• Tidak ada hasil yang ditemukan

5. Conclusions and Future Work

5.2. Future work

67

68

REFERENCES

1. H. Li et al., “Structural health monitoring system for the Shandong Binzhou Yellow River Highway Bridge,” Comput. Civ. Infrastruct. Eng., 2006.

2. H. Li and J. Ou, “The state of the art in structural health monitoring of cable-stayed bridges,” J.

Civ. Struct. Heal. Monit., 2016.

3. J.-W. Park, S.-H. Sim, and H.-J. Jung, “Displacement Estimation Using Multimetric Data Fusion,”

IEEE/ASME Trans. Mechatronics, vol. 18, no. 6, pp. 1675–1682, Dec. 2013.

4. T. Sanpei and T. Mizoguchi, “Fundamental study for real-time detection of sudden displacement by high-speed laser scanner,” J. Struct. Integr. Maint., vol. 3, no. 4, pp. 227–232, Oct. 2018.

5. American Association of State Highway and Transportation Officials, “AASHTO LRFD bridge design specifications,” customary U.S. units. 2012.

6. M.R. Kaczinski, “Steel Bridge Design Handbook: Bearing Design (No. FHWA-HIF-16-002- Vol.15)”, United States, Federal Highway Administration, Office of Bridges and Structures, 2016.

7. S. Cho, C.-B. Yun, and S.-H. Sim, “Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model,” Smart Struct. Syst., vol. 15, no. 3, pp. 645–663, Mar. 2015.

8. M. Yu, J. Guo, and K.-M. Lee, “A Modal Expansion Method for Displacement and Strain Field Reconstruction of a Thin-Wall Component During Machining,” IEEE/ASME Trans. Mechatronics, vol. 23, no. 3, pp. 1028–1037, Jun. 2018.

9. M. Aguero, A. Ozdagli, and F. Moreu, “Measuring Reference-Free Total Displacements of Piles and Columns Using Low-Cost, Battery-Powered, Efficient Wireless Intelligent Sensors (LEWIS2),”

Sensors, vol. 19, no. 7. Mar. 2019.

10. H. Pan, X. Jing, W. Sun, and Z. Li, “Analysis and Design of a Bioinspired Vibration Sensor System in Noisy Environment,” IEEE/ASME Trans. Mechatronics, vol. 23, no. 2, pp. 845–855, Feb. 2018.

11. J. Lee, K.-C. Lee, S. Cho, and S.-H. Sim, “Computer Vision-Based Structural Displacement Measurement Robust to Light-Induced Image Degradation for In-Service Bridges,” Sensors, vol.

17, no. 10, p. 2317, Oct. 2017.

12. J.-W. Park, J.-J. Lee, H.-J. Jung, and H. Myung, “Vision-based displacement measurement method for high-rise building structures using partitioning approach,” NDT E Int., vol. 43, no. 7, pp. 642–

647, Oct. 2010.

13. J. J. Lee and M. Shinozuka, “A vision-based system for remote sensing of bridge displacement,”

NDT E Int., vol. 39, no. 5, pp. 425–431, Jul. 2006.

69

14. H.-S. Choi, J.-H. Cheung, S.-H. Kim, and J.-H. Ahn, “Structural dynamic displacement vision system using digital image processing,” NDT E Int., vol. 44, no. 7, pp. 597–608, Nov. 2011.

15. H. Jeon, Y. Bang, and H. Myung, “A paired visual servoing system for 6-DOF displacement measurement of structures,” Smart Mater. Struct., vol. 20, no. 4, p. 045019, Apr. 2011.

16. D. Feng, M. Feng, E. Ozer, and Y. Fukuda, “A Vision-Based Sensor for Noncontact Structural Displacement Measurement,” Sensors, vol. 15, no. 7, pp. 16557–16575, Jul. 2015.

17. D. Ribeiro, R. Calçada, J. Ferreira, and T. Martins, “Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system,” Eng. Struct., vol. 75, pp.

164–180, Sep. 2014.

18. A. M. Wahbeh, J. P. Caffrey, and S. F. Masri, “A vision-based approach for the direct measurement of displacements in vibrating systems,” Smart Mater. Struct., vol. 12, no. 5, pp. 785–794, Oct. 2003.

19. S. Yoneyama and H. Ueda, “Bridge deflection measurement using digital image correlation with camera movement correction,” Mater. Trans., vol. 53, no. 2, pp. 285–290, 2012.

20. J. Lee, K.-C. Lee, S. Jeong, Y.-J. Lee, and S.-H. Sim, “Long-term displacement measurement of full-scale bridges using camera ego-motion compensation,” Mech. Syst. Signal Process., vol. 140, p. 106651, Jun. 2020.

21. Y. Fukuda, M. Q. Feng, Y. Narita, S. Kaneko, and T. Tanaka, “Vision-Based Displacement Sensor for Monitoring Dynamic Response Using Robust Object Search Algorithm,” IEEE Sens. J., vol.

13, no. 12, pp. 4725–4732, Dec. 2013.

22. G. Busca, A. Cigada, P. Mazzoleni, M. Tarabini, and E. Zappa, “Static and Dynamic Monitoring of Bridges by Means of Vision-Based Measuring System,” in Conference Proceedings of the Society for Experimental Mechanics Series, 2013, pp. 83–92.

23. T. Khuc and F. N. Catbas, “Computer vision-based displacement and vibration monitoring without using physical target on structures,” Struct. Infrastruct. Eng., vol. 13, no. 4, pp. 505–516, Apr. 2017.

24. I. Choi, J. Kim, and D. Kim, “A Target-Less Vision-Based Displacement Sensor Based on Image Convex Hull Optimization for Measuring the Dynamic Response of Building Structures,” Sensors, vol. 16, no. 12, p. 2085, Dec. 2016.

25. M. Q. Feng, Y. Fukuda, D. Feng, and M. Mizuta, “Nontarget Vision Sensor for Remote Measurement of Bridge Dynamic Response,” J. Bridg. Eng., vol. 20, no. 12, p. 04015023, Dec.

2015.

26. Y. F. Ji and C. C. Chang, “Nontarget Image-Based Technique for Small Cable Vibration Measurement,” J. Bridg. Eng., vol. 13, no. 1, pp. 34–42, Jan. 2008.

27. H. Yoon, H. Elanwar, H. Choi, M. Golparvar-Fard, and B. F. Spencer, “Target-free approach for

70

vision-based structural system identification using consumer-grade cameras,” Struct. Control Heal.

Monit., vol. 23, no. 12, pp. 1405–1416, Dec. 2016.

28. Y. F. Ji and C. C. Chang, “Nontarget Stereo Vision Technique for Spatiotemporal Response Measurement of Line-Like Structures,” J. Eng. Mech., vol. 134, no. 6, pp. 466–474, Jun. 2008.

29. Y. Xu, J. Brownjohn, and D. Kong, “A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge,” Struct. Control Heal. Monit., 2018.

30. C.-C. Chen, H.-Z. Tseng, W.-H. Wu, and C.-H. Chen, “Modal Frequency Identification of Stay Cables with Ambient Vibration Measurements Based on Nontarget Image Processing Techniques,”

Adv. Struct. Eng., vol. 15, no. 6, pp. 929–942, Jun. 2012.

31. J. Won, JW. Park, W. Park, H. Yoon, and DS. Moon, “Non-target structural displacement measurement using reference frame based deepflow”, Sensors, vol. 10, no. 13. Jul. 2019.

32. D. Feng and M. Q. Feng, “Vision-based multipoint displacement measurement for structural health monitoring,” Struct. Control Heal. Monit., vol. 23, no. 5, pp. 876–890, May 2016.

33. H. Yoon, J. Shin, and B. F. Spencer, “Structural Displacement Measurement Using an Unmanned Aerial System,” Comput. Civ. Infrastruct. Eng., vol. 33, no. 3, pp. 183–192, Mar. 2018.

34. J.-W. Park, S.-H. Sim, H.-J. Jung, and B. Jr., “Development of a Wireless Displacement Measurement System Using Acceleration Responses,” Sensors, vol. 13, no. 7, pp. 8377–8392, Jul.

2013.

35. S. Cho, J.-W. Park, R. P. Palanisamy, and S.-H. Sim, “Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion,” J. Sensors, vol. 2016, pp. 1–9, 2016.

36. A. Smyth and M. Wu, “Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring,” Mech. Syst. Signal Process., vol. 21, no. 2, pp. 706–723, Feb. 2007.

37. S. H. Sim, B. F. Spencer, and T. Nagayama, “Multimetric Sensing for Structural Damage Detection,”

J. Eng. Mech., vol. 137, no. 1, pp. 22–30, Jan. 2011.

38. S. Cho, J. Lee, and S. H. Sim, “Comparative study on displacement measurement sensors for high- speed railroad bridge,” in Smart Structures and Systems, 2018, vol. 21, no. 5, pp. 637–652.

39. K. Kim, J. Choi, G. Koo, and H. Sohn, “Dynamic displacement estimation by fusing biased high- sampling rate acceleration and low-sampling rate displacement measurements using two-stage Kalman estimator,” Smart Struct. Syst., vol. 17, no. 4, pp. 647–667, Apr. 2016.

40. J. A. Rice, C. Li, C. Gu, and J. C. Hernandez, “A wireless multifunctional radar-based displacement sensor for structural health monitoring,” in Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 2011, p. 79810K.

71

41. G. Koo et al., “Development of a High Precision Displacement Measurement System by Fusing a Low Cost RTK-GPS Sensor and a Force Feedback Accelerometer for Infrastructure Monitoring,”

Sensors, vol. 17, no. 12, p. 2745, Nov. 2017.

42. J. Guo and C. Zhu, “Dynamic displacement measurement of large-scale structures based on the Lucas–Kanade template tracking algorithm,” Mech. Syst. Signal Process., vol. 66–67, pp. 425–436, Jan. 2016.

43. D. F. Truong-Hong, Linh; Laefer, “Using terrestrial laser scanning for dynamic bridge deflection measurement,” IABSE Istanbul Bridg. Conf., vol. 59, no. 5, pp. 477–482, 2009.

44. H. S. Park, H. M. Lee, H. Adeli, and I. Lee, “A New Approach for Health Monitoring of Structures:

Terrestrial Laser Scanning,” Comput. Civ. Infrastruct. Eng., vol. 22, no. 1, pp. 19–30, Jan. 2007.

45. K. Kim and J. Kim, “Dynamic displacement measurement of a vibratory object using a terrestrial laser scanner,” Meas. Sci. Technol., vol. 26, no. 4, p. 045002, Apr. 2015.

46. H. M. Lee and H. S. Park, “Estimation of deformed shapes of beam structures using 3D coordinate information from terrestrial laser scanning,” C. - Comput. Model. Eng. Sci., vol. 29, no. 1, pp. 29–

44, 2008.

47. H. Yang, X. Xu, and I. Neumann, “Deformation behavior analysis of composite structures under monotonic loads based on terrestrial laser scanning technology,” Compos. Struct., vol. 183, pp.

594–599, Jan. 2018.

48. W. Mukupa, G. W. Roberts, C. M. Hancock, and K. Al-Manasir, “A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures,” Surv.

Rev., pp. 1–18, Apr. 2016.

49. D. Feng and M. Q. Feng, “Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review,” Eng. Struct., vol. 156, pp. 105–117, Feb. 2018.

50. C. Z. Dong and F. N. Catbas, “A non-target structural displacement measurement method using advanced feature matching strategy,” Adv. Struct. Eng., 2019.

51. J. M. W. Brownjohn, Y. Xu, and D. Hester, “Vision-based bridge deformation monitoring,” Front.

Built Environ., 2017.

52. F. Ullah and S. Kaneko, “Using orientation codes for rotation-invariant template matching,”

Pattern Recognit., vol. 37, no. 2, pp. 201–209, Feb. 2004.

53. Y. LI, H. TAKAUJI, I. OHMURA, S. KANEKO, and T. TANAKA, “Robust Focusing using Orientation Code Matching,” J. Japan Soc. Precis. Eng., vol. 75, no. 5, pp. 650–656, 2009.

54. P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, “RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments,” Int. J. Rob. Res., vol. 31, no. 5, pp. 647–

72 663, Apr. 2012.

55. F. Endres, J. Hess, J. Sturm, D. Cremers, and W. Burgard, “3-D Mapping With an RGB-D Camera,”

IEEE Trans. Robot., vol. 30, no. 1, pp. 177–187, Feb. 2014.

56. A. Canessa, M. Chessa, A. Gibaldi, S. P. Sabatini, and F. Solari, “Calibrated depth and color cameras for accurate 3D interaction in a stereoscopic augmented reality environment,” J. Vis.

Commun. Image Represent., vol. 25, no. 1, pp. 227–237, Jan. 2014.

57. A. Grano and R. Zinno, “A new low-cost displacements monitoring system based on Kinect sensor,”

J. Civ. Struct. Heal. Monit., vol. 5, no. 5, pp. 727–733, Nov. 2015.

58. Y. L. Chen, M. Abdelbarr, M. R. Jahanshahi, and S. F. Masri, “Color and depth data fusion using an RGB-D sensor for inexpensive and contactless dynamic displacement-field measurement,”

Struct. Control Heal. Monit., vol. 24, no. 11, p. e2000, Nov. 2017.

59. M. Abdelbarr, Y. L. Chen, M. R. Jahanshahi, S. F. Masri, W.-M. Shen, and U. A. Qidwai, “3D dynamic displacement-field measurement for structural health monitoring using inexpensive RGB- D based sensor,” Smart Mater. Struct., vol. 26, no. 12, p. 125016, Dec. 2017.

60. N. Ramakrishnan, T. Srikanthan, S. K. Lam, and G. R. Tulsulkar, “Adaptive Window Strategy for High-Speed and Robust KLT Feature Tracker,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 355–

367, 2016.

61. J.-C. Piao and S.-D. Kim, “Adaptive Monocular Visual–Inertial SLAM for Real-Time Augmented Reality Applications in Mobile Devices,” Sensors, vol. 17, no. 11, p. 2567, Nov. 2017.

62. S. Tanathong and I. Lee, “The improvement of KLT for real-time feature tracking from UAV image sequence,” in 30th Asian Conference on Remote Sensing 2009, ACRS 2009, 2009, vol. 2, pp. 748–

753.

63. W. Jang, S. Oh, and G. Kim, “A hardware implementation of pyramidal KLT feature tracker for driving assistance systems,” in 2009 12th International IEEE Conference on Intelligent Transportation Systems, 2009, pp. 1–6.

64. M. A. Kuddus, J. Li, H. Hao, C. Li, and K. Bi, “Target-free vision-based technique for vibration measurements of structures subjected to out-of-plane movements,” Eng. Struct., vol. 190, pp. 210–

222, Jul. 2019.

65. D. Lydon et al., “Development and Field Testing of a Time-Synchronized System for Multi-Point Displacement Calculation Using Low-Cost Wireless Vision-Based Sensors,” IEEE Sens. J., vol. 18, no. 23, pp. 9744–9754, Dec. 2018.

66. J. Morlier and G. Michon, “Virtual vibration measurement using KLT motion tracking algorithm,”

73

J. Dyn. Syst. Meas. Control. Trans. ASME, vol. 132, no. 1, pp. 1–8, Jan. 2010.

67. R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision,” 2nd ed., Cambridge, United Kingdom: Cambridge University Press; 2003. [Online]. Available:

https://www.cambridge.org/core/books/multiple-view-geometry-in-computer- vision/0B6F289C78B2B23F596CAA76D3D43F7A

68. A. P. Badali, Y. Zhang, P. Carr, P. J. Thomas, and R. I. Hornsey, “Scale factor in digital cameras,”

in Photonic Applications in Biosensing and Imaging, 2005, p. 59692B.

69. Y. Fukuda, M. Q. Feng, and M. Shinozuka, “Cost-effective vision-based system for monitoring dynamic response of civil engineering structures,” Struct. Control Heal. Monit., 2010.

70. Y. Xu and J. M. W. Brownjohn, “Review of machine-vision based methodologies for displacement measurement in civil structures,” J. Civ. Struct. Heal. Monit., vol. 8, no. 1, pp. 91–110, Jan. 2018.

71. Dubrofsky, E. (2009). Homography estimation. Diplomová práce. Vancouver: Univerzita Britské Kolumbie.

72. H. Jeon, Y. Kim, D. Lee, and H. Myung, “Vision-based remote 6-DOF structural displacement monitoring system using a unique marker,” Smart Struct. Syst., vol. 13, no. 6, pp. 927–942, Jun.

2014.

73. S. Lee, J. Lee, J.-W. Park, and S.-H. Sim, “Nontarget-based Measurement of 6-DOF Structural Displacement using Combined RGB Color and Depth Information,” IEEE/ASME Trans.

Mechatronics, pp. 1–1, 2020.

74. P. Fankhauser, M. Bloesch, D. Rodriguez, R. Kaestner, M. Hutter, and R. Siegwart, “Kinect v2 for mobile robot navigation: Evaluation and modeling,” in 2015 International Conference on Advanced Robotics (ICAR), 2015, pp. 388–394.

75. O. Wasenmüller and D. Stricker, “Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, pp. 34–45.

76. H. Sarbolandi, D. Lefloch, and A. Kolb, “Kinect range sensing: Structured-light versus Time-of- Flight Kinect,” Comput. Vis. Image Underst., vol. 139, pp. 1–20, Oct. 2015.

77. X. Qi, D. Lichti, M. El-Badry, J. Chow, and K. Ang, “Vertical Dynamic Deflection Measurement in Concrete Beams with the Microsoft Kinect,” Sensors, vol. 14, no. 2, pp. 3293–3307, Feb. 2014.

78. Microsoft Official Website (2014, Sep. 21). Kinect for Windows SDK 2.0. [Online]. Available:

https://www.microsoft.com/en-us/download/details.aspx?id=44561. Published September 21, 2014. Accessed on: Dec. 15, 2016.

79. A. I. Mees, P. E. Rapp, and L. S. Jennings, “Singular-value decomposition and embedding

74

dimension,” Phys. Rev. A, vol. 36, no. 1, pp. 340–346, Jul. 1987.

80. B. D. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision.,” in Proceedings DARPA Image Understanding Workshop, 1981, pp. 121-130.

81. D. W. Eggert, A. Lorusso, and R. B. Fisher, “Estimating 3-D rigid body transformations: a comparison of four major algorithms,” Mach. Vis. Appl., vol. 9, no. 5–6, pp. 272–290, Mar. 1997.

82. Z. Zhang, “A flexible new technique for camera calibration,” IEEE Trans. Pattern Anal. Mach.

Intell., vol. 22, no. 11, pp. 1330–1334, 2000.

83. Z. Kalal, K. Mikolajczyk, and J. Matas, “Forward-Backward Error: Automatic Detection of Tracking Failures,” in 2010 20th International Conference on Pattern Recognition, 2010, pp.

2756–2759.

84. Leica BLK360 User Manual. [Online]. Available: https://shop.leica- geosystems.com/sites/default/files/2019-04/853811_leica_blk360_um_v2.0.0_en.pdf. Accessed on: Oct. 20, 2020.

85. J. Choi, “Range Sensors: Ultrasonic Sensors, Kinect, and LiDAR,” in Humanoid Robotics: A Reference, Dordrecht: Springer Netherlands, 2017, pp. 1–19.

86. H. Kim, S. Lee, E. Ahn, M. Shin, and S. H. Sim, “Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion,” Struct. Heal.

Monit., 2020.

87. D. Antón, P. Pineda, B. Medjdoub, and A. Iranzo, “As-Built 3D Heritage City Modelling to Support Numerical Structural Analysis: Application to the Assessment of an Archaeological Remain,”

Remote Sens., vol. 11, no. 11, p. 1276, May 2019.

88. J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., 1986.

89. R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,”

Commun. ACM, vol. 15, no. 1, pp. 11–15, Jan. 1972.

90. C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” in Procedings of the Alvey Vision Conference 1988, 1988, pp. 23.1-23.6.

91. D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.

92. M. Muja and D. G. Lowe, “Fast approximate nearest neighbors with automatic algorithm configuration,” in VISAPP 2009 - Proceedings of the 4th International Conference on Computer Vision Theory and Applications, 2009.

93. P. H. S. Torr and A. Zisserman, “MLESAC: A New Robust Estimator with Application to

Dokumen terkait