Research Article
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Year 2023, Volume: 27 Issue: 6, 1367 - 1378, 18.12.2023
https://doi.org/10.16984/saufenbilder.1321819

Abstract

References

  • [1] C. S. Fraser, B. Riedel, “Monitoring the thermal deformation of steel beams via vision metrology”, ISPRS Journal of Photogrammetry & Remote Sensing, vol. 55, pp. 268–276, 2000.
  • [2] G. A. Stephen, J. M. W. Brownjohn, C. A. Taylor, “Measurements of static and dynamic displacement from visual monitoring of the Humber Bridge”, Engineering Structures, vol. 15, no. 3, pp. 197–208, 1993.
  • [3] K. Park, S. Kim, H. Park, K. Lee, “The determination of bridge displacement using measured acceleration”, Engineering Structures, vol. 27, pp. 371–378, 2005.
  • [4] J. J. Lee, M. Shinozuka, “A visionbased system for remote sensing of bridge displacement”, NDT&E International, vol. 39, pp. 425–431, 2006.
  • [5] R. Jiang, D. V. Jauregui, K. R. White, “Close-range photogrammetry applications in bridge measurement : Literature review,” Measurement, vol. 41, pp. 823–834, 2008.
  • [6] J. Park, J. Lee, H. Jung, H. Myung, “Vision-based displacement measurement method for high-rise building structures using partitioning approach”, NDT and E International, vol. 43, no. 7, pp. 642–647, 2010.
  • [7] S. Kim, N. Kim, “Multi-point Displacement response measurement of civil infrastructures using digital image Processing”, Procedia Engineering, vol. 14, pp. 195–203, 2011.
  • [8] Y. Yang, C. Dorn, T. Mancini, Z. Talken, G. Kenyon, C. Farrar, D. Mascareñas, “Blind identification of full-field vibration modes from video measu-rements with phase-based video motion magnification”, Mechanical Systems and Signal Processing, vol. 85, pp. 567–590, 2017.
  • [9] J. Javh, J. Slavic, M. Boltez, “High frequency modal identification on noisy high-speed camera data”, Mechanical Systems and Signal Processing, vol. 98, pp. 344–351, 2018.
  • [10] B. Kwan, J. Woo, Y. Kim, T. Cho, H. Seon, “Vision-based system identification technique for building structures using a motion capture system”, Journal of Sound and Vibration, vol. 356, pp. 72–85, 2015.
  • [11] X. W. Ye, T. Yi, C. Z. Dong, T. Liu, “Vision-based structural displacement measurement : System performance evaluation and influence factor analysis”, Measurement, vol. 88, pp. 372–384, 2016.
  • [12] S. Wang, B. Guan, G. Wang, Q. Li, “Measurement of sinusoidal vibration from motion blurred images”, Pattern Recognition Letters, vol. 28, pp. 1029– 1040, 2007.
  • [13] H. Choi, J. Cheung, S. Kim, J. Ahn, “Structural dynamic displacement vision system using digital image processing”, NDT & E International, vol. 44, no. 7, pp. 597–608, 2011.
  • [14] J. G. Chen, N. Wadhwa, Y. Cha, F. Durand, W. T. Freeman, “Modal identification of simple structures with high-speed video using motion magnification”, Journal of Sound and Vibration, vol. 345, pp. 58–71, 2015.
  • [15] J. G. Chen, N. Wadhwa, F. Durand, W. T. Freeman, O. Buyukozturk, “Develop-ments with Motion Magnification for Structural Modal Identification Through Camera Video”, Dynamics of Civil Structures, Conference Proceedings of the Society for Experimental Mechanics Series, vol. 2, pp. 49–57, 2015.
  • [16] P. L. Reu, D. P. Rohe, L. D. Jacobs, “Comparison of DIC and LDV for practical vibration and modal measurements”, Mechanical Systems and Signal Processing, vol. 86, pp. 2–16, 2017.
  • [17] C. Rinaldi, J. Ciambella, V. Gattulli, “Image-based operational modal analysis and damage detection validated in an instrumented smallscale steel frame structure”, Mechanical Systems and Signal Processing, vol. 168, no. May 2021, p. 108640, 2022.
  • [18] D. Feng, M. Q. Feng, "Experimental validation of cost-effective visionbased structural health monitoring", Mechanical Systems and Signal Processing, vol. 88, no. November 2016, pp. 199–211, 2017.
  • [19] H. Chung, J. Liang, S. Kushiyama, M. Shinozuka, "Digital image processing for nonlinear system identification", International Journal of Non-Linear Mechanics, vol. 39, pp. 691–707, 2004.
  • [20] C. T. Do Cabo, N. A. Valente, Z. Mao, “A Comparative Analysis of Imaging Processing Techniques for NonInvasive Structural Health Monitoring”, IFAC-PapersOnLine, vol. 55, no. 27, pp. 150–154, 2022.
  • [21] E. Damcı, Ç. Şekerci, “Development of a Low-Cost Single-Axis Shake Table Based on Arduino”, Experimental Techniques, vol. 43, no. 2, pp. 179–198, 2019.
  • [22] S. Antoniou, R. Pinho, F. Bianchi, “SeismoSignal” 2008.
  • [23] Matlab, “version 2017b” The MathWorks, Inc., Natick, 2017.
  • [24] A. K. Chopra, Dynamics of Structures Theory and Applications to Earthquake Engineering, Englewood Cliffs, New Jersey: Prentice-Hall, Inc., 1995.
  • [25] PEER Center. (2016, Feb. 01). PEER Ground Motion Database [Online]. Available: http://ngawest2.berkeley.edu/

Determination of the Dynamic Properties of SDOF and MDOF Shear Frames with Image Processing Technique

Year 2023, Volume: 27 Issue: 6, 1367 - 1378, 18.12.2023
https://doi.org/10.16984/saufenbilder.1321819

Abstract

In this study, experimental modal analyses on shear frame models consisting of single and multi-degree-of-freedom structure models were carried out to examine structural behavior. The image processing technique is used for the tests on shaking tables, such as free vibration, simple harmonic, and strong ground motion. An approach is proposed for image processing techniques to consider the appropriate filter size. The experiments aimed to determine the displacements at the floor levels and the dynamic characteristics of the structure models. To determine the displacements and frequency responses, results obtained from three different methods, namely the data obtained by accelerometers, image processing technique, and theoretical calculations, were compared. It has been shown that the image processing technique is a good tool compared to frequently used vibration measurements with accelerometers. It is advantageous because it is easier to implement for laboratory experiments and less costly.

Supporting Institution

Scientific Research Projects Coordination Unit of Istanbul University-Cerrahpasa

Thanks

The Authors would like to thank undergraduate students Barış Can Akkoçak, Ahmet Ali Öztürk and Emrecan Çelik for their assistance during the project (FLO-2017-27219) study in 2018.

References

  • [1] C. S. Fraser, B. Riedel, “Monitoring the thermal deformation of steel beams via vision metrology”, ISPRS Journal of Photogrammetry & Remote Sensing, vol. 55, pp. 268–276, 2000.
  • [2] G. A. Stephen, J. M. W. Brownjohn, C. A. Taylor, “Measurements of static and dynamic displacement from visual monitoring of the Humber Bridge”, Engineering Structures, vol. 15, no. 3, pp. 197–208, 1993.
  • [3] K. Park, S. Kim, H. Park, K. Lee, “The determination of bridge displacement using measured acceleration”, Engineering Structures, vol. 27, pp. 371–378, 2005.
  • [4] J. J. Lee, M. Shinozuka, “A visionbased system for remote sensing of bridge displacement”, NDT&E International, vol. 39, pp. 425–431, 2006.
  • [5] R. Jiang, D. V. Jauregui, K. R. White, “Close-range photogrammetry applications in bridge measurement : Literature review,” Measurement, vol. 41, pp. 823–834, 2008.
  • [6] J. Park, J. Lee, H. Jung, H. Myung, “Vision-based displacement measurement method for high-rise building structures using partitioning approach”, NDT and E International, vol. 43, no. 7, pp. 642–647, 2010.
  • [7] S. Kim, N. Kim, “Multi-point Displacement response measurement of civil infrastructures using digital image Processing”, Procedia Engineering, vol. 14, pp. 195–203, 2011.
  • [8] Y. Yang, C. Dorn, T. Mancini, Z. Talken, G. Kenyon, C. Farrar, D. Mascareñas, “Blind identification of full-field vibration modes from video measu-rements with phase-based video motion magnification”, Mechanical Systems and Signal Processing, vol. 85, pp. 567–590, 2017.
  • [9] J. Javh, J. Slavic, M. Boltez, “High frequency modal identification on noisy high-speed camera data”, Mechanical Systems and Signal Processing, vol. 98, pp. 344–351, 2018.
  • [10] B. Kwan, J. Woo, Y. Kim, T. Cho, H. Seon, “Vision-based system identification technique for building structures using a motion capture system”, Journal of Sound and Vibration, vol. 356, pp. 72–85, 2015.
  • [11] X. W. Ye, T. Yi, C. Z. Dong, T. Liu, “Vision-based structural displacement measurement : System performance evaluation and influence factor analysis”, Measurement, vol. 88, pp. 372–384, 2016.
  • [12] S. Wang, B. Guan, G. Wang, Q. Li, “Measurement of sinusoidal vibration from motion blurred images”, Pattern Recognition Letters, vol. 28, pp. 1029– 1040, 2007.
  • [13] H. Choi, J. Cheung, S. Kim, J. Ahn, “Structural dynamic displacement vision system using digital image processing”, NDT & E International, vol. 44, no. 7, pp. 597–608, 2011.
  • [14] J. G. Chen, N. Wadhwa, Y. Cha, F. Durand, W. T. Freeman, “Modal identification of simple structures with high-speed video using motion magnification”, Journal of Sound and Vibration, vol. 345, pp. 58–71, 2015.
  • [15] J. G. Chen, N. Wadhwa, F. Durand, W. T. Freeman, O. Buyukozturk, “Develop-ments with Motion Magnification for Structural Modal Identification Through Camera Video”, Dynamics of Civil Structures, Conference Proceedings of the Society for Experimental Mechanics Series, vol. 2, pp. 49–57, 2015.
  • [16] P. L. Reu, D. P. Rohe, L. D. Jacobs, “Comparison of DIC and LDV for practical vibration and modal measurements”, Mechanical Systems and Signal Processing, vol. 86, pp. 2–16, 2017.
  • [17] C. Rinaldi, J. Ciambella, V. Gattulli, “Image-based operational modal analysis and damage detection validated in an instrumented smallscale steel frame structure”, Mechanical Systems and Signal Processing, vol. 168, no. May 2021, p. 108640, 2022.
  • [18] D. Feng, M. Q. Feng, "Experimental validation of cost-effective visionbased structural health monitoring", Mechanical Systems and Signal Processing, vol. 88, no. November 2016, pp. 199–211, 2017.
  • [19] H. Chung, J. Liang, S. Kushiyama, M. Shinozuka, "Digital image processing for nonlinear system identification", International Journal of Non-Linear Mechanics, vol. 39, pp. 691–707, 2004.
  • [20] C. T. Do Cabo, N. A. Valente, Z. Mao, “A Comparative Analysis of Imaging Processing Techniques for NonInvasive Structural Health Monitoring”, IFAC-PapersOnLine, vol. 55, no. 27, pp. 150–154, 2022.
  • [21] E. Damcı, Ç. Şekerci, “Development of a Low-Cost Single-Axis Shake Table Based on Arduino”, Experimental Techniques, vol. 43, no. 2, pp. 179–198, 2019.
  • [22] S. Antoniou, R. Pinho, F. Bianchi, “SeismoSignal” 2008.
  • [23] Matlab, “version 2017b” The MathWorks, Inc., Natick, 2017.
  • [24] A. K. Chopra, Dynamics of Structures Theory and Applications to Earthquake Engineering, Englewood Cliffs, New Jersey: Prentice-Hall, Inc., 1995.
  • [25] PEER Center. (2016, Feb. 01). PEER Ground Motion Database [Online]. Available: http://ngawest2.berkeley.edu/

Details

Primary Language English
Subjects Civil Engineering (Other)
Journal Section Research Articles
Authors

Erdem DAMCI 0000-0003-2295-1686

Çağla ŞEKERCİ 0000-0001-7070-1804

Project Number FLO-2017-27219
Early Pub Date December 1, 2023
Publication Date December 18, 2023
Submission Date July 2, 2023
Acceptance Date September 11, 2023
Published in Issue Year 2023 Volume: 27 Issue: 6

Cite

APA DAMCI, E., & ŞEKERCİ, Ç. (2023). Determination of the Dynamic Properties of SDOF and MDOF Shear Frames with Image Processing Technique. Sakarya University Journal of Science, 27(6), 1367-1378. https://doi.org/10.16984/saufenbilder.1321819
AMA DAMCI E, ŞEKERCİ Ç. Determination of the Dynamic Properties of SDOF and MDOF Shear Frames with Image Processing Technique. SAUJS. December 2023;27(6):1367-1378. doi:10.16984/saufenbilder.1321819
Chicago DAMCI, Erdem, and Çağla ŞEKERCİ. “Determination of the Dynamic Properties of SDOF and MDOF Shear Frames With Image Processing Technique”. Sakarya University Journal of Science 27, no. 6 (December 2023): 1367-78. https://doi.org/10.16984/saufenbilder.1321819.
EndNote DAMCI E, ŞEKERCİ Ç (December 1, 2023) Determination of the Dynamic Properties of SDOF and MDOF Shear Frames with Image Processing Technique. Sakarya University Journal of Science 27 6 1367–1378.
IEEE E. DAMCI and Ç. ŞEKERCİ, “Determination of the Dynamic Properties of SDOF and MDOF Shear Frames with Image Processing Technique”, SAUJS, vol. 27, no. 6, pp. 1367–1378, 2023, doi: 10.16984/saufenbilder.1321819.
ISNAD DAMCI, Erdem - ŞEKERCİ, Çağla. “Determination of the Dynamic Properties of SDOF and MDOF Shear Frames With Image Processing Technique”. Sakarya University Journal of Science 27/6 (December 2023), 1367-1378. https://doi.org/10.16984/saufenbilder.1321819.
JAMA DAMCI E, ŞEKERCİ Ç. Determination of the Dynamic Properties of SDOF and MDOF Shear Frames with Image Processing Technique. SAUJS. 2023;27:1367–1378.
MLA DAMCI, Erdem and Çağla ŞEKERCİ. “Determination of the Dynamic Properties of SDOF and MDOF Shear Frames With Image Processing Technique”. Sakarya University Journal of Science, vol. 27, no. 6, 2023, pp. 1367-78, doi:10.16984/saufenbilder.1321819.
Vancouver DAMCI E, ŞEKERCİ Ç. Determination of the Dynamic Properties of SDOF and MDOF Shear Frames with Image Processing Technique. SAUJS. 2023;27(6):1367-78.

Sakarya University Journal of Science (SAUJS)