Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures

  • Lee, Jong Jae (Department of Civil & Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Fukuda, Yoshio (Department of Civil & Environmental Engineering, University of California Irvine) ;
  • Shinozuka, Masanobu (Department of Civil & Environmental Engineering, University of California Irvine) ;
  • Cho, Soojin (Department of Civil & Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Yun, Chung-Bang (Department of Civil & Environmental Engineering, Korea Advanced Institute of Science and Technology)
  • Received : 2006.10.31
  • Accepted : 2006.11.28
  • Published : 2007.07.25
⟨ Previous Next ⟩

Abstract

For structural health monitoring (SHM) of civil infrastructures, displacement is a good descriptor of the structural behavior under all the potential disturbances. However, it is not easy to measure displacement of civil infrastructures, since the conventional sensors need a reference point, and inaccessibility to the reference point is sometimes caused by the geographic conditions, such as a highway or river under a bridge, which makes installation of measuring devices time-consuming and costly, if not impossible. To resolve this issue, a visionbased real-time displacement measurement system using digital image processing techniques is developed. The effectiveness of the proposed system was verified by comparing the load carrying capacities of a steel-plate girder bridge obtained from the conventional sensor and the present system. Further, to simultaneously measure multiple points, a synchronized vision-based system is developed using master/slave system with wireless data communication. For the purpose of verification, the measured displacement by a synchronized vision-based system was compared with the data measured by conventional contact-type sensors, linear variable differential transformers (LVDT) from a laboratory test.

Keywords

References

  1. Brincker, R., Zhang, L. and Andersen, P. (2000), "Modal identification from ambient response using frequency domain decomposition", Proceedings of 18th International Modal Analysis Conference, 625-630, San Antonio, TX, USA.
  2. Çelibi, M. (2000), "GPS in dynamic monitoring of long-period structures", Soil Dyn. Earthq. Eng., 20, 477-483. https://doi.org/10.1016/S0267-7261(00)00094-4
  3. Faulkner, B. C., Furman, W. B., Thomas, T. B. and Wallace, T. M. (1996), "Determination of bridge response using acceleration data", Report No. FHWA/VA-97-R5 1996; Virginia Department of Transportation.
  4. Knecht, A. and Manetti, L. (2001), "Using GPS in structural health monitoring", Proc. SPIE's 8th Annu. Symp. on Smart Structures and Materials, Newport Beach, CA.
  5. Lee, J. J. and Shinozuka, M. (2006), "A vision-based system for remote sensing of bridge", NDT&E Inter., 39(5), 425-431. https://doi.org/10.1016/j.ndteint.2005.12.003
  6. Ministry of Construction and Transportation (1996), A guide to Safety Inspection and Detailed Safety Assessment, Seoul, Korea, 1996.
  7. Ministry of Construction and Transportation (2005), Standard Design Specification of Highway Bridges, Seoul, Korea, 2005.
  8. Nakamura, S. (2000), "GPS measurement of wind-induced suspension bridge girder displacements", J. Struct. Eng., 126, 1413-1419. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:12(1413)
  9. Nassif, H. H., Gindy, M., and Davis, J. (2005), "Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration", NDT&E Inter., 38, 213-218. https://doi.org/10.1016/j.ndteint.2004.06.012
  10. Olaszek, P. (1999), "Investigation of the dynamic characteristic of bridge structures using a computer vision method", Measurement, 25, 227-236. https://doi.org/10.1016/S0263-2241(99)00006-8
  11. Otte, D., Van de Ponseele, P. and Leuridan, J. (1990), "Operational shapes estimation as a function of dynamic loads", Proceedings of the 8th International Modal Analysis Conference, 413-421.
  12. Park, K. T., Kim, S. H., Park, H. S. and Lee, K. W. (2005), "The determination of bridge displacement using measured acceleration", Eng. Struct., 27, 371-378. https://doi.org/10.1016/j.engstruct.2004.10.013
  13. Roberts, G. W. and Dodson, A. H. (1998), "A remote bridge health monitoring system using computational simulation and GPS sensor data", Seismol. Soc. Am. 25, 299-308.
  14. Stephen, G. A., Brownjohn, J. M. W. and Taylor, C. A. (1993), "Visual monitoring of the humber bridge", Eng. Struct., 15, 197-208. https://doi.org/10.1016/0141-0296(93)90054-8
  15. Wahbeh, A. M., Caffrey, J. P. and Masri, S. F. (2003), "A vision-based approach for the direct measurement of displacements in vibrating systems", Smart Mater. Struct. 12, 785-794. https://doi.org/10.1088/0964-1726/12/5/016
  16. Xu, L., Guo, J. J. and Jiang, J. J. (2002), "Time-frequency analysis of a suspension bridge based on GPS", J. Sound Vib., 254(1), 105-116. https://doi.org/10.1006/jsvi.2001.4087

Cited by

  1. Uncertainty analysis of high frequency image-based vibration measurements vol.46, pp.8, 2013, https://doi.org/10.1016/j.measurement.2013.04.075
  2. A Synchronized Multipoint Vision-Based System for Displacement Measurement of Civil Infrastructures vol.2012, 2012, https://doi.org/10.1100/2012/519146
  3. Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model vol.15, pp.3, 2015, https://doi.org/10.12989/sss.2015.15.3.645
  4. Dynamics-Based Stereo Visual Inspection Using Multidimensional Modal Analysis vol.13, pp.12, 2013, https://doi.org/10.1109/JSEN.2013.2276620
  5. Monitoring of Structures and Mechanical Systems Using Virtual Visual Sensors for Video Analysis: Fundamental Concept and Proof of Feasibility vol.13, pp.12, 2013, https://doi.org/10.3390/s131216551
  6. Verification on the Application of Monitoring for Frame Structures Using the VRS-RTK Method through the Free Vibration Test vol.18, pp.1, 2014, https://doi.org/10.11112/jksmi.2014.18.1.174
  7. Structural Optimization of a Novel 6-DOF Pose Sensor System for Enhancing Noise Robustness at a Long Distance vol.61, pp.10, 2014, https://doi.org/10.1109/TIE.2013.2297307
  8. Structural displacement and strain monitoring based on the edge detection operator vol.20, pp.2, 2017, https://doi.org/10.1177/1369433216660220
  9. Structural dynamic displacement vision system using digital image processing vol.44, pp.7, 2011, https://doi.org/10.1016/j.ndteint.2011.06.003
  10. Vision-based system identification technique for building structures using a motion capture system vol.356, 2015, https://doi.org/10.1016/j.jsv.2015.07.011
  11. Application of Local Reference-Free Damage Detection Techniques to In Situ Bridges vol.140, pp.3, 2014, https://doi.org/10.1061/(ASCE)ST.1943-541X.0000846
  12. Smart structure technologies for civil infrastructures in Korea: recent research and applications vol.7, pp.9, 2011, https://doi.org/10.1080/15732470902720109
  13. Rapid full-scale expansion joint monitoring using wireless hybrid sensor vol.12, pp.3_4, 2013, https://doi.org/10.12989/sss.2013.12.3_4.415
  14. Vision-Based Displacement Measurement System Operable at Arbitrary Positions vol.18, pp.6, 2014, https://doi.org/10.11112/jksmi.2014.18.6.123
  15. Data Reconstruction Based on a Generalized Orthogonal Polynomial Function vol.17, pp.5, 2014, https://doi.org/10.1260/1369-4332.17.5.659
  16. Cost-effective vision-based system for monitoring dynamic response of civil engineering structures vol.17, pp.8, 2010, https://doi.org/10.1002/stc.360
  17. Relative Displacement Sensing Techniques for Postevent Structural Damage Assessment: Review vol.139, pp.9, 2013, https://doi.org/10.1061/(ASCE)ST.1943-541X.0000729
  18. Structural Safety Assessment of Existing Multiarch Tunnel: A Case Study vol.2017, 2017, https://doi.org/10.1155/2017/1697041
  19. Vibration frequency measurement using a local multithreshold technique vol.21, pp.22, 2013, https://doi.org/10.1364/OE.21.026198
  20. Seismic Response Measurement of an Under-Water Model Through High Speed Camera and Feature Tracking vol.40, pp.1, 2016, https://doi.org/10.1007/s40799-016-0013-0
  21. Contactless Bridge Weigh-in-Motion vol.21, pp.7, 2016, https://doi.org/10.1061/(ASCE)BE.1943-5592.0000776
  22. Integration of Structural Health Monitoring and Intelligent Transportation Systems for Bridge Condition Assessment: Current Status and Future Direction vol.17, pp.8, 2016, https://doi.org/10.1109/TITS.2016.2520499
  23. A Review of Machine Vision-Based Structural Health Monitoring: Methodologies and Applications vol.2016, 2016, https://doi.org/10.1155/2016/7103039
  24. Paired Structured Light for Structural Health Monitoring Robot System vol.10, pp.1, 2011, https://doi.org/10.1177/1475921710365413
  25. Measurement of the Dynamic Displacements of Railway Bridges Using Video Technology vol.24, 2015, https://doi.org/10.1051/matecconf/20152402007
  26. Experimental Verification for Cable Force Estimation Using Handheld Shooting of Smartphones vol.2017, 2017, https://doi.org/10.1155/2017/5625396
  27. A Vision-Based Dynamic Rotational Angle Measurement System for Large Civil Structures vol.12, pp.12, 2012, https://doi.org/10.3390/s120607326
  28. Simplified Technique for Remote Monitoring of Deflection in Arch Structures vol.37, pp.1, 2013, https://doi.org/10.1111/j.1747-1567.2011.00766.x
  29. Hybrid Sensor-Camera Monitoring for Damage Detection: Case Study of a Real Bridge vol.21, pp.6, 2016, https://doi.org/10.1061/(ASCE)BE.1943-5592.0000811
  30. Developing Accurate Long Distance 6-DOF Motion Detection with 1-D Laser Sensors: Three-Beam Detection System 2013, https://doi.org/10.1109/TIE.2012.2200225
  31. Vibration Monitoring of Multiple Bridge Points by Means of a Unique Vision-Based Measuring System vol.54, pp.2, 2014, https://doi.org/10.1007/s11340-013-9784-8
  32. 3D displacement measurement model for health monitoring of structures using a motion capture system vol.59, 2015, https://doi.org/10.1016/j.measurement.2014.09.063
  33. Automated condition assessment of concrete bridges with digital imaging vol.13, pp.6, 2014, https://doi.org/10.12989/sss.2014.13.6.901
  34. A vision-based system for measuring the displacements of large structures: Simultaneous adaptive calibration and full motion estimation vol.72-73, 2016, https://doi.org/10.1016/j.ymssp.2015.10.033
  35. Vision-Based Natural Frequency Identification Using Laser Speckle Imaging and Parallel Computing vol.33, pp.1, 2018, https://doi.org/10.1111/mice.12312
  36. Calibration methodology of a vision system for measuring the displacements of long-deck suspension bridges vol.19, pp.3, 2012, https://doi.org/10.1002/stc.438
  37. Field evaluation of optical-based three-dimensional dynamic motion measurement system with multiple targets for a floating structure vol.62, 2013, https://doi.org/10.1016/j.oceaneng.2012.12.046
  38. Completely contactless structural health monitoring of real-life structures using cameras and computer vision vol.24, pp.1, 2017, https://doi.org/10.1002/stc.1852
  39. Design improvement of the three-beam detector towards a precise long-range 6-degree of freedom motion sensor system vol.85, pp.1, 2014, https://doi.org/10.1063/1.4861915
  40. Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review vol.156, 2018, https://doi.org/10.1016/j.engstruct.2017.11.018
  41. Nontarget Stereo Vision Technique for Spatiotemporal Response Measurement of Line-Like Structures vol.134, pp.6, 2008, https://doi.org/10.1061/(ASCE)0733-9399(2008)134:6(466)
  42. Seismic structural displacement measurement using a line-scan camera: camera-pattern calibration and experimental validation vol.1, pp.3-4, 2011, https://doi.org/10.1007/s13349-011-0012-x
  43. Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system vol.75, 2014, https://doi.org/10.1016/j.engstruct.2014.04.051
  44. Videogrammetric technique for three-dimensional structural progressive collapse measurement vol.63, 2015, https://doi.org/10.1016/j.measurement.2014.11.023
  45. Computer vision-based displacement and vibration monitoring without using physical target on structures vol.13, pp.4, 2017, https://doi.org/10.1080/15732479.2016.1164729
  46. A three-step displacement measurement method for a 3-DOF macro-micro positioning stage vol.89, pp.11, 2018, https://doi.org/10.1063/1.5046700
  47. Bolt loosening angle detection technology using deep learning pp.15452255, 2019, https://doi.org/10.1002/stc.2292
  48. Performance of videogrammetric displacement monitoring technique under varying ambient temperature pp.2048-4011, 2019, https://doi.org/10.1177/1369433218822089
  49. Rapid-to-deploy reconfigurable wireless structural monitoring systems using extended-range wireless sensors vol.6, pp.5, 2007, https://doi.org/10.12989/sss.2010.6.5_6.505
  50. 다중 표적을 이용한 부유식 구조물의 영상 기반 동적 응답 계측 vol.32, pp.a1, 2007, https://doi.org/10.12652/ksce.2012.32.1a.019
  51. Comparative study on displacement measurement sensors for high-speed railroad bridge vol.21, pp.5, 2007, https://doi.org/10.12989/sss.2018.21.5.637
  52. Effective Heterogeneous Data Fusion procedure via Kalman filtering vol.22, pp.5, 2018, https://doi.org/10.12989/sss.2018.22.5.631
  53. Cosmic ray tracking to monitor the stability of historical buildings: a feasibility study vol.30, pp.4, 2007, https://doi.org/10.1088/1361-6501/ab00d7
  54. Acceleration of object tracking in high‐speed videogrammetry using a parallel OpenMP and SIMD strategy vol.34, pp.166, 2019, https://doi.org/10.1111/phor.12279
  55. Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles vol.9, pp.14, 2007, https://doi.org/10.3390/app9142881
  56. Non-contact structural health monitoring of a cable-stayed bridge: case study vol.15, pp.8, 2019, https://doi.org/10.1080/15732479.2019.1609529
  57. A Displacement Sensor Based on a Normal Mode Helical Antenna vol.19, pp.17, 2007, https://doi.org/10.3390/s19173767
  58. A vision-based system for long-distance remote monitoring of dynamic displacement: experimental verification on a supertall structure vol.24, pp.6, 2007, https://doi.org/10.12989/sss.2019.24.6.769
  59. An experimental study of the feasibility of phase‐based video magnification for damage detection and localisation in operational deflection shapes vol.56, pp.1, 2007, https://doi.org/10.1111/str.12336
  60. Exploration of temperature effect on videogrammetric technique for displacement monitoring vol.25, pp.2, 2007, https://doi.org/10.12989/sss.2020.25.2.135
  61. A portable monitoring approach using cameras and computer vision for bridge load rating in smart cities vol.10, pp.5, 2007, https://doi.org/10.1007/s13349-020-00431-2
  62. Vibration Signal-based Structural Damage Detection through Deep Learning and Digital Image Correlation vol.719, pp.2, 2007, https://doi.org/10.1088/1755-1315/719/2/022047
  63. Non-Destructive Testing Applications for Steel Bridges vol.11, pp.20, 2007, https://doi.org/10.3390/app11209757
  64. A marker-free method for structural dynamic displacement measurement based on optical flow vol.18, pp.1, 2007, https://doi.org/10.1080/15732479.2020.1835999