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Structural dynamic displacement vision system using digital image processing
NDT&E International 44 (2011) 597–608
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NDT&E International journal homepage: www.elsevier.com/locate/ndteint
Structural dynamic displacement vision system using digital image processing Hyoung-Suk Choi a, Jin-Hwan Cheung a, Sang-Hyo Kim b, Jin-Hee Ahn b,n a b
Department of Civil Engineering, Pusan National University, Busan 609-735, Republic of Korea School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749, Republic of Korea
a r t i c l e i n f o
abstract
Article history: Received 5 November 2010 Received in revised form 9 June 2011 Accepted 12 June 2011 Available online 21 June 2011
This study introduces dynamic displacement vision system (DDVS), which is applicable for imaging unapproachable structures using a hand-held digital video camcorder and is more economical than the existing contact and contactless measurement methods of dynamic displacement and deformation. This proposed DDVS method is applied to the Region of Interest (ROI) resizing and coefficient updating at each time step to improve the accuracy of the measurement from the digital image. Thus, after evaluating the algorithms conducted in this study by the static and dynamic verification, the measurement’s usability by calculating the dynamic displacement of the masonry specimen, and the two-story steel frame specimen is evaluated under uniaxial seismic loading. The algorithm of the proposed method in this study, despite the relatively low resolution during frozen, slow, and seismic motions, has precision and usability that can replace the existing displacement transducer. Moreover, the method can be effectively applied to even fast behavior for multi-measurement positions like the seismic simulation test using large scale specimen. DDVS, using the consecutive images of the structures with an economic, hand-held digital video camcorder is a more economical displacement sensing concept than the existing contact and contactless measurement methods. & 2011 Elsevier Ltd. All rights reserved.
Keywords: Dynamic displacement vision system Dynamic displacement Camcorder Measurement techniques Shake table test Masonry wall Two-story steel frame
1. Introduction In various fields, such as the movie industry, the automation technology industry, and in medical practice the video camera is used for measuring an object’s shape and the changes to its deformation. This economical and effective technology to measure the displacements and deformation of objects is widely practiced as seen through vision systems or machine visions [2–6]. In dynamic engineering, modal analysis is commonly used to assess the dynamic systems and the method of using an optical camera is being considered effective in obtaining modal parameters. The previous works assess dynamic parameters by introducing real targets on the structure or by studying simple structures in ideal conditions [7–9]. Real-time displacement measurements have been proposed using texture recognition algorithms on a flexible bridge [10] and using light-emitting diode targets [1]. Non-target displacement measurements using the concept of optical flow is also introduced for vibration measurements [11]. Furthermore, the approach adopted Kanade–Lucas–Tomasi trackers, virtual sensors on the mechanical systems’ video from a high speed camera, which developed as a kind of noncontact and non-marker method [12].
n
Corresponding author. Tel.: þ82 2 2123 2804; fax: þ82 2 313 2804. E-mail address: [email protected] (J.-H. Ahn).
0963-8695/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ndteint.2011.06.003
Generally, dynamic behavior of large structure such as highrise buildings and long-span bridges is hard to measure because it is difficult to install the reference points for the displacement transducer. Despite that, the movements of such structures can be accurately measured by laser Doppler effects, GPS systems, and high-speed measurements with an infrared camera, the listed methods are not economical and are difficult to measure in terms of the dynamic displacement due to errors in the data and a restricted sampling rate [13–16]. Likewise, this problem arises in the shaking table test for a large specimen. Using the shaking table test to evaluate the dynamic behaviors of structural systems, the main measuring components are accelerations and displacements as members of the structural system. Generally, the accelerometer is attached to the structural specimen because acceleration is achieved as an absolute value. However, reference points are needed to measure its displacement at the outer parts or inner parts of the shaking table. High-capacity transducers are also needed to measure displacements of large scale specimens, which can make large movement at the top parts of the specimen. However, this can cause a difficulty in acquiring a suitable (effective) data resolution and it is not easy to install the highcapacity transducers with relatively long-length. Thus, installing the reference point at the inner parts of the shaking table is more usable and suitable for researchers. Fig. 1 shows an example of a reference frame to measure the displacement in the shaking table test. The reference frame
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2.1. Concept of DDVS
Fig. 1. Example of reference frame and high-rise specimen on the shake table (the reference frame is painted dark gray).
should have a high stiffness to measure the relative displacements of the specimen because it should move identically to the movements of the shaking table. However, it is difficult to fabricate the reference frame to satisfy the high stiffness conditions and it demands the financial support and effort of the researcher. If the constructed frame does not have sufficient stiffness or if the resonance of the frame occurs according to excitation, unexpected motion, and displacement could be incurred at the installed reference frame on the shake table and it could give inaccurate measurements. Therefore, this study proposes a digital video image resizing method to measure the dynamic displacement or mode shapes using an adjusted camera for replacing high-capacity transducers in the shaking table test of large scale civil and building structure specimens. The suggested algorithms are evaluated by static and dynamic verification. In this DDVS algorithm, the resized Region of Interest (ROI) of the acquired digital image is used and the coefficient, for calculating the relationship between pixel movements in the digital image and the moving distance of the real object, is updated at each time step to improve the precision of the measurement. After evaluating the usability of the displacement sensing by the rigid frame specimen, the dynamic displacement of the earthquake loaded masonry specimen, and the two-story steel frame specimen are measured as applications of this method.
The main concept of DDVS is to update the displacement converting coefficient at each frame to reduce the calculation errors and to resize the ROI with low resolution images to improve the calculation accuracy. Generally, when capturing a video image using a digital camcorder, geometric distortion from the camera lens may occur as shown in Fig. 2. This phenomenon, occurring due to the curve of the lens surface and the refraction of light caused by different refraction rates which is dependent on the choice of the lens, can be compensated for through various existing methods. However, such compensation methods may also have additional error from geometric calculation because the methods make numerical errors during the correction process. Thus, the proposed method recognizes the distortion caused by the camera lens but, for accuracy, the method analyzes the image without revision assuming the effect is very small in the ROI. The DDVS introduces a target, which has five circles on the plate as shown in the very top of Fig. 2. The DDVS applies mm/pixel coefficient (MPC) calculated by the relationship between real distance and pixel distance on the recorded video image of the circles on the target for each frame to the displacement conversion algorithm for compensating the distortion. For example, as shown in Fig. 2, assuming the target plate has moved from the 1st to nth position horizontally, the displacement of the real movement is relatively small, and that the captured motion is slower than the recording frequency. The relationship between the incurred displacement, L1, and the projected curve of the image, L2, can be regulated by compensating with a cumulative updated coefficient from relationship between a and a0 or b and b0 at each frame and the error can be reduced in comparison to displacements that are derived directly from the relationship between L1 and L2. Commonly, the purpose of setting up the ROI during image processing is to improve the calculation speed and to manage memory usage. Therefore, existing method only considering applying the digital image correlation with the ROI to the minimum area including the target and the ROI is moved in predicting the next location. Such a set-up leads to calculating the relative displacement according to the change of target in terms of time step and accumulating errors. Thus, this study minimizes the errors by constructing absolute coordinates from applying the ROI to the identical location of the whole frame of filmed video and by tracking the location of the target in each time step.
2. Concept and algorithms of DDVS The video camera is used for motion tracking or recognition using the vision processing records with high resolution and high frame-rate. However, this video recording is limited by recording time and place because it is affected by light intensity, focus, and the video camera’s memory. Also, the high-performance video camera for recording is too much expensive and specific equipment such as lighting is needed to record the image in some cases. Aptly, the hand-held digital video camcorder is very useful and attractive for measuring a variety of features. The hand-held digital video camcorder is not high-quality equipment because the instrument is not intended for commercial use. The sampled video from this style of camcorder does not have enough resolution and the quality of the images is not good to use as measuring tools. Therefore, the following concept and algorithms of DDVS are employed to measure the structural displacements with this hand-held type of video camcorder.
Fig. 2. Target movement and image distortion caused by camera lens.
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This predetermined ROI for one point can be applied as absolute coordinates due to its being fixed from first frame to the last frame. The pixel amount of the target may be limited (or less) in each frame depending on the actual size of the target and the filming distance. In the case of the large specimen installed with multitargets as shown in Fig. 3(a), the amount of pixel for expressing the target image in the digital image is few as shown in Fig. 3(b). In such a situation, the MPC, which is associated with the pixel of the image, may not be calculated or produced accurately. Hence, to improve the accuracy, the concept of resizing the achieved ROI is proposed. Fig. 3(c) is the resized ROI image of Fig. 3(b) by ten times. Distorted circle images on the target shown in Fig. 3(b) are restored as actual circles on the target and it shows sufficient pixels to calculate the MPC. Though target, which consists of five circles is used in this study, any other shape can be used for the target image component. In this proposed algorithm, the center of gravity is calculated to determine the center point on the target circles. If any other shape on the target is used, distortion of the target image can occur due to a rapid movement of the target object and errors in determining the center of gravity can be incurred. Therefore, the circle type of image components on the target are used considering that the accuracy in measurement and distance between the centers of the circle components have to be confirmed to calculate the MPC. 2.2. Algorithms of the DDVS First, the specimen movement under the dynamic loading is captured with the digital camcorder. From the video, the number of frames, the image size, the video duration, and the frame-rate are identified as calculation parameters using the MATLAB image processing toolbox. The recorded video contains important dynamic information of the target specimen throughout the length of the test. However, the moving time of the specimen under the loading is only significant to measure the displacement in the total recording time. Therefore, some parts of the video are removed to increase the efficiency in calculating the target displacement. Then, the gap distances of each of the five circles on the real target plate are entered and the ROI, with just enough space to measure the displacement, is selected manually on the first frame of the video. The ROI is set up as shown in Fig. 4. The selected ROI image is multiplied and resized to acquire the accurate calculation of the displacement by bi-cubic interpolation
599
Fig. 4. Set-up of Region of Interest (ROI) in input digital image.
method that the resized output pixel is a weighted average of pixels in the nearest 4-by-4 neighborhood. Also, the threshold is configured for each frame and the colored layer is changed to a gray image to improve the image processing speed. It applies 2-D filtering to improve the accuracy of the measured data and to correct the distorted image as the original image form. According to the recording video image’s quality, brightness and definition are corrected to improve the recognition of the target through the image processing and the location of the center points of each circle is saved as a pixel unit in time history. There are various established methods to determine the target position in terms of the target tracking and motion capture technique etc. In this study, the center point of each circle is decided by calculating the center of gravity of the pixels from each circle images after finishing the search for five circles with the boundary on the target in the ROI region. From the information the target’s position, the following equation is determined in each time step by deciding the ratio of pixel distance to actual target. MPC Coeff xl ¼ Lg =½ðtbxðiÞ taxðiÞ Þ2 þ ðtbyðiÞ tayðiÞ Þ2 1=2
ð1Þ
Coeff xr ¼ Lg =½ðtbxðiÞ tcxðiÞ Þ2 þðtbyðiÞ tcyðiÞ Þ2 1=2
ð2Þ
Coeff yu ¼ Lg =½ðtbxðiÞ tdxðiÞ Þ2 þ ðtbyðiÞ tdyðiÞ Þ2 1=2
ð3Þ
Coeff yd ¼ Lg =½ðtbxðiÞ texðiÞ Þ2 þðtbyðiÞ teyðiÞ Þ2 1=2
ð4Þ
Displacement at (iþ1) frame (initial displacement¼ 0)
Fig. 3. Example of the target resizing ((a) test specimen, (b) captured ROI, and (c) resized ROI).
dispxðiþ 1Þ ¼ taxði þ 1Þ taxðiÞ
ð5Þ
dispyði þ 1Þ ¼ tayði þ 1Þ tayðiÞ
ð6Þ
where, Lg, tax, tbx, tcx, tdx, tex, tay, tby, tcy, tdy, tey are shown in Fig. 4, and Coeff is the MPC of the up-down and left-right direction. Finally, the displacement at each time step is derived by tracking the center point of the center circle (ta0 ) on the target based on the upper left reference point on the ROI and by multiplying the MPC according to Eq. (1)–(4). The MPC of i time step is applied as the MPC of (iþ1) time step to calculate the displacement of the center point of the next measured time step. This process is repeated until the target specimen stops moving. For example, if the target moves in the upper right direction from the 1st frame to the nth frame as shown in Fig. 4, the displacement is derived by multiplying the movement information of the center point of the pixel using Eq. (2) for the right direction and Eq. (3) for the
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Fig. 5. Algorithm summary of the suggested measurement method.
Table 1 Results of videotaping distance test from freeze-frame to the mm/pixel.
8 mm/pixel
MPC(mm/pixel coeff.)
Linear regression
6
4
2
2
4 6 8 Distance of videotaping (m)
4 Crest factor
12
3
8
2
4
1
VDV(Vibraion Dose Value)
Crest factor
VDV
0
0 2
4 6 8 Distance of videotaping (m)
Pixel diameter
MPC (mm/ pixel coeff.)
Pixel area
Peak
RMS
Crest factor
2.3 2.9 3.5 4.1 4.7 5.3 5.9 6.5 7.1 7.7 8.3 8.9
16.303 12.800 10.156 8.604 7.190 5.939 5.553 5.035 4.758 4.345 4.221 4.222
0.979 1.217 1.459 1.689 3.206 3.596 4.008 4.407 4.808 5.192 5.584 6.059
208.744 128.678 81.007 58.140 40.598 27.703 24.218 19.912 17.782 14.828 13.992 14.000
0.125 0.194 0.284 0.373 0.779 0.648 0.910 0.572 1.251 1.504 0.848 0.609
0.057 2.200 0.109 1.771 0.154 1.840 0.187 1.997 0.368 2.118 0.240 2.700 0.374 2.432 0.167 3.419 0.621 2.014 0.661 2.275 0.077 10.948 0.039 15.427
VDV
0.279 0.504 0.722 0.880 1.674 1.268 1.911 1.206 2.649 2.987 1.005 0.609
2.3. Limitation of the algorithms
10
16
Distance (m)
10
Fig. 6. Results of videotaping distance test from freeze-frame to the mm/pixel (a) videotaping distance–mm/pixel relationship. (b) Videotaping distance–crest factor, VDV relationship.
upper direction among the calculated coefficient. Fig. 5 simply shows the summarized algorithms of the suggested measurement method.
Generally, the recording frame-rate of the camcorder is from 30 Hz to a maximum of 60 Hz; however, the sampling rate of such equipment does not decrease to an integer; though advertised as 60 Hz the actual frame sampling rate is around 59 Hz. It is continually changed by each frame memory size, therefore, a minor time gap for measuring time occurs. Also, since the maximum frame sampling rate of the existing general video camcorder is around 60 Hz, the motions above the Nyquist frequency, 30 Hz, cannot be calculated. Collision and other high frequency movements are particularly hard to capture. Although other research has set the objective of calculating real-time displacement with this method, our research aims primarily for greater accuracy. The definition and sampling rate of the camcorder are set high automatically to increase precision, which makes it hard to process the measurement in real-time. Also, the processing time increases as the measuring point increase. (The total calculation time is the sum of each point’s displacement calculation time) Furthermore, only flat surfaces (X, Z axes, the filmed surface) can be tracked. If movement occurs in Y axis (front and rear of the filmed surface,) it is prevented since the coefficient changes as the targets’ size changes with the Y axis movement, the measurement cannot be performed on all axes (X, Y and Z axes). Moreover, the measurement values are unstable depending on the movement of the camera support (tripod) and are affected by the accuracy of their adherence to the target. Despite the disadvantages, the suggested measurement records the full video and helps to measure the displacement
H.-S. Choi et al. / NDT&E International 44 (2011) 597–608
601
Fig. 7. Outline of the effect of the angle of the videotaping and resizing.
100 Resizing factor RF : 1
Displacement (mm)
50 0
RF :
5
RF :
10
RF :
20
Input motion
-50 -100 -150 0
5
10
15 Time (sec)
20
25
30
Fig. 8. Test set-up for evaluating the effect of the angle of videotaping and the resizing.
and distinction of the mode shape under a simulated earthquake motion with a frequency component that is almost concentrated within 30 Hz. If the target is located in the boundary of the video screen comparably large displacements can be measured. Also, the measurement can be applied to the locations where the displacement transducer is difficult to attach and it can be in a bi-directional motion. The targets are only required to be printed using commercial printer.
3. Verification of the DDVS method For the development of DDVS, displacement is measured for slow motion using a low frequency sine wave to validate the proposed DDVS method. Also, the displacement of the structural system is measured under the El Centro earthquake to confirm the DDVS method’s application for dynamic motion and is compared with the displacement measured by Linear Variable Differential Transformer (LVDT). Furthermore, a seismic wave is also a kind of random wave and it is a good way to represent the measuring signal since a random wave has various frequency components. Generally, shock motion is unusual for seismic research in civil construction. Therefore, the El Centro motion profile which is familiar to researchers of seismic problems in civil engineering is used to
Displacement (mm)
-88 -92 -96
Resizing factor RF : 1
-100
RF :
5
RF :
10
RF :
20
Input motion
-104 4
5
6 Time (sec)
7
8
Fig. 9. Time–displacement relationship according to the resizing factors (a) full time–displacement relationship. (b) Expanded partial time–displacement relationship.
validate the suggested DDVS method. This study, to evaluate the applicability and the efficiency of the DDVS, analyzes the effect of videotaping distance to the mm/pixel and verifies the effect of the videotaping angle and resizing to the rigid frame under cyclic loading finally, the DDVS method is verified using the earthquake motion test of the masonry specimen and the two-story steel frame specimen. 3.1. Analysis of the effect of videotaping distance from freeze-frame to the mm/pixel In tracking the installed target on the fixed wall, the measured displacement value from the DDVS analysis should be fixed.
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However, measured values from the digital camcorder did not appear as stable and correct values and it showed to randomly fluctuate in the real case according to luminance and the target shape being distorted by the threshold. These effects can be found as forms of random noise in this study. Therefore, it is necessary to evaluate the effects of errors in the measurement system. To analyze the errors and the applicability of the calculation system developed in this study, targets consisting of 30 mmdiameter-circles in 50 mm gaps were installed at a fixed location. The location of the camcorder was then changed to investigate the effect of the MPC and the pixel area of the target toward the calculation. The camcorder used in this measurement has a resolution of 1280 720 pixels and is able to measure almost 60 frames per second. The errors, measured as random signals, are calculated as vibration evaluation factors by peak value, Root Mean Square (RMS), crest factor, and vibration dose value (VDV). Peak and RMS values can be used to evaluate the noise level. But when assessing intermittent noise, crest factor, and VDV were generally used for representative value. The crest factor is a measurement of a waveform, calculated from the peak amplitude of the waveform divided by the RMS value of the waveform. Crest factor, Eq. (7) is an important parameter to understand when trying to take accurate measurements of low
4 Angle of videotaping 0° 30°
MPC(mm/pixel coeff.)
3
2
frequency signals C¼
9dpeak 9 RMS
ð7Þ
C is the crest factor and dpeak is the absolute maximum value of measured displacement. The VDV is given the fourth root of the integral with respect to the time of the fourth power of the given value. This is the root-mean-quad approach. The use of the fourth power method makes VDV more sensitive to peaks in the waveform Z T 4 VDV ¼ d4 ðtÞdt 0:25 ð8Þ 0
where, VDV is the vibration dose value in pixel^1.75, d is the measured pixel movement, and T is the total period of the test time in which vibration may occur. Generally, RMS and peak value are considered to determine the error level but when crest factor is more than 2.0, VDV is more reasonable than RMS. In this study, VDV is less than 2.0, high peak values in random noise problem does not affect the general vibration measurement. Fig. 6 and Table 1 show the results of the videotaping distance test from freeze-frame to the MPC. In Table 1, pixel diameter, as a circle on the target of the acquired images, is a diameter in pixel units and pixel area means the area of the center circle in pixel unit. These values can be estimated by prerecording and we can find the optimal distance between the camcorder and the object by comparing them to the evaluated values. As a result, as shown in Fig. 6 and Table 1, until the ratio of the expressed actual distance per pixel is 4.407 mm, the RMS error, 0.374 mm, may be judged to replace the LVDT to calculate the displacement. However, if the pixel coefficient exceeds roughly 4.808 mm/pixel, the calculation value fluctuates, the safety level decreases, and is thus judged not to be applicable for actual measurement. 3.2. Analysis of the effect of the angle of videotaping and resizing under cyclic loading
1
0
0
5
10 Resizing factor
15
20
Fig. 10. Resizing factor and the MPC relationship according to the videotaping angle.
When measuring the behavior of the flat surface with one camera, camera lens distortion may occur due to the angle of videotaping angle. Thus analysis of such an error is required and during the proposed process of resizing the ROI, computation of the proper resizing factor is necessary. To verify the measurement method, after attaching the targets to two rigid frames, which were installed on a shaking table under cyclic loading, the measurement error depending on the angle of videotaping and the effect of resizing were identified. Figs. 7 and 8 show the experiment outline of the effect of the angle of videotaping and resizing to the two rigid frames under cyclic loading. The two rigid frames, separated by 2.5 m, were installed on the shaking
Table 2 Results of the videotaping angle and the resizing tests of the rigid frame on the shaking table. Videotaping angle (deg.)
Resizing factor
MPC (mm/ pixel coeff.)
Measured displacement (mm) Max
Error (%)
Min
Amplitude
Max
Min
Amplitude
0
1 5 10 20
3.15 0.64 0.32 0.16
98.99 98.01 98.43 98.32
98.17 97.94 97.90 97.90
197.16 195.95 196.34 196.22
0.33 1.32 0.89 1.00
0.89 1.13 1.17 1.17
0.61 1.22 1.03 1.09
30
1 5 10 20
3.36 0.67 0.34 0.16
97.38 99.84 100.33 98.41
96.38 99.63 99.58 97.74
193.75 199.47 199.91 196.15
1.96 0.52 1.02 0.91
2.71 0.58 0.53 1.33
2.33 0.55 0.77 1.12
H.-S. Choi et al. / NDT&E International 44 (2011) 597–608
603
Fig. 11. Masonry specimen drawings (a) dimension of the test masonry specimen. (b) Measurements set-up of test masonry specimen.
0.4
Acceleration (g)
0.2
0
-0.2
PGA 0.35g
-0.4 0
10
20
30 Time (sec)
40
50
60
Fig. 12. Acceleration time history of the El Centro earthquake.
table and the camcorder was installed at a height of 1.5 m and videotaping distance of 4.33 m to consisting of a 301 videotaping angle between the two rigid frames.
The object of DDVS is to measure the displacement of the large-scale shaking table test. For a screen, a 301 recording angle is sufficient to measure the displacement of a large scale specimen in a laboratory. Therefore, this study considers 301 angle of video view. The sine wave dynamic loading was applied and the effect of the angle of videotaping and resizing was analyzed. The sine wave dynamic loading had amplitude of 100 mm and a frequency of 0.05 Hz. Fig. 9 is a comparison of the feedback from the actuator LVDT of the shaking table and the result of the proposed method to the right rigid frame. Fig. 9(a) displays the comparison of the displacement from the shaking table and the rigid frame and the overall displacement from the shaking table and the measurement result of the DDVS are very similar. Since the effect from the resizing factor is not visible from Fig. 9(a), Fig. 9(b) magnifies a certain portion of the displacement from the measurement and presents the effect of the resizing factor. As shown in Fig. 9(b,) the proximity of the measured displacement increases according to the resizing factor. Fig. 10 shows the change of MPC according to the angle of the videotaping and the resizing factor. The figure shows that the MPC is fixed and disregards the angle of the videotaping when the resizing factor exceeds ten. The measurement error according to
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the angle of the videotaping and the results of the resizing applied test are summarized in Table 2.
4. Application of the DDVS method for real structural system 4.1. Outlines of the structural systems for DDVS applications
Fig. 13. Test set-up of the tested masonry specimen.
The applicability of the actual construction of DDVS proposed in this study was evaluated by measuring the dynamic displacement of the masonry structure and the two-story steel frame structure under seismic loading. The digital camcorder used in this study is a Panasonic HDC-TM700, which has 1920 1080 pixels of resolution (full HD) and a 60 Hz sampling frequency. The masonry structure subjected to the measurement is a masonry wall with a height of 1761 and 190 mm of thickness, as shown in Fig. 11. To apply the fixed load of 10 kN as a vertical load to the first floor exterior wall of the two-story masonry structure, a concrete weight with a width of 540 mm, a height of 370 mm, and a length of 1750 mm was installed. To apply the seismic loading, the specimen structure was installed on a 200 mm thick steel beamed concrete slab. The masonry specimen was installed on the shaking table of the Multi-platform Seismic Simulation Center at Pusan National University. The steel plate was installed under the floor slab of the specimen and the floor was firmly integrated with high tension bolts to transfer the base motion from the shaking table to the concrete slab. The El Centro earthquake motion was used for the seismic loading on the object masonry structure as shown in Fig. 12. The El Centro earthquake
Fig. 15. Two-story frame specimen and measurement set-up.
80 Time-displacement relationships Input motion (LVDT of shaking table)
Displacement (mm)
Values at 1st target
40
0
-40
-80 0
Fig. 14. Two-story frame specimen drawings and target notations (a) dimension of two-story frame specimen and (b)target notations.
10
20
30
40 Time (sec)
50
60
70
Fig. 16. Full time–displacement relationships of the masonry specimen for comparing the 1st target and actuator LVDT feedback.
H.-S. Choi et al. / NDT&E International 44 (2011) 597–608
has an energy concentration frequency of 1–4 Hz where the dominant frequency is 1.5 Hz and the Peak Ground Acceleration (PGA) is around 0.35g. To verify the dynamic displacement of the structure under various seismic loading levels, the El Centro wave
4 Time-displacement relationships Input motion(LVDT of shaking table)
Displacement (mm)
Values at 1st target
2
0
-2
-4 45
46
47 Time (sec)
48
49
Fig. 17. Expanded partial time–displacement relationships of the masonry specimen.
2000 Height of masonry specimen (mm)
Valuse caculated by 4th and 5th target data Valuse at 4th target
80 Displacement (mm)
was scaled to a total of six variations of 0.06g, 0.1g, 0.14g 0.2g, 0.3g, and 0.4g according to the PGA. The LVDT and accelerometers were installed on the masonry structure, as shown in Fig. 11(b). The LVDT and the accelerometers were installed on the sides of the top and the bottom of the structure, respectively; thus, a total of four LVDTs and four accelerometers were installed and the target, consisting of 30 mm-diameter-circles in a 50 mm gap, was installed in the direction of the seismic loading. For the LVDT on the bottom, the installed location was identical to the location of the bottom target circle. However the LVDT on the top, due to the location issue, the installed location was not identical to the target location. Fig. 13 is the masonry structure installed with the dynamic displacement targets evaluated in this study and the experiment picture. Another evaluation issue was examined by measuring the structural responses of the two-story steel frame specimen under the dynamic loading condition that is the same input motion with the masonry wall specimen. In the case of the two-story frame, the deformation and mode shape can be determined by mass concentrated on the floor slab. However, many displacement meters and extra-rigid body structures may be required to use the existing contact displacement meter, LVDT. However, if the
Time-displacement relationships LVDT at uppwer position
120
Valuse at 5th target
40 0 -40 -80
1600
1200
800
20
30
40 Time (sec)
50
60
Displacement of masonry specimen Maximum dis. at o.1g Minimum dis. at o.1g Maximum dis. at o.4g Minimum dis. at o.4g
400
-120 10
605
0 -120
70
Fig. 18. Full time–displacement relationships at LVDT and targets of the masonry specimen for comparing 4th, 5th targets and installed LVDT.
-80
-40 0 40 80 Displacement (mm)
Fig. 20. Maximum and minimum displacement of the masonry specimen according to its height and seismic loading.
Time-displacement relationships LVDT at uppwer position Valuse caculated by 4th and 5th target data Valuse at 4th target
120
-60
Displacement (mm)
Valuse at 5th target
Displacement (mm)
80 40
-70 -80 -90 -100
0
-110 19.4
-40
19.5 Time (sec)
19.6
-80 -120 15
18
21
120
24
Time (sec) Fig. 19. Expanded partial time–displacement relationships at LVDT and targets of the masonry specimen.
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proposed method is used, attaching the targets and measuring the dynamic displacement may distinguish the deformation trend of the objective two-story frame and mode shape. Fig. 14 shows the dimension of the two-story frame installed on the shaking table and the location of attached targets. As shown in Fig. 14, since the targets are attached between the nodes of the two-story frame and the LVDT is installed at the horizontal actuator that is equipped to control the shake table, to compare the accuracy of the displacement feedback from the seismic loading at the shaking table and the displacement measured by the suggested method, the target was attached at the node of the shaking table’s
60 Time-displacement relationship Measured values by DDVS LVDT values at actuator
Displacement
30
0
-30
-60 0
20
40 Time (sec)
60
80
31
32
Time-displacement relationship
10
Measured values by DDVS LVDT values at actuator
Displacement
5
0
-5
-10 29
28
30 Time (sec)
Fig. 21. Time–displacement relationships of the DDVS and actuator LVDT feedback of the two-story frame specimen (a) total time–displacement relationship. (b) Partial time–displacement relationship at 28–32 s.
joint at the bottom-left of the two-story frame. Fig. 15 is the experiment picture of the two-story frame specimen.
4.2. Application results of the DDVS method The dynamic displacement of the masonry wall under the earthquake wave excitation was measured by the shaking table test to evaluate the applicability of the DDVS. Fig. 16 compared the measured displacement in case that the PGA is 0.3g, which is input ground motion, of the first target on the bottom of the masonry wall using DDVS and the displacement transducer installed on the actuator for the shaking table control. Fig. 17 magnifies the comparatively quiet zone of the displacement time history from the comparison result in Fig. 16. Fig. 16 shows that DDVS can measure the dynamic displacement of the simulated El Centro earthquake (PGA¼0.3g) relatively well and, as shown in Fig. 17, this suggested method can measure the displacement from 1 to 4 mm. It is therefore judged to be applicable to measure the dynamic displacement measuring of actual bridges and various civil structures. Figs. 18 and 19 depict the comparisons between the displacement of the masonry wall specimen through the 4th and 5th targets, the displacement measured from the LVDT installed on the top of the construction, and the displacement measured by the 4th and 5th targets at the same position as the LVDT. As shown in Figs. 18 and 19, the displacement increases as the position of the measurement changes. The displacement measured from the LVDT and the displacement measured by the 4th and 5th targets at the same position as the LVDT was discovered to be similar. So, as shown in Fig. 20, the dynamic displacement of the masonry wall specimen can be effectively distinguished and evaluated through the proposed measurement method due to the exact measurement of the relative displacement according to the size of the construction by the attached target on the seismic loaded construction. From the two-story steel frame test result, Fig. 21 displays the comparison between the displacement of the LVDT, which is installed on the horizontal actuator to control the shaking table, and the displacement measured through the target attached at the node of the shaking table joint at the bottom-left of the twostory frame. As shown in Fig. 21, the displacement measured through the DDVS method and the displacement of the LVDT installed on the actuator show almost the same shapes. To analyze the degree of the measured displacement results, the accuracy and precision of measurement results from this study are compared using percent error of Eq. (9), a RMS of Eq. (10), and a system error of Eq. (11). Percent error ¼
n X i¼1
ðdi dl Þ2 =
n X
ðdl Þ2
ð9Þ
i¼1
Table 3 Measured displacement and relative displacement by DDVS of the two-story frame specimen. Target position (height)
Target position (relative width) 0
5270 4020 2770 1520 270
1400
2100
3500
Measured displacement
Relative displacement
Measured displacement
Relative displacement
Measured displacement
Relative displacement
Measured displacement
Relative displacement
83.03/ 61.16 76.66/ 56.37 70.21/ 49.47 57.79/ 40.16 48.45/ 31.03
35.08/ 30.64 28.71/ 25.84 22.26/ 18.94 9.84/ 9.63 0.50/ 0.50
83.61/ 61.63 69.15/ 48.83
35.66/ 31.10 21.20/ 18.30
84.71/ 61.20 69.04/ 49.05
36.76/ 30.67 21.09/ 18.52
84.57/ 61.05 77.80/ 55.46 70.02/ 48.09 57.29/ 38.24 47.08/ 29.37
36.62/ 30.52 29.85/ 24.93 22.07/ 17.56 9.34/ 7.72 0.87/1.16
H.-S. Choi et al. / NDT&E International 44 (2011) 597–608
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 i ¼ 1 ðdi dl Þ RMS error ¼ n
ð10Þ
System error ¼ RMS error=max displacement from DDVS
ð11Þ
where, dl is the measured displacement by LVDT, di is the measured displacement by DDVS, and n is the amount of data measured. As shown in Table 3, after comparing the displacement measured through the proposed measurement method and the displacement of the LVDT installed on the actuator, it is known that the DDVS method has an equal level of measurement degree to the contact measurement method since the RMS error was 0.559 mm, the maximum displacement from the DDVS was 51.600 mm despite that the maximum LVDT feedback was 51.504 mm, the percent error was 0.471%, and the system error was 1.083%. Thus, it is judged that the suggested measurement method is able to
20 0 -20
Relatve displacement at R1
20 0 -20 -40
-40 0
20
40 Time (sec)
60
0
80
Time-relative displacement relationship
40
20
20 0 -20 -40
40 Time (sec)
60
80
Time-relative displacement relationship
40
Relatve displacement at L3
Displacement
Displacement
Time-relative displacement relationship
40
Relatve displacement at L1
Displacement
Displacement
Fig. 23. Maximum deformation shape of the two-story frame specimen measured by the DDVS method.
Time-relative displacement relationship
40
Relatve displacement at R3
20 0 -20 -40
0
20
40 Time (sec)
60
0
80
Time-relative displacement relationship
40
20
20 0 -20 -40
40 Time (sec)
60
80
Time-relative displacement relationship
40
Relatve displacement at L5
Displacement
Displacement
607
Relatve displacement at R5
20 0 -20 -40
0
20
40 Time (sec)
60
80
0
20
40 Time (sec)
60
80
Fig. 22. Time-relative displacement relationships of the two-story frame specimen by the DDVS (a) At L1 target (b) At R1 target (c) At L3 target (d) At R3 target (e) At L5 target, and (f) At R5 target.
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evaluate the dynamic behavior of the two-story frame installed on the shaking table. Fig. 22 shows the measured dynamic displacement on each target by the DDVS method and behaviors and deformation shape of the two-story frame under seismic loading is shown in Fig. 23 from the measurement results of Fig. 22. Thus the DDVS method, despite the relatively low resolution and video sampling rate, during frozen, slow, and fast seismic motions, has precision and usability that can replace the existing transducer. Particularly, the method can be effectively applied to even the fast behavior such as that of the seismic wave.
5. Conclusions This study evaluates the DDVS method, which is more economical than the existing contact and contactless measurement methods of dynamic displacement and deformation and is applicable for imaging unapproachable structures using a hand-held digital video camcorder through the dynamic displacement measurement of the flat surface of two frames. To improve the accuracy of the digital image during the suggested displacement measurement method, the ROI was resized and the coefficient was updated at each time step. To evaluate the suggested measurement, the effect of videotaping distance to mm/pixel was analyzed, and the effect of the angle of videotaping and resizing was verified. Also, the usability of the suggested method was evaluated by measuring the dynamic displacement of the masonry wall specimen and the two-story steel frame specimen under the El Centro earthquake loading. Through this, the dynamic displacement and the behavior of the construction can be effectively verified. Therefore, the suggested measurement method, despite the relatively low resolution and video sampling rate, during frozen, slow, and fast seismic motions, has precision and usability that can replace the existing transducer. Particularly, the method can be effectively applied to even the fast behavior such as that of the seismic wave.
Acknowledgement This study has been conducted as long distance cooperation research by KREONET built by KISTI, and the Brain Korea 21
program at Pusan National University’s Division for UbiquitousApplied Construction of Port Logistics Infrastructures and at Yonsei University’s Center for Future Infrastructure.
References [1] Wahbeh AMazen, Caffrey John P, Masri Sami F. A vision-based approach for the direct measurement of displacements in vibrating systems. Smart Materials and Structures 2003;12(5):785–94. [2] Lee Jong Jae, Shinozuka Masanobu. Vision-based system for remote sensing of bridge displacement. NDT and E International 2006;39(5):425–31. [3] Morlier J, Salom P, Bos F. New image processing tools for structural dynamic monitoring. Key Engineering Materials 2007;347:239–44. [4] Lee Jong Jae, Fukuda Yoshio, Shinozuka Masanobu, Cho Soojin, Yun Chung-Bang. Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures. Smart Structures and Systems 2007;3(3):373–84. [5] Park Jong-Woong, Lee Jong-Jae, Jung Hyung-Jo, Myung Hyun. Vision-based displacement measurement method for high-rise building structures using partitioning approach. NDT&E International 2010. on line. [6] Kim Sung-Wan, Kim Nam-Sik. Multi-point dynamic displacement measurements of structures using digital image correlation technique. Journal of the Earthquake Engineering Society of Korea 2009;13(3):11–9. in Korean. [7] Olaszek P. Investigation of the dynamic characteristic of bridge structures using a computer vision method. Measurement 1999;25(3):227–36. [8] Patsias S, Staszewskiy WJ. Damage detection using optical measurements and wavelets. Structural Health Monitoring 2002;1:5–22. [9] Poudel UP, Fu G, Ye J. Structural damage detection using digital video imaging technique and wavelet transformation. Journal of Sound and Vibration 2005;286(4–5):869–95. [10] Lee JJ, Shinozuka M. Real-time displacement measurement of a flexible bridge Using digital image processing techniques. Experimental Mechanics 2006;46(1):105–14. [11] Ji YF, Chang CC. Nontarget image-based technique for small cable vibration measurement. Journal of Bridge Engineering 2008;13(1):34–42. [12] Morlier Joseph, Michon Guilhem. Virtual vibration measurement using KLT motion tracking algorithm. Journal of Dynamic Systems, Measurement, and Control 2010;132(1). 011003-1-8. [13] Xu L, Guo JJ, Jiang JJ. Time-frequency analysis of a suspension bridge based on GPS. Journal of Sound and Vibration 2002;254(1):105–16. [14] Nassif HH, Gindy M, Davis J. Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration. NDT&E International 2005;38(3):213–8. [15] Oberholster AJ, Heyns PS. Online condition monitoring of axial-flow turbomachinery blades using rotor-axial Eulerian laser Doppler vibrometry. Mechanical Systems and Signal Processing 2009;23:1343–634. [16] Gentile Carmelo. Application of microwave remote sensing to dynamic testing of stay-cables. Remote Sensing 2010;2:36–51.
Contents lists available at ScienceDirect
NDT&E International journal homepage: www.elsevier.com/locate/ndteint
Structural dynamic displacement vision system using digital image processing Hyoung-Suk Choi a, Jin-Hwan Cheung a, Sang-Hyo Kim b, Jin-Hee Ahn b,n a b
Department of Civil Engineering, Pusan National University, Busan 609-735, Republic of Korea School of Civil and Environmental Engineering, Yonsei University, Seoul 120-749, Republic of Korea
a r t i c l e i n f o
abstract
Article history: Received 5 November 2010 Received in revised form 9 June 2011 Accepted 12 June 2011 Available online 21 June 2011
This study introduces dynamic displacement vision system (DDVS), which is applicable for imaging unapproachable structures using a hand-held digital video camcorder and is more economical than the existing contact and contactless measurement methods of dynamic displacement and deformation. This proposed DDVS method is applied to the Region of Interest (ROI) resizing and coefficient updating at each time step to improve the accuracy of the measurement from the digital image. Thus, after evaluating the algorithms conducted in this study by the static and dynamic verification, the measurement’s usability by calculating the dynamic displacement of the masonry specimen, and the two-story steel frame specimen is evaluated under uniaxial seismic loading. The algorithm of the proposed method in this study, despite the relatively low resolution during frozen, slow, and seismic motions, has precision and usability that can replace the existing displacement transducer. Moreover, the method can be effectively applied to even fast behavior for multi-measurement positions like the seismic simulation test using large scale specimen. DDVS, using the consecutive images of the structures with an economic, hand-held digital video camcorder is a more economical displacement sensing concept than the existing contact and contactless measurement methods. & 2011 Elsevier Ltd. All rights reserved.
Keywords: Dynamic displacement vision system Dynamic displacement Camcorder Measurement techniques Shake table test Masonry wall Two-story steel frame
1. Introduction In various fields, such as the movie industry, the automation technology industry, and in medical practice the video camera is used for measuring an object’s shape and the changes to its deformation. This economical and effective technology to measure the displacements and deformation of objects is widely practiced as seen through vision systems or machine visions [2–6]. In dynamic engineering, modal analysis is commonly used to assess the dynamic systems and the method of using an optical camera is being considered effective in obtaining modal parameters. The previous works assess dynamic parameters by introducing real targets on the structure or by studying simple structures in ideal conditions [7–9]. Real-time displacement measurements have been proposed using texture recognition algorithms on a flexible bridge [10] and using light-emitting diode targets [1]. Non-target displacement measurements using the concept of optical flow is also introduced for vibration measurements [11]. Furthermore, the approach adopted Kanade–Lucas–Tomasi trackers, virtual sensors on the mechanical systems’ video from a high speed camera, which developed as a kind of noncontact and non-marker method [12].
n
Corresponding author. Tel.: þ82 2 2123 2804; fax: þ82 2 313 2804. E-mail address: [email protected] (J.-H. Ahn).
0963-8695/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ndteint.2011.06.003
Generally, dynamic behavior of large structure such as highrise buildings and long-span bridges is hard to measure because it is difficult to install the reference points for the displacement transducer. Despite that, the movements of such structures can be accurately measured by laser Doppler effects, GPS systems, and high-speed measurements with an infrared camera, the listed methods are not economical and are difficult to measure in terms of the dynamic displacement due to errors in the data and a restricted sampling rate [13–16]. Likewise, this problem arises in the shaking table test for a large specimen. Using the shaking table test to evaluate the dynamic behaviors of structural systems, the main measuring components are accelerations and displacements as members of the structural system. Generally, the accelerometer is attached to the structural specimen because acceleration is achieved as an absolute value. However, reference points are needed to measure its displacement at the outer parts or inner parts of the shaking table. High-capacity transducers are also needed to measure displacements of large scale specimens, which can make large movement at the top parts of the specimen. However, this can cause a difficulty in acquiring a suitable (effective) data resolution and it is not easy to install the highcapacity transducers with relatively long-length. Thus, installing the reference point at the inner parts of the shaking table is more usable and suitable for researchers. Fig. 1 shows an example of a reference frame to measure the displacement in the shaking table test. The reference frame
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2.1. Concept of DDVS
Fig. 1. Example of reference frame and high-rise specimen on the shake table (the reference frame is painted dark gray).
should have a high stiffness to measure the relative displacements of the specimen because it should move identically to the movements of the shaking table. However, it is difficult to fabricate the reference frame to satisfy the high stiffness conditions and it demands the financial support and effort of the researcher. If the constructed frame does not have sufficient stiffness or if the resonance of the frame occurs according to excitation, unexpected motion, and displacement could be incurred at the installed reference frame on the shake table and it could give inaccurate measurements. Therefore, this study proposes a digital video image resizing method to measure the dynamic displacement or mode shapes using an adjusted camera for replacing high-capacity transducers in the shaking table test of large scale civil and building structure specimens. The suggested algorithms are evaluated by static and dynamic verification. In this DDVS algorithm, the resized Region of Interest (ROI) of the acquired digital image is used and the coefficient, for calculating the relationship between pixel movements in the digital image and the moving distance of the real object, is updated at each time step to improve the precision of the measurement. After evaluating the usability of the displacement sensing by the rigid frame specimen, the dynamic displacement of the earthquake loaded masonry specimen, and the two-story steel frame specimen are measured as applications of this method.
The main concept of DDVS is to update the displacement converting coefficient at each frame to reduce the calculation errors and to resize the ROI with low resolution images to improve the calculation accuracy. Generally, when capturing a video image using a digital camcorder, geometric distortion from the camera lens may occur as shown in Fig. 2. This phenomenon, occurring due to the curve of the lens surface and the refraction of light caused by different refraction rates which is dependent on the choice of the lens, can be compensated for through various existing methods. However, such compensation methods may also have additional error from geometric calculation because the methods make numerical errors during the correction process. Thus, the proposed method recognizes the distortion caused by the camera lens but, for accuracy, the method analyzes the image without revision assuming the effect is very small in the ROI. The DDVS introduces a target, which has five circles on the plate as shown in the very top of Fig. 2. The DDVS applies mm/pixel coefficient (MPC) calculated by the relationship between real distance and pixel distance on the recorded video image of the circles on the target for each frame to the displacement conversion algorithm for compensating the distortion. For example, as shown in Fig. 2, assuming the target plate has moved from the 1st to nth position horizontally, the displacement of the real movement is relatively small, and that the captured motion is slower than the recording frequency. The relationship between the incurred displacement, L1, and the projected curve of the image, L2, can be regulated by compensating with a cumulative updated coefficient from relationship between a and a0 or b and b0 at each frame and the error can be reduced in comparison to displacements that are derived directly from the relationship between L1 and L2. Commonly, the purpose of setting up the ROI during image processing is to improve the calculation speed and to manage memory usage. Therefore, existing method only considering applying the digital image correlation with the ROI to the minimum area including the target and the ROI is moved in predicting the next location. Such a set-up leads to calculating the relative displacement according to the change of target in terms of time step and accumulating errors. Thus, this study minimizes the errors by constructing absolute coordinates from applying the ROI to the identical location of the whole frame of filmed video and by tracking the location of the target in each time step.
2. Concept and algorithms of DDVS The video camera is used for motion tracking or recognition using the vision processing records with high resolution and high frame-rate. However, this video recording is limited by recording time and place because it is affected by light intensity, focus, and the video camera’s memory. Also, the high-performance video camera for recording is too much expensive and specific equipment such as lighting is needed to record the image in some cases. Aptly, the hand-held digital video camcorder is very useful and attractive for measuring a variety of features. The hand-held digital video camcorder is not high-quality equipment because the instrument is not intended for commercial use. The sampled video from this style of camcorder does not have enough resolution and the quality of the images is not good to use as measuring tools. Therefore, the following concept and algorithms of DDVS are employed to measure the structural displacements with this hand-held type of video camcorder.
Fig. 2. Target movement and image distortion caused by camera lens.
H.-S. Choi et al. / NDT&E International 44 (2011) 597–608
This predetermined ROI for one point can be applied as absolute coordinates due to its being fixed from first frame to the last frame. The pixel amount of the target may be limited (or less) in each frame depending on the actual size of the target and the filming distance. In the case of the large specimen installed with multitargets as shown in Fig. 3(a), the amount of pixel for expressing the target image in the digital image is few as shown in Fig. 3(b). In such a situation, the MPC, which is associated with the pixel of the image, may not be calculated or produced accurately. Hence, to improve the accuracy, the concept of resizing the achieved ROI is proposed. Fig. 3(c) is the resized ROI image of Fig. 3(b) by ten times. Distorted circle images on the target shown in Fig. 3(b) are restored as actual circles on the target and it shows sufficient pixels to calculate the MPC. Though target, which consists of five circles is used in this study, any other shape can be used for the target image component. In this proposed algorithm, the center of gravity is calculated to determine the center point on the target circles. If any other shape on the target is used, distortion of the target image can occur due to a rapid movement of the target object and errors in determining the center of gravity can be incurred. Therefore, the circle type of image components on the target are used considering that the accuracy in measurement and distance between the centers of the circle components have to be confirmed to calculate the MPC. 2.2. Algorithms of the DDVS First, the specimen movement under the dynamic loading is captured with the digital camcorder. From the video, the number of frames, the image size, the video duration, and the frame-rate are identified as calculation parameters using the MATLAB image processing toolbox. The recorded video contains important dynamic information of the target specimen throughout the length of the test. However, the moving time of the specimen under the loading is only significant to measure the displacement in the total recording time. Therefore, some parts of the video are removed to increase the efficiency in calculating the target displacement. Then, the gap distances of each of the five circles on the real target plate are entered and the ROI, with just enough space to measure the displacement, is selected manually on the first frame of the video. The ROI is set up as shown in Fig. 4. The selected ROI image is multiplied and resized to acquire the accurate calculation of the displacement by bi-cubic interpolation
599
Fig. 4. Set-up of Region of Interest (ROI) in input digital image.
method that the resized output pixel is a weighted average of pixels in the nearest 4-by-4 neighborhood. Also, the threshold is configured for each frame and the colored layer is changed to a gray image to improve the image processing speed. It applies 2-D filtering to improve the accuracy of the measured data and to correct the distorted image as the original image form. According to the recording video image’s quality, brightness and definition are corrected to improve the recognition of the target through the image processing and the location of the center points of each circle is saved as a pixel unit in time history. There are various established methods to determine the target position in terms of the target tracking and motion capture technique etc. In this study, the center point of each circle is decided by calculating the center of gravity of the pixels from each circle images after finishing the search for five circles with the boundary on the target in the ROI region. From the information the target’s position, the following equation is determined in each time step by deciding the ratio of pixel distance to actual target. MPC Coeff xl ¼ Lg =½ðtbxðiÞ taxðiÞ Þ2 þ ðtbyðiÞ tayðiÞ Þ2 1=2
ð1Þ
Coeff xr ¼ Lg =½ðtbxðiÞ tcxðiÞ Þ2 þðtbyðiÞ tcyðiÞ Þ2 1=2
ð2Þ
Coeff yu ¼ Lg =½ðtbxðiÞ tdxðiÞ Þ2 þ ðtbyðiÞ tdyðiÞ Þ2 1=2
ð3Þ
Coeff yd ¼ Lg =½ðtbxðiÞ texðiÞ Þ2 þðtbyðiÞ teyðiÞ Þ2 1=2
ð4Þ
Displacement at (iþ1) frame (initial displacement¼ 0)
Fig. 3. Example of the target resizing ((a) test specimen, (b) captured ROI, and (c) resized ROI).
dispxðiþ 1Þ ¼ taxði þ 1Þ taxðiÞ
ð5Þ
dispyði þ 1Þ ¼ tayði þ 1Þ tayðiÞ
ð6Þ
where, Lg, tax, tbx, tcx, tdx, tex, tay, tby, tcy, tdy, tey are shown in Fig. 4, and Coeff is the MPC of the up-down and left-right direction. Finally, the displacement at each time step is derived by tracking the center point of the center circle (ta0 ) on the target based on the upper left reference point on the ROI and by multiplying the MPC according to Eq. (1)–(4). The MPC of i time step is applied as the MPC of (iþ1) time step to calculate the displacement of the center point of the next measured time step. This process is repeated until the target specimen stops moving. For example, if the target moves in the upper right direction from the 1st frame to the nth frame as shown in Fig. 4, the displacement is derived by multiplying the movement information of the center point of the pixel using Eq. (2) for the right direction and Eq. (3) for the
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Fig. 5. Algorithm summary of the suggested measurement method.
Table 1 Results of videotaping distance test from freeze-frame to the mm/pixel.
8 mm/pixel
MPC(mm/pixel coeff.)
Linear regression
6
4
2
2
4 6 8 Distance of videotaping (m)
4 Crest factor
12
3
8
2
4
1
VDV(Vibraion Dose Value)
Crest factor
VDV
0
0 2
4 6 8 Distance of videotaping (m)
Pixel diameter
MPC (mm/ pixel coeff.)
Pixel area
Peak
RMS
Crest factor
2.3 2.9 3.5 4.1 4.7 5.3 5.9 6.5 7.1 7.7 8.3 8.9
16.303 12.800 10.156 8.604 7.190 5.939 5.553 5.035 4.758 4.345 4.221 4.222
0.979 1.217 1.459 1.689 3.206 3.596 4.008 4.407 4.808 5.192 5.584 6.059
208.744 128.678 81.007 58.140 40.598 27.703 24.218 19.912 17.782 14.828 13.992 14.000
0.125 0.194 0.284 0.373 0.779 0.648 0.910 0.572 1.251 1.504 0.848 0.609
0.057 2.200 0.109 1.771 0.154 1.840 0.187 1.997 0.368 2.118 0.240 2.700 0.374 2.432 0.167 3.419 0.621 2.014 0.661 2.275 0.077 10.948 0.039 15.427
VDV
0.279 0.504 0.722 0.880 1.674 1.268 1.911 1.206 2.649 2.987 1.005 0.609
2.3. Limitation of the algorithms
10
16
Distance (m)
10
Fig. 6. Results of videotaping distance test from freeze-frame to the mm/pixel (a) videotaping distance–mm/pixel relationship. (b) Videotaping distance–crest factor, VDV relationship.
upper direction among the calculated coefficient. Fig. 5 simply shows the summarized algorithms of the suggested measurement method.
Generally, the recording frame-rate of the camcorder is from 30 Hz to a maximum of 60 Hz; however, the sampling rate of such equipment does not decrease to an integer; though advertised as 60 Hz the actual frame sampling rate is around 59 Hz. It is continually changed by each frame memory size, therefore, a minor time gap for measuring time occurs. Also, since the maximum frame sampling rate of the existing general video camcorder is around 60 Hz, the motions above the Nyquist frequency, 30 Hz, cannot be calculated. Collision and other high frequency movements are particularly hard to capture. Although other research has set the objective of calculating real-time displacement with this method, our research aims primarily for greater accuracy. The definition and sampling rate of the camcorder are set high automatically to increase precision, which makes it hard to process the measurement in real-time. Also, the processing time increases as the measuring point increase. (The total calculation time is the sum of each point’s displacement calculation time) Furthermore, only flat surfaces (X, Z axes, the filmed surface) can be tracked. If movement occurs in Y axis (front and rear of the filmed surface,) it is prevented since the coefficient changes as the targets’ size changes with the Y axis movement, the measurement cannot be performed on all axes (X, Y and Z axes). Moreover, the measurement values are unstable depending on the movement of the camera support (tripod) and are affected by the accuracy of their adherence to the target. Despite the disadvantages, the suggested measurement records the full video and helps to measure the displacement
H.-S. Choi et al. / NDT&E International 44 (2011) 597–608
601
Fig. 7. Outline of the effect of the angle of the videotaping and resizing.
100 Resizing factor RF : 1
Displacement (mm)
50 0
RF :
5
RF :
10
RF :
20
Input motion
-50 -100 -150 0
5
10
15 Time (sec)
20
25
30
Fig. 8. Test set-up for evaluating the effect of the angle of videotaping and the resizing.
and distinction of the mode shape under a simulated earthquake motion with a frequency component that is almost concentrated within 30 Hz. If the target is located in the boundary of the video screen comparably large displacements can be measured. Also, the measurement can be applied to the locations where the displacement transducer is difficult to attach and it can be in a bi-directional motion. The targets are only required to be printed using commercial printer.
3. Verification of the DDVS method For the development of DDVS, displacement is measured for slow motion using a low frequency sine wave to validate the proposed DDVS method. Also, the displacement of the structural system is measured under the El Centro earthquake to confirm the DDVS method’s application for dynamic motion and is compared with the displacement measured by Linear Variable Differential Transformer (LVDT). Furthermore, a seismic wave is also a kind of random wave and it is a good way to represent the measuring signal since a random wave has various frequency components. Generally, shock motion is unusual for seismic research in civil construction. Therefore, the El Centro motion profile which is familiar to researchers of seismic problems in civil engineering is used to
Displacement (mm)
-88 -92 -96
Resizing factor RF : 1
-100
RF :
5
RF :
10
RF :
20
Input motion
-104 4
5
6 Time (sec)
7
8
Fig. 9. Time–displacement relationship according to the resizing factors (a) full time–displacement relationship. (b) Expanded partial time–displacement relationship.
validate the suggested DDVS method. This study, to evaluate the applicability and the efficiency of the DDVS, analyzes the effect of videotaping distance to the mm/pixel and verifies the effect of the videotaping angle and resizing to the rigid frame under cyclic loading finally, the DDVS method is verified using the earthquake motion test of the masonry specimen and the two-story steel frame specimen. 3.1. Analysis of the effect of videotaping distance from freeze-frame to the mm/pixel In tracking the installed target on the fixed wall, the measured displacement value from the DDVS analysis should be fixed.
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However, measured values from the digital camcorder did not appear as stable and correct values and it showed to randomly fluctuate in the real case according to luminance and the target shape being distorted by the threshold. These effects can be found as forms of random noise in this study. Therefore, it is necessary to evaluate the effects of errors in the measurement system. To analyze the errors and the applicability of the calculation system developed in this study, targets consisting of 30 mmdiameter-circles in 50 mm gaps were installed at a fixed location. The location of the camcorder was then changed to investigate the effect of the MPC and the pixel area of the target toward the calculation. The camcorder used in this measurement has a resolution of 1280 720 pixels and is able to measure almost 60 frames per second. The errors, measured as random signals, are calculated as vibration evaluation factors by peak value, Root Mean Square (RMS), crest factor, and vibration dose value (VDV). Peak and RMS values can be used to evaluate the noise level. But when assessing intermittent noise, crest factor, and VDV were generally used for representative value. The crest factor is a measurement of a waveform, calculated from the peak amplitude of the waveform divided by the RMS value of the waveform. Crest factor, Eq. (7) is an important parameter to understand when trying to take accurate measurements of low
4 Angle of videotaping 0° 30°
MPC(mm/pixel coeff.)
3
2
frequency signals C¼
9dpeak 9 RMS
ð7Þ
C is the crest factor and dpeak is the absolute maximum value of measured displacement. The VDV is given the fourth root of the integral with respect to the time of the fourth power of the given value. This is the root-mean-quad approach. The use of the fourth power method makes VDV more sensitive to peaks in the waveform Z T 4 VDV ¼ d4 ðtÞdt 0:25 ð8Þ 0
where, VDV is the vibration dose value in pixel^1.75, d is the measured pixel movement, and T is the total period of the test time in which vibration may occur. Generally, RMS and peak value are considered to determine the error level but when crest factor is more than 2.0, VDV is more reasonable than RMS. In this study, VDV is less than 2.0, high peak values in random noise problem does not affect the general vibration measurement. Fig. 6 and Table 1 show the results of the videotaping distance test from freeze-frame to the MPC. In Table 1, pixel diameter, as a circle on the target of the acquired images, is a diameter in pixel units and pixel area means the area of the center circle in pixel unit. These values can be estimated by prerecording and we can find the optimal distance between the camcorder and the object by comparing them to the evaluated values. As a result, as shown in Fig. 6 and Table 1, until the ratio of the expressed actual distance per pixel is 4.407 mm, the RMS error, 0.374 mm, may be judged to replace the LVDT to calculate the displacement. However, if the pixel coefficient exceeds roughly 4.808 mm/pixel, the calculation value fluctuates, the safety level decreases, and is thus judged not to be applicable for actual measurement. 3.2. Analysis of the effect of the angle of videotaping and resizing under cyclic loading
1
0
0
5
10 Resizing factor
15
20
Fig. 10. Resizing factor and the MPC relationship according to the videotaping angle.
When measuring the behavior of the flat surface with one camera, camera lens distortion may occur due to the angle of videotaping angle. Thus analysis of such an error is required and during the proposed process of resizing the ROI, computation of the proper resizing factor is necessary. To verify the measurement method, after attaching the targets to two rigid frames, which were installed on a shaking table under cyclic loading, the measurement error depending on the angle of videotaping and the effect of resizing were identified. Figs. 7 and 8 show the experiment outline of the effect of the angle of videotaping and resizing to the two rigid frames under cyclic loading. The two rigid frames, separated by 2.5 m, were installed on the shaking
Table 2 Results of the videotaping angle and the resizing tests of the rigid frame on the shaking table. Videotaping angle (deg.)
Resizing factor
MPC (mm/ pixel coeff.)
Measured displacement (mm) Max
Error (%)
Min
Amplitude
Max
Min
Amplitude
0
1 5 10 20
3.15 0.64 0.32 0.16
98.99 98.01 98.43 98.32
98.17 97.94 97.90 97.90
197.16 195.95 196.34 196.22
0.33 1.32 0.89 1.00
0.89 1.13 1.17 1.17
0.61 1.22 1.03 1.09
30
1 5 10 20
3.36 0.67 0.34 0.16
97.38 99.84 100.33 98.41
96.38 99.63 99.58 97.74
193.75 199.47 199.91 196.15
1.96 0.52 1.02 0.91
2.71 0.58 0.53 1.33
2.33 0.55 0.77 1.12
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603
Fig. 11. Masonry specimen drawings (a) dimension of the test masonry specimen. (b) Measurements set-up of test masonry specimen.
0.4
Acceleration (g)
0.2
0
-0.2
PGA 0.35g
-0.4 0
10
20
30 Time (sec)
40
50
60
Fig. 12. Acceleration time history of the El Centro earthquake.
table and the camcorder was installed at a height of 1.5 m and videotaping distance of 4.33 m to consisting of a 301 videotaping angle between the two rigid frames.
The object of DDVS is to measure the displacement of the large-scale shaking table test. For a screen, a 301 recording angle is sufficient to measure the displacement of a large scale specimen in a laboratory. Therefore, this study considers 301 angle of video view. The sine wave dynamic loading was applied and the effect of the angle of videotaping and resizing was analyzed. The sine wave dynamic loading had amplitude of 100 mm and a frequency of 0.05 Hz. Fig. 9 is a comparison of the feedback from the actuator LVDT of the shaking table and the result of the proposed method to the right rigid frame. Fig. 9(a) displays the comparison of the displacement from the shaking table and the rigid frame and the overall displacement from the shaking table and the measurement result of the DDVS are very similar. Since the effect from the resizing factor is not visible from Fig. 9(a), Fig. 9(b) magnifies a certain portion of the displacement from the measurement and presents the effect of the resizing factor. As shown in Fig. 9(b,) the proximity of the measured displacement increases according to the resizing factor. Fig. 10 shows the change of MPC according to the angle of the videotaping and the resizing factor. The figure shows that the MPC is fixed and disregards the angle of the videotaping when the resizing factor exceeds ten. The measurement error according to
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the angle of the videotaping and the results of the resizing applied test are summarized in Table 2.
4. Application of the DDVS method for real structural system 4.1. Outlines of the structural systems for DDVS applications
Fig. 13. Test set-up of the tested masonry specimen.
The applicability of the actual construction of DDVS proposed in this study was evaluated by measuring the dynamic displacement of the masonry structure and the two-story steel frame structure under seismic loading. The digital camcorder used in this study is a Panasonic HDC-TM700, which has 1920 1080 pixels of resolution (full HD) and a 60 Hz sampling frequency. The masonry structure subjected to the measurement is a masonry wall with a height of 1761 and 190 mm of thickness, as shown in Fig. 11. To apply the fixed load of 10 kN as a vertical load to the first floor exterior wall of the two-story masonry structure, a concrete weight with a width of 540 mm, a height of 370 mm, and a length of 1750 mm was installed. To apply the seismic loading, the specimen structure was installed on a 200 mm thick steel beamed concrete slab. The masonry specimen was installed on the shaking table of the Multi-platform Seismic Simulation Center at Pusan National University. The steel plate was installed under the floor slab of the specimen and the floor was firmly integrated with high tension bolts to transfer the base motion from the shaking table to the concrete slab. The El Centro earthquake motion was used for the seismic loading on the object masonry structure as shown in Fig. 12. The El Centro earthquake
Fig. 15. Two-story frame specimen and measurement set-up.
80 Time-displacement relationships Input motion (LVDT of shaking table)
Displacement (mm)
Values at 1st target
40
0
-40
-80 0
Fig. 14. Two-story frame specimen drawings and target notations (a) dimension of two-story frame specimen and (b)target notations.
10
20
30
40 Time (sec)
50
60
70
Fig. 16. Full time–displacement relationships of the masonry specimen for comparing the 1st target and actuator LVDT feedback.
H.-S. Choi et al. / NDT&E International 44 (2011) 597–608
has an energy concentration frequency of 1–4 Hz where the dominant frequency is 1.5 Hz and the Peak Ground Acceleration (PGA) is around 0.35g. To verify the dynamic displacement of the structure under various seismic loading levels, the El Centro wave
4 Time-displacement relationships Input motion(LVDT of shaking table)
Displacement (mm)
Values at 1st target
2
0
-2
-4 45
46
47 Time (sec)
48
49
Fig. 17. Expanded partial time–displacement relationships of the masonry specimen.
2000 Height of masonry specimen (mm)
Valuse caculated by 4th and 5th target data Valuse at 4th target
80 Displacement (mm)
was scaled to a total of six variations of 0.06g, 0.1g, 0.14g 0.2g, 0.3g, and 0.4g according to the PGA. The LVDT and accelerometers were installed on the masonry structure, as shown in Fig. 11(b). The LVDT and the accelerometers were installed on the sides of the top and the bottom of the structure, respectively; thus, a total of four LVDTs and four accelerometers were installed and the target, consisting of 30 mm-diameter-circles in a 50 mm gap, was installed in the direction of the seismic loading. For the LVDT on the bottom, the installed location was identical to the location of the bottom target circle. However the LVDT on the top, due to the location issue, the installed location was not identical to the target location. Fig. 13 is the masonry structure installed with the dynamic displacement targets evaluated in this study and the experiment picture. Another evaluation issue was examined by measuring the structural responses of the two-story steel frame specimen under the dynamic loading condition that is the same input motion with the masonry wall specimen. In the case of the two-story frame, the deformation and mode shape can be determined by mass concentrated on the floor slab. However, many displacement meters and extra-rigid body structures may be required to use the existing contact displacement meter, LVDT. However, if the
Time-displacement relationships LVDT at uppwer position
120
Valuse at 5th target
40 0 -40 -80
1600
1200
800
20
30
40 Time (sec)
50
60
Displacement of masonry specimen Maximum dis. at o.1g Minimum dis. at o.1g Maximum dis. at o.4g Minimum dis. at o.4g
400
-120 10
605
0 -120
70
Fig. 18. Full time–displacement relationships at LVDT and targets of the masonry specimen for comparing 4th, 5th targets and installed LVDT.
-80
-40 0 40 80 Displacement (mm)
Fig. 20. Maximum and minimum displacement of the masonry specimen according to its height and seismic loading.
Time-displacement relationships LVDT at uppwer position Valuse caculated by 4th and 5th target data Valuse at 4th target
120
-60
Displacement (mm)
Valuse at 5th target
Displacement (mm)
80 40
-70 -80 -90 -100
0
-110 19.4
-40
19.5 Time (sec)
19.6
-80 -120 15
18
21
120
24
Time (sec) Fig. 19. Expanded partial time–displacement relationships at LVDT and targets of the masonry specimen.
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proposed method is used, attaching the targets and measuring the dynamic displacement may distinguish the deformation trend of the objective two-story frame and mode shape. Fig. 14 shows the dimension of the two-story frame installed on the shaking table and the location of attached targets. As shown in Fig. 14, since the targets are attached between the nodes of the two-story frame and the LVDT is installed at the horizontal actuator that is equipped to control the shake table, to compare the accuracy of the displacement feedback from the seismic loading at the shaking table and the displacement measured by the suggested method, the target was attached at the node of the shaking table’s
60 Time-displacement relationship Measured values by DDVS LVDT values at actuator
Displacement
30
0
-30
-60 0
20
40 Time (sec)
60
80
31
32
Time-displacement relationship
10
Measured values by DDVS LVDT values at actuator
Displacement
5
0
-5
-10 29
28
30 Time (sec)
Fig. 21. Time–displacement relationships of the DDVS and actuator LVDT feedback of the two-story frame specimen (a) total time–displacement relationship. (b) Partial time–displacement relationship at 28–32 s.
joint at the bottom-left of the two-story frame. Fig. 15 is the experiment picture of the two-story frame specimen.
4.2. Application results of the DDVS method The dynamic displacement of the masonry wall under the earthquake wave excitation was measured by the shaking table test to evaluate the applicability of the DDVS. Fig. 16 compared the measured displacement in case that the PGA is 0.3g, which is input ground motion, of the first target on the bottom of the masonry wall using DDVS and the displacement transducer installed on the actuator for the shaking table control. Fig. 17 magnifies the comparatively quiet zone of the displacement time history from the comparison result in Fig. 16. Fig. 16 shows that DDVS can measure the dynamic displacement of the simulated El Centro earthquake (PGA¼0.3g) relatively well and, as shown in Fig. 17, this suggested method can measure the displacement from 1 to 4 mm. It is therefore judged to be applicable to measure the dynamic displacement measuring of actual bridges and various civil structures. Figs. 18 and 19 depict the comparisons between the displacement of the masonry wall specimen through the 4th and 5th targets, the displacement measured from the LVDT installed on the top of the construction, and the displacement measured by the 4th and 5th targets at the same position as the LVDT. As shown in Figs. 18 and 19, the displacement increases as the position of the measurement changes. The displacement measured from the LVDT and the displacement measured by the 4th and 5th targets at the same position as the LVDT was discovered to be similar. So, as shown in Fig. 20, the dynamic displacement of the masonry wall specimen can be effectively distinguished and evaluated through the proposed measurement method due to the exact measurement of the relative displacement according to the size of the construction by the attached target on the seismic loaded construction. From the two-story steel frame test result, Fig. 21 displays the comparison between the displacement of the LVDT, which is installed on the horizontal actuator to control the shaking table, and the displacement measured through the target attached at the node of the shaking table joint at the bottom-left of the twostory frame. As shown in Fig. 21, the displacement measured through the DDVS method and the displacement of the LVDT installed on the actuator show almost the same shapes. To analyze the degree of the measured displacement results, the accuracy and precision of measurement results from this study are compared using percent error of Eq. (9), a RMS of Eq. (10), and a system error of Eq. (11). Percent error ¼
n X i¼1
ðdi dl Þ2 =
n X
ðdl Þ2
ð9Þ
i¼1
Table 3 Measured displacement and relative displacement by DDVS of the two-story frame specimen. Target position (height)
Target position (relative width) 0
5270 4020 2770 1520 270
1400
2100
3500
Measured displacement
Relative displacement
Measured displacement
Relative displacement
Measured displacement
Relative displacement
Measured displacement
Relative displacement
83.03/ 61.16 76.66/ 56.37 70.21/ 49.47 57.79/ 40.16 48.45/ 31.03
35.08/ 30.64 28.71/ 25.84 22.26/ 18.94 9.84/ 9.63 0.50/ 0.50
83.61/ 61.63 69.15/ 48.83
35.66/ 31.10 21.20/ 18.30
84.71/ 61.20 69.04/ 49.05
36.76/ 30.67 21.09/ 18.52
84.57/ 61.05 77.80/ 55.46 70.02/ 48.09 57.29/ 38.24 47.08/ 29.37
36.62/ 30.52 29.85/ 24.93 22.07/ 17.56 9.34/ 7.72 0.87/1.16
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sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn 2 i ¼ 1 ðdi dl Þ RMS error ¼ n
ð10Þ
System error ¼ RMS error=max displacement from DDVS
ð11Þ
where, dl is the measured displacement by LVDT, di is the measured displacement by DDVS, and n is the amount of data measured. As shown in Table 3, after comparing the displacement measured through the proposed measurement method and the displacement of the LVDT installed on the actuator, it is known that the DDVS method has an equal level of measurement degree to the contact measurement method since the RMS error was 0.559 mm, the maximum displacement from the DDVS was 51.600 mm despite that the maximum LVDT feedback was 51.504 mm, the percent error was 0.471%, and the system error was 1.083%. Thus, it is judged that the suggested measurement method is able to
20 0 -20
Relatve displacement at R1
20 0 -20 -40
-40 0
20
40 Time (sec)
60
0
80
Time-relative displacement relationship
40
20
20 0 -20 -40
40 Time (sec)
60
80
Time-relative displacement relationship
40
Relatve displacement at L3
Displacement
Displacement
Time-relative displacement relationship
40
Relatve displacement at L1
Displacement
Displacement
Fig. 23. Maximum deformation shape of the two-story frame specimen measured by the DDVS method.
Time-relative displacement relationship
40
Relatve displacement at R3
20 0 -20 -40
0
20
40 Time (sec)
60
0
80
Time-relative displacement relationship
40
20
20 0 -20 -40
40 Time (sec)
60
80
Time-relative displacement relationship
40
Relatve displacement at L5
Displacement
Displacement
607
Relatve displacement at R5
20 0 -20 -40
0
20
40 Time (sec)
60
80
0
20
40 Time (sec)
60
80
Fig. 22. Time-relative displacement relationships of the two-story frame specimen by the DDVS (a) At L1 target (b) At R1 target (c) At L3 target (d) At R3 target (e) At L5 target, and (f) At R5 target.
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evaluate the dynamic behavior of the two-story frame installed on the shaking table. Fig. 22 shows the measured dynamic displacement on each target by the DDVS method and behaviors and deformation shape of the two-story frame under seismic loading is shown in Fig. 23 from the measurement results of Fig. 22. Thus the DDVS method, despite the relatively low resolution and video sampling rate, during frozen, slow, and fast seismic motions, has precision and usability that can replace the existing transducer. Particularly, the method can be effectively applied to even the fast behavior such as that of the seismic wave.
5. Conclusions This study evaluates the DDVS method, which is more economical than the existing contact and contactless measurement methods of dynamic displacement and deformation and is applicable for imaging unapproachable structures using a hand-held digital video camcorder through the dynamic displacement measurement of the flat surface of two frames. To improve the accuracy of the digital image during the suggested displacement measurement method, the ROI was resized and the coefficient was updated at each time step. To evaluate the suggested measurement, the effect of videotaping distance to mm/pixel was analyzed, and the effect of the angle of videotaping and resizing was verified. Also, the usability of the suggested method was evaluated by measuring the dynamic displacement of the masonry wall specimen and the two-story steel frame specimen under the El Centro earthquake loading. Through this, the dynamic displacement and the behavior of the construction can be effectively verified. Therefore, the suggested measurement method, despite the relatively low resolution and video sampling rate, during frozen, slow, and fast seismic motions, has precision and usability that can replace the existing transducer. Particularly, the method can be effectively applied to even the fast behavior such as that of the seismic wave.
Acknowledgement This study has been conducted as long distance cooperation research by KREONET built by KISTI, and the Brain Korea 21
program at Pusan National University’s Division for UbiquitousApplied Construction of Port Logistics Infrastructures and at Yonsei University’s Center for Future Infrastructure.
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