
0
-4
-8
-12
0
10
20
30
40
50
Time [sec]
Figure 10. Comparison of displacement responses at Mass 1.
shapes matched, the displacement responses of the
high-frequency component appeared to have different
shapes. Figure 11 shows the PSD function derived from
the responses in the experiment case where the camera
image center was located at Mass 3. The accuracy of
the natural frequency in each mode (first mode: 0.54
Hz; second mode: 4.97 Hz; and third mode: 11.32 Hz)
was within 60.1%. In Table 5, the rate of the percent
error for the displacement responses was within 1.5%,
and the RMS error was within about 1 mm, both of
which are very small values. Therefore, the displacement response results estimated from the image processing technique using the vision-based monitoring
system were found to be reliable.
Application of vision-based monitoring
system for stay cable monitoring
Outline of example bridge
As shown in Figure 12, the installed bridge crosses from
Jeodo to Geojedo as part of the Busan–Geoje connecting road. It consists of an approach bridge (a steelconcrete composite girder bridge), a secondary reserve
sea road bridge (a three-pylon cable-stayed bridge), a
main reserve sea road bridge (a two-pylon cable-stayed
bridge), and Jeodo Bridge (a steel-concrete composite
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Structural Health Monitoring 12(5-6)
girder bridge). Its design speed is 80 km/h, it has four
lanes, and its total length is about 3.7 km.
The bridge where the vision-based monitoring system was installed was a three-span steel composite
cable-stayed bridge with a length of 222 + 475 + 222
= 919 m. The pylon has a curved diamond shape, the
material is concrete, and the foundation is a caisson.
The girder is composed of a steel plate girder and concrete slab, and 80 two-side-support-type cables are
installed.
0.1
Vision-based Monitoting System[Mass1]
Power Amplitude
0.01
1E-3 0.54
1E-4
1E-5
4.97
1E-6
11.31
1E-7
0
3
6
9
12
15
Frequency [Hz]
Vision-based monitoring system
0.01
Laser Triangulation Meters[Mass1]
Power Amplitude
1E-3
0.54
1E-4
1E-5
1E-6
4.97
1E-7
0
3
6
9
12
15
Frequency [Hz]
1E-5
Power Amplitude
Accelerometer[Mass1]
1E-6
11.32
1E-7
4.97
1E-8 0.54
1E-9
0
3
6
9
12
15
Frequency [Hz]
Figure 11. Comparison of PSD function of response obtained
at Mass 1.
PSD: power spectral density.
Table 5. Error analysis of displacement response relative to
camera image center.
Camera-centered
projection
Percent
error (%)
RMS
error (mm)
Mass 1
Mass 2
Mass 3
0.27
1.47
1.10
0.86
0.47
0.38
RMS: root mean square.
Figure 13 shows the image transmission of the visionbased monitoring system. The distance between the
image storage server and the camera was about 150 m,
and data transmission was carried out through optical
cables and converters. In addition, the pan-tilt drive,
zoom lens, and lens focus were controlled by sending
control signals through an RS485 cable and repeater.
The image storage server could be remotely controlled
in real time through LAN communication from the
laboratory. Figure 14 shows the camera, housing, and
TX control box installed on crossbeam 2. Figure 15
shows the image storage server and an optical cable
installed on crossbeam 1 as well as LAN communication for remote control from the laboratory. Figure 16
shows the RX control installed in the image storage
server, an optical converter, and an RS485 repeater.
Figure 17 shows the ImCam image storage program
supplied by the camera company. The control program
for the pan-tilt drive, electric zoom lens, and lens focus
was developed in this study using Visual C + .
Estimation of tensile force of stay cable using visionbased monitoring system
To verify the validity of the vision-based monitoring
system for estimating the dynamic characteristics of
stay cables, an experiment was conducted under an
ambient vibration condition in the elliptical section
shown in Figure 18. In Figure 18, looking at Busan
from the P6 pylon, the cable on the left is called ‘‘BL’’
and the cable on the right is called ‘‘BR.’’ The dynamic
responses of the cables were estimated using the visionbased monitoring system for 36 (BL and BR 03-20) of
the total of 40 cables in the direction of Busan from the
P6 pylon. Four cables (BL and BR 01-02) could not be
measured because they could not be seen owing to the
installation location of the vision-based monitoring system. Furthermore, because it was possible to use the
sensor to estimate the dynamic responses through the
cable shapes without installing a target on the cable,29
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Figure 12. Busan–Geoje fixed link.
Figure 13. Image transmission of vision-based monitoring system.
Figure 14. Camera, housing, and TX control box installed on crossbeam 2.
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Structural Health Monitoring 12(5-6)
Figure 15. Image storage server and optical cable installed on crossbeam 1.
Figure 16. RX control installed in image storage server.
the cable shape in the acquired images was set as the
target window when the image processing technique
was applied. In Figure 18, the acceleration sensors for
long-term monitoring in the elliptical section were
installed only for the BR 05, BR 15, and BR 20 cables,
whereas no sensor was installed for the BL cable. The
vision-based monitoring system captured 640 3 480
pixel images at 60 fps with a frequency resolution Df of
0.01465 Hz. The acceleration sensors for the long-term
monitoring system were used to conduct measurements
at a sampling rate of 100 Hz and a frequency resolution
of Df = 0:00367 Hz. The natural frequencies in each
mode and the tensile forces estimated using the visionbased monitoring system were compared. Table 6
lists the specifications of the cables where the acceleration sensors were installed for long-term cable
monitoring.
Figure 19 shows the images captured by the visionbased monitoring system. Figure 19(a) shows the BR
05 cable, and Figure 19(b) shows the BR 18, 19, and 20
cables from the right.
Figures 20 to 22 show the acceleration responses of
the cables where the acceleration sensors were installed
for long-term monitoring, along with the dynamic
responses estimated by the image processing technique
using the vision-based monitoring system. Figures 23
to 25 show the PSD functions for the responses. It was
found that the PSD function for the dynamic responses
estimated by the image processing technique using the
vision-based monitoring system could estimate up to
the fifth mode because of the small resolution for the
dynamic responses of the high-frequency components.
The natural frequencies in each mode and the tensile
forces of the stay cables are listed in Table 7. The errors
for the natural frequencies in each mode and the tensile
forces, which were measured using the acceleration sensors for the long-term monitoring and by the image
processing technique using the vision-based monitoring
system, were very small (below 61%). Therefore, the
dynamic response results estimated from the image processing technique using the vision-based monitoring
system were found to be reliable.
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Figure 17. (a) Image storage program and (b) pan-tilt and zoom lens control program.
BR05
To Geoje
P5
BR15 BR20
P6
Figure 18. Measurement locations.
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To Busan
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Structural Health Monitoring 12(5-6)
Table 6. Specifications of cables where acceleration sensors were installed for long-term monitoring.
Cable
Effective length (m)
Young’s modulus (kN/m2)
Area (m2)
Unit weight (kN/m)
Design tension (kN)
BR05
BR15
BR20
10.522
10.726
24.938
1.95E + 8
4.05E23
7.2E23
13.65E23
0.371
0.648
1.231
2011
4437
4558
Figure 19. Images of BR 05, 18, 19, and 20 cables captured by vision-based monitoring system: (a) BL 05 cable and (b) BR 18, 19,
and 20 cables from right.
0.2
0.2
Accelerometer
Acceleration [m/s2]
Acceleration [m/s2]
Accelerometer
0.1
0.0
-0.1
0.1
0.0
-0.1
-0.2
-0.2
0
30
60
90
120
0
150
30
Horizontal-Displacement [pixel]
Horizontal-Displacement [pixel]
Vision-based Monitoring System
Max. Disp : 0.78mm (1pixel = 0.36 mm)
3
2
1
0
-1
0
30
60
90
120
150
120
150
2.0
Vision-based Monitoring System
Max. Disp : 2.22mm (1pixel = 1.74 mm)
1.5
1.0
0.5
0.0
-0.5
-1.0
0
30
60
90
Time [sec]
Time [sec]
Figure 20. Responses to BR 05 cable.
90
Time [sec]
Time [sec]
4
60
Figure 21. Responses to BR 15 cable.
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120
150
Kim et al.
453
1E-8
0.2
Accelerometer
0.1
Power Amplitude
Acceleration [m/s2]
Accelerometer
0.0
-0.1
1E-9
1.98
1E-10
3.30
2.65
3.96
4.62
1.31
1E-11
0.66
-0.2
1E-12
-0.3
0
30
60
90
120
0
150
1
2
Time [sec]
4
5
1E-5
2.0
Vision-based Monitoring System
Vision-based Monitoring System
Max. Disp : 3.41mm (1pixel = 3.50 mm)
1.5
Power Amplitude
Horizontal-Displacement [pixel]
3
Frequency [Hz]
1.0
0.5
0.0
-0.5
1E-6
0.66
1E-7
1.33
1E-8
1.98
-1.0
2.64
3.00
1E-9
-1.5
0
0
30
60
90
120
1
2
150
3
4
5
Frequency [Hz]
Time [sec]
Figure 24. PSD function for responses of BR 15 cable.
Figure 22. Responses to BR 20 cable.
PSD: power spectral density.
1E-8
1E-8
1E-9
Accelerometer
Power Amplitude
Power Amplitude
Accelerometer
2.50
3.76
1.26
1E-10
4.98
6.24
1E-11
1E-9
1.18 1.96 2.362.75 3.13
0.78
1.57
3.54
0.43
1E-10
1E-11
1E-12
1E-13
1E-12
0
2
4
Frequency [Hz]
6
8
0
1
3
4
5
1E-5
1E-5
Vision-based Monitoring System
1.25
Power Amplitude
Vision-based Monitoring System
Power Amplitude
2
Frequency [Hz]
2.50
1E-6
1E-7
3.74
1E-8
2
4
0.78
1E-7
1.96
Frequency [Hz]
1.17 1.57
6.25
6
Figure 23. PSD function for responses of BR 05 cable.
PSD: power spectral density.
0.39
1E-8
4.99
0
1E-6
8
0
1
2
3
4
Frequency [Hz]
Figure 25. PSD function for responses of BR 20 cable.
PSD: power spectral density.
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5
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Structural Health Monitoring 12(5-6)
Table 7. Comparison of natural frequencies in each mode and tensile forces.
Cable
BR05
BR15
BR20
Sensor
Frequencies (Hz)
Accelerometer
Vision-based monitoring system
Accelerometer
Vision-based monitoring system
Accelerometer
Vision-based monitoring system
1st
2nd
3rd
4th
5th
6th
7th
8th
9th
1.26
1.26
0.66
0.66
0.43
0.39
2.50
2.50
1.32
1.33
0.79
0.78
3.75
3.75
1.98
1.98
1.18
1.17
4.99
4.99
2.65
2.64
1.57
1.57
6.23
6.25
3.30
3.30
1.96
1.96
7.40
–
3.96
–
2.36
–
–
–
4.62
–
2.75
–
10.04
–
5.30
–
3.13
–
–
–
5.95
–
3.55
–
Tension
(kN)
Error
(%)
2063
2043
4355
4358
4442
4413
0.97
0.07
0.66
Table 8. Comparison of tensile forces for BL cables.
Cable
BL03
BL04
BL05
BL06
BL07
BL08
BL09
BL10
BL11
BL12
BL13
BL14
BL15
BL16
BL17
BL18
BL19
BL20
Effective
length (m)
Distance
(m)
Frequencies (Hz)
Tension (kN)
1st
2nd
3rd
4th
5th
Vision-based
monitoring
system
Design
77.33
84.92
93.04
101.68
110.91
120.61
130.62
140.86
151.40
162.10
172.97
183.96
195.08
206.29
217.60
228.91
235.79
240.01
9
15
21
27
33
39
45
51
57
63
69
75
81
87
93
99
105
111
1.57
1.44
1.23
1.12
1.02
0.99
0.90
0.81
0.66
0.60
0.61
0.67
0.66
0.62
0.60
0.43
0.40
0.39
3.11
2.83
2.48
2.25
2.05
1.98
1.80
1.61
1.34
1.20
1.21
1.35
1.33
1.27
1.18
0.86
0.81
0.77
4.67
4.25
3.67
3.38
3.07
2.96
2.71
2.41
2.01
1.79
1.83
2.03
1.99
1.91
1.77
1.30
1.22
1.15
–
5.67
4.90
4.50
4.09
3.94
3.60
3.21
2.65
2.39
2.40
2.70
2.66
2.55
2.37
1.73
–
–
–
–
6.12
5.62
5.10
4.93
4.50
4.01
3.36
2.99
3.01
3.37
3.32
3.16
2.96
–
–
–
2105
2111
1995
2129
2311
2679
2839
3055
3323
3291
3852
4096
4448
4818
4878
4179
4314
4353
2087
2042
2003
2120
2361
2691
2828
3045
3388
3289
3859
4109
4427
4832
4881
4288
4443
4557
Tables 8 and 9 compare the natural frequencies in
each mode and the tensile forces, which were estimated
by the image processing technique using the visionbased monitoring system for the BL and BR 03-20
cables, with the design tensile force. The errors in the
tensile force estimated by the image processing technique using the vision-based monitoring system relative
to the design tensile force were very small (below
65%). Therefore, the system for noncontact measurements that was developed in this study could be used to
identify the dynamic characteristics of multiple stay
cables using a single system.
Conclusion
In this study, a noncontact dynamic response measurement method and system using an image processing
technique based on a vision-based monitoring system
Error (%)
0.86
3.38
0.40
0.42
2.12
0.45
0.39
0.33
1.92
0.06
0.18
0.32
0.47
0.29
0.06
2.54
2.90
4.48
were proposed as an appropriate method for the longterm monitoring of stay cables.
A free vibration experiment on an experimental
model showed that the errors in the displacement
responses estimated by the image processing algorithm
using the vision-based monitoring system relative to
the displacement responses measured by a laser triangulation meter were very small. Thus, the displacement
responses estimated by the image processing technique
using the vision-based monitoring system were reliable.
Furthermore, the errors in the natural frequencies in
each mode, which were obtained using the image processing data, relative to those obtained from the acceleration sensors, were within 1%, thus confirming the
validity of the image processing data.
The vision-based monitoring system was developed
to monitor stay cables and the natural frequencies in
each mode, along with the tensile forces, which were
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Kim et al.
455
Table 9. Comparison of tensile forces for BR cables.
Cable
BR03
BR04
BR05
BR06
BR07
BR08
BR09
BR10
BR11
BR12
BR13
BR14
BR15
BR16
BR17
BR18
BR19
BR20
Effective
length (m)
77.29
84.88
93.00
101.65
110.88
120.58
130.59
140.83
151.38
162.08
172.95
183.94
195.06
206.27
217.58
228.89
235.77
239.99
Distance
(m)
9
15
21
27
33
39
45
51
57
63
69
75
81
87
93
99
105
111
Frequencies (Hz)
Tension (kN)
Error
(%)
1st
2nd
3rd
4th
5th
Vision-based
Monitoring
System
Design
1.57
1.39
1.25
1.11
1.03
0.98
0.91
0.82
0.67
0.62
0.60
0.68
0.66
0.64
0.60
0.44
0.40
0.39
3.11
2.79
2.50
2.22
2.05
1.97
1.82
1.63
1.35
1.22
1.20
1.34
1.33
1.27
1.20
0.88
0.80
0.78
4.64
4.17
3.74
3.33
3.05
2.94
2.73
2.45
2.02
1.84
1.78
2.01
1.98
1.92
1.80
1.33
1.22
1.17
6.22
–
4.99
4.44
4.06
3.91
3.65
3.27
2.69
2.45
2.40
2.69
2.64
2.54
2.40
–
1.61
1.57
7.78
–
6.25
5.54
5.07
4.89
4.55
4.08
3.37
3.05
–
–
3.30
3.18
3.00
–
2.01
1.96
2123
2056
2035
2059
2305
2665
2898
3155
3374
3448
3656
4047
4367
4830
4950
4261
4401
4403
2094
2050
2011
2129
2371
2702
2840
3058
3404
3306
3875
4121
4437
4840
4886
4292
4445
4558
measured by acceleration sensors for long-term monitoring. The image processing technique showed high
accuracy, with a 61% error rate. Furthermore, the tensile forces estimated by the image processing method
using the vision-based monitoring system showed a
very small error (below 65%) relative to the design
tensile force. It was found that the dynamic characteristics could be estimated from the cable shapes without
installing targets when the vision-based monitoring system was used to monitor stay cables. Therefore, the
dynamic response measurement method using the imaging system and analysis method proposed in this study
was found to be applicable for monitoring stay cables
and capable of estimating the dynamic characteristics
of remote cables in an economical and efficient way.
In this study, a monitoring system for stay cables
using an imaging system was developed, which could
replace the conventional measurement sensors and
overcome their limitations (high price, fixed measurement, and short-term use). Furthermore, although the
current maintenance methods for stay cables vary by
bridge, the cable tensile forces are usually estimated in
biannual inspections. The development of an economical vision-based monitoring system could allow routine
inspections of stay cables. This study captured images
in daytime under good weather conditions using the
vision-based monitoring system. When estimating the
dynamic responses of the cable using the vision-based
monitoring system, however, there are difficulties in
1.39
0.29
1.19
3.27
2.79
1.37
2.06
3.18
0.89
4.29
5.65
1.79
1.57
0.20
1.30
0.72
0.99
3.40
estimating the cable dynamic responses due to problems such as the spreading and deterioration of the
obtained images in the case of fog, rainy day, night,
and backlight. Thus, in the future, an image filtering
algorithm will be applied to enable the estimation of
the dynamic characteristics of stay cables even under
bad weather conditions and at night.
Declaration of conflicting interests
The authors declare that there is no conflict of interest.
Funding
This work was supported by grants from the National
Construction and Transportation R&D Program (No.
08CTIPE01) and the Super Long Span Bridge Project of the
Construction Technology Innovation Program (CTIP).
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