Vision-based inspection of bolted joints: Field evaluation on a historical truss bridge in Vietnam
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https://doi.org/10.15625/0866-7136/15073Keywords:
bridge monitoring, vision-based inspection, bolted joint, loosened bolt detection, structural health monitoring, damage detectionAbstract
Vision-based inspection has received significant interests from structural health monitoring and maintenance academia. The vision-based approach has unique advantages over the traditional sensor-based inspection, including non-contact sensing, low cost, simple setup, and being immune to environmental effects. Despite that, the translation of the vision-based inspection to in-service structures in Viet Nam has been limited so far. Herein, the authors examine the field applicability of a vision-based approach for joints monitoring of a historical truss bridge in Vietnam. Firstly, a well-established vision-based bolt-loosening monitoring approach is briefly described. Secondly, a field test on the Nam O bridge (Da Nang City) is performed. A digital camera is used to capture the images of representative bolted joints of the bridge. Lastly, the vision-based approach is applied to monitor the bolted joints. The angle of bolts in the joints is estimated from the captured images, from which the accuracy of the approach is evaluated. This study is one of the first case applications, demonstrating the field applicability of the vision-based bolt-loosening approach for inspecting a real bridge in Vietnam.
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T. Wang, G. Song, S. Liu, Y. Li, and H. Xiao. Review of bolted connection monitoring. International Journal of Distributed Sensor Networks, 9, (12), (2013). https://doi.org/10.1155/2013/871213.
T.-C. Nguyen, T.-C. Huynh, J.-H. Yi, and J.-T. Kim. Hybrid bolt-loosening detection in wind turbine tower structures by vibration and impedance responses. Wind and Structures, 24, (4), (2017), pp. 385–403. https://doi.org/10.12989/was.2017.24.4.385.
S. M. Y. Nikravesh and M. Goudarzi. A review paper on looseness detection methods in bolted structures. Latin American Journal of Solids and Structures, 14, (12), (2017), pp. 2153–2176. https://doi.org/10.1590/1679-78254231.
M. Zhang, Y. Shen, L. Xiao, and W. Qu. Application of subharmonic resonance for the detection of bolted joint looseness. Nonlinear Dynamics, 88, (3), (2017), pp. 1643–1653. https://doi.org/10.1007/s11071-017-3336-1.
T.-C. Huynh, N.-L. Dang, and J.-T. Kim. Preload monitoring in bolted connection using piezoelectric-based smart interface. Sensors, 18, (9), (2018). https://doi.org/10.3390/s18092766.
S. G. Joshi and R. G. Pathare. Ultrasonic instrument for measuring bolt stress. Ultrasonics, 22, (6), (1984), pp. 261–269. https://doi.org/10.1016/0041-624x(84)90043-x.
M. Hirao, H. Ogi, and H. Yasui. Contactless measurement of bolt axial stress using a shear-wave electromagnetic acoustic transducer. Ndt & E International, 34, (3), (2001), pp. 179–183. https://doi.org/10.1016/s0963-8695(00)00055-4.
T.-C. Huynh, D.-D. Ho, N.-L. Dang, and J.-T. Kim. Sensitivity of piezoelectric-based smart interfaces to structural damage in bolted connections. Sensors, 19, (17), (2019). https://doi.org/10.3390/s19173670.
L. Huo, F.Wang, H. Li, and G. Song. A fractal contact theory based model for bolted connection looseness monitoring using piezoceramic transducers. Smart Materials and Structures, 26, (10), (2017). https://doi.org/10.1088/1361-665x/aa6e93.
T.-C. Huynh. Structural parameter identification of a bolted connection embedded with a piezoelectric interface. Vietnam Journal of Mechanics, 42, (2), (2020), pp. 173–188. https://doi.org/10.15625/0866-7136/14806.
Y.-J. Cha, K. You, and W. Choi. Vision-based detection of loosened bolts using the Hough transform and support vector machines. Automation in Construction, 71, (2016), pp. 181–188. https://doi.org/10.1016/j.autcon.2016.06.008.
D. Feng and M. Q. Feng. Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection–A review. Engineering Structures, 156, (2018), pp. 105–117. https://doi.org/10.1016/j.engstruct.2017.11.018.
C. M. Yeum and S. J. Dyke. Vision-based automated crack detection for bridge inspection. Computer-Aided Civil and Infrastructure Engineering, 30, (10), (2015), pp. 759–770. https://doi.org/10.1111/mice.12141.
J.-H. Park, T.-C. Huynh, S.-H. Choi, and J.-T. Kim. Vision-based technique for bolt-loosening detection in wind turbine tower. Wind and Structures, 21, (6), (2015), pp. 709–726. https://doi.org/10.12989/was.2015.21.6.709.
T.-C. Nguyen, T.-C. Huynh, J.-Y. Ryu, J.-H. Park, and J.-T. Kim. Bolt-loosening identification of bolt connections by vision image-based technique. In Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure, (2016). https://doi.org/10.1117/12.2219055.
L. Ramana, W. Choi, and Y.-J. Cha. Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm. Structural Health Monitoring, 18, (2), (2019), pp. 422–434. https://doi.org/10.1177/1475921718757459.
X. Kong and J. Li. Image registration-based bolt loosening detection of steel joints. Sensors, 18, (4), (2018). https://doi.org/10.3390/s18041000.
X. Zhao, Y. Zhang, and N.Wang. Bolt loosening angle detection technology using deep learning. Structural Control and Health Monitoring, 26, (1), (2019). https://doi.org/10.1002/stc.2292.
C.Wang, N.Wang, S.-C. Ho, X. Chen, and G. Song. Design of a new vision-based method for the bolts looseness detection in flange connections. IEEE Transactions on Industrial Electronics, 67, (2), (2019), pp. 1366–1375. https://doi.org/10.1109/tie.2019.2899555.
T.-C. Huynh, J.-H. Park, H.-J. Jung, and J.-T. Kim. Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing. Automation in Construction, 105, (2019). https://doi.org/10.1016/j.autcon.2019.102844.
S.-Y. Lee, T.-C. Huynh, J.-H. Park, and J.-T. Kim. Bolt-loosening detection using vision-based deep learning algorithm and image processing method. Journal of the Computational Structural Engineering Institute of Korea, 32, (4), (2019), pp. 265–272. https://doi.org/10.7734/coseik.2019.32.4.265.
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2014), pp. 580–587. https://doi.org/10.1109/CVPR.2014.81.
J. R. R. Uijlings, K. E. A. Van De Sande, T. Gevers, and A. W. M. Smeulders. Selective search for object recognition. International Journal of Computer Vision, 104, (2), (2013), pp. 154–171. https://doi.org/10.1007/s11263-013-0620-5.
S.-J. Yang, C. C. Ho, J.-Y. Chen, and C.-Y. Chang. Practical homography-based perspective correction method for license plate recognition. In 2012 International Conference on Information Security and Intelligent Control, IEEE, IEEE, (2012), pp. 198–201. https://doi.org/10.1109/isic.2012.6449740.
J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6), (1986), pp. 679–698. https://doi.org/10.1016/b978-0-08-051581-6.50024-6.
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