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Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

邮发代号 80-968

2019 Impact Factor: 1.68

Frontiers of Structural and Civil Engineering  2016, Vol. 10 Issue (1): 12-21   https://doi.org/10.1007/s11709-016-0313-6
  本期目录
Image analyses for video-based remote structure vibration monitoring system
Yang YANG1,Xiong (Bill) YU2,*()
1. Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Bingham 203d, Cleveland, OH 44106-7201, USA
2. Department of Civil Engineering, Case Western Reserve University, 10900 Euclid Avenue, Bingham 210, Cleveland, OH 44106-7201, USA
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Abstract

Video-based vibration measurement is a cost-effective way for remote monitoring the health conditions of transportation and other civil structures, especially for situations where accessibility is restricted and does not allow installation of conventional monitoring devices. Besides, video-based system is global measurement. The technical basis of video-based remote vibration measurement system is digital image analysis. Comparison of the images allow the field of motion to be accurately delineated. Such information is important to understand the structure behaviors including the motion and strain distribution. This paper presents system and analyses to monitor the vibration velocity and displacement field. The performance is demonstrated on a testbed of model building. Three different methods (i.e., frame difference method, particle image velocimetry, and optical Flow Method) are utilized to analyze the image sequences to extract the feature of motion. The Performance is validated using accelerometer data. The results indicate that all three methods can estimate the velocity field of the model building, although the results can be affected by factors such as background noise and environmental interference. Optical flow method achieved the best performance among these three methods studied. With further refinement of system hardware and image processing software, it will be developed into a remote video based monitoring system for structural health monitoring of transportation infrastructure to assist the diagnoses of its health conditions.

Key wordsstructure health monitoring    velocity estimation    frame difference    PIV    optical-flow method
收稿日期: 2015-08-24      出版日期: 2016-01-19
Corresponding Author(s): Xiong (Bill) YU   
 引用本文:   
. [J]. Frontiers of Structural and Civil Engineering, 2016, 10(1): 12-21.
Yang YANG,Xiong (Bill) YU. Image analyses for video-based remote structure vibration monitoring system. Front. Struct. Civ. Eng., 2016, 10(1): 12-21.
 链接本文:  
https://academic.hep.com.cn/fsce/CN/10.1007/s11709-016-0313-6
https://academic.hep.com.cn/fsce/CN/Y2016/V10/I1/12
accelerometer1 accelerometer2 accelerometer3 accelerometer4
x-direction scale 0.033 0.033 0.033 0.033
offset 1.650 1.633 1.644 1.629
y-direction scale 0.033 0.033 0.033 0.033
offset 1.596 1.626 1.622 1.630
z-direction scale 0.033 0.033 0.033 0.033
offset 1.629 1.677 1.606 1.667
Tab.1  
Fig.1  
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measurement accuracy robust to noise computational speed complex motion measurement
frame difference (~) (-) (+) (-)
PIV (~) (~) (~) (+)
optical flow (+) (~) (~) (+)
Tab.2  
1 LeBlanc  B, Niezrecki  C, Avitabile  P. Structural health monitoring of helicopter hard landing using 3D digital image correlation. Health Monitoring of Structural and Biological Systems 2010, Pts 1 and 2, 2010, 7650: 89–98
2 Bragge  T, Hakkarainen  M, Liikavainio  T, Arokoski  J, Karjalaiene  P. Calibration of triaxial accelerometer by determining sensitivity matric and offsets simultaneously. In: Proceedings of the 1st Joint ESMAC-GCMAS Meeting. Amsterdam, the Netherlands, 2006
3 Arraigada  M. Calculation of displacement of measured accelerations, analysis of two accelerometers and application in road engineering. In: Proceedings of 6th Swiss Transport Research Conference. Monter Verita, Ascona, 2006
4 Hamid  M A.,Abdullah-AI-Wadud   M, Alam Muhammad   Mahbub. A reliable structural health monitoring protocol using wireless sensor networks. In: Proceedings of 14th International Conference on Computer and Information Technology. 2011, 601–606
5 Kapur  J, Sahoo  P K, Wong  A. A new method for gray level perjure Thresholding using the entropy of the histogram. In: Proceeding of 7th International Conference on Computing and Convergence Technology (ICCCT). 1985, 29: 273–285
6 Kumar  S. 2D maximum entropy method for image Thresholding converge with differential evolution. Advances in Mechanical Engineering and its Applications, 2012, 2(3): 289–292
7 Bailey  D G. Pixel calibration techniques. Proceedings of the New Zealand Image and Vision Computing Workshop, 1995
8 Wereley  S T, Gui  L. A correlation-based central difference image correction (CDIC) method and application in a four-roll mill flow PIV measurement. Experiments in Fluids, 2003, 24(1): 42–51
9 Willert  C E, Gharib  M. Digital particle image velocimetry. Experiments in Fluids, 1991, 10(4): 181–193
10 Quénot  G M, Pakleza  J, Kowalewski  T A. Particle image velocimetry with optical flow. Experiments in Fluids, 1998, 25(3): 177–189
11 Moodley  K, Murrell  H. A color-map plugin for the open source, java based, image processing package, ImageJ. Computers & Geosciences, 2004, 30(6): 609–618
12 Igathinathane  C, Pordesimo  L O, Columbus  E P, Batchelor  W D, Methuku  S R. Shape identification and particles size distribution from basic shape parameters using ImageJ. Computers and Electronics in Agriculture, 2008, 63(2): 168–182
13 Ruhnau  P, Kohlberger  T, Schnorr  C, Nobach  H. Variational optical flow estimation for particle image velocimetry. Experiments in Fluids, 2005, 38(1): 21–32
14 Angelini  E D, Gerard  O. Review of myocardial motion estimation methods from optical flow tracking on ultrasound data. In: the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology society, 2006, 1(15): 6337–6340
15 Barron  J L, Fleet  D J, Beauchemin  S S. Performance of optical flow techniques. International Journal of Computer Vision, 1994, 12(1): 43–77
16 Rocha  F R P, Raimundo  I M Jr, Teixeira  L S G. Direct sold-phase optical measurements in flow systems: a review. Analytical Letters, 2011, 44(1): 528–559
17 Horn  B K P, Schunck  B G. Determining optical flow. Artificial Intelligence, 1981, 17(1): 185–203
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