Abstract
As a key indicator of the structural performance of cable-stayed bridges, tensile forces in stay cables are required to be controlled for maintaining the structural integrity of bridges. In this paper, a non-contact vision-based system for cable tension monitoring is proposed. To measure the dynamic response of cables cost-effectively, a feature-based video image processing technique is developed. The Scale Invariant Feature Transform (SIFT) is adopted for the implementation of the feature-based methodology. Since the detected keypoints associated with the cable play a critical role in extracting the displacement time-history, a study on the feasibility of the feature-based detection algorithm is conducted under a variety of test scenarios within laboratory settings. The performance of the keypoint detector for tracking a vibrating cable is quantified based on a set of evaluation parameters. To extend the versatility of the keypoint detector within complex background scenarios, enhancement techniques are investigated as well. The analysis of the performance indicators demonstrates that the detector is capable of extracting sufficient dynamic information of a vibrating cable from a video image sequence. Subsequently, threshold-dependent image matching approaches are proposed, which optimize the functionality of the vision-based system under complex background conditions. The developed feature-based image processing technique is further integrated seamlessly with cable dynamic analysis for cable tension monitoring. Through experimental studies, the proposed non-contact vision-based system is validated for cable frequency identification as well as tensile force estimation.
Similar content being viewed by others
References
Au FT, Si X (2012) Time-dependent effects on dynamic properties of cable-stayed bridges. Struct Eng Mech 41(1):139–155
Chang CC, Ji YF (2008) Nontarget image-based technique for small cable vibration measurement. J Bridge Eng 13(1):34–42
Chen JG, Abe D, Neal W, Frédo D, Freeman WT, Oral B (2017) Video camera-based vibration measurement for civil infrastructure applications. J Infrastruct Syst. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000348
Fang Z, Wang J-Q (2012) Practical formula for cable tension estimation by vibration method. J Bridge Eng 17(1):161–164
Feng D, Feng MQ (2016) Vision-based multipoint displacement measurement for structural health monitoring. Struct Control Health Monit 2016(23):876–890
Feng D, Feng MQ (2017) Experimental validation of cost-effective vision-based structural health monitoring. Mech Syst Signal Process 88(2017):199–211
Feng D, Feng MQ (2018) Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection—a review. Eng Struct 156(2018):105–117
Feng D, Feng MQ, Ozer E, Fukuda Y (2015) A vision-based sensor for noncontact structural displacement measurement. Sens 15:16557–16575
Feng D, Mauch C, Summerville S, Fernandez O (2018) Suspender replacement for a signature bridge. J Brid Eng. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001299
Feng D, Scarangello T, Feng MQ, Ye Q (2016) Cable tension force estimate using novel noncontact vision-based sensor. Measurement 99(2017):44–52
Harris C, Stephens M (1988) A combined corner and edge detector. Manchester, UK, The Plessey Company pic
Irvine HM (1981) Cable structures. The MIT Press, Cambridge, Massachusetts
Ji Y, Chang C (2008) Nontarget image-based technique for small cable vibration measurement. J Bridge Eng 13(1):34–42
Jo B-W, Lee Y-S, Jo JH, Khan RMA (2018) Computer vision-based bridge displacement measurements using rotation-invariant image processing technique. Sustainability. https://doi.org/10.3390/su10061785
Kangas S, Helmicki A, Hunt V, Sexton R, Swanson J (2012) Cable-stayed bridges: case study for ambient vibration-based cable tension estimation. J Bridge Eng 17(6):839–846
Khalil HH, Rahmat ROK, Mahmoud WA (2008) Estimation of noise in gray-scale and colored images using median absolute deviation (MAD). 3rd International Conference on Geometric Modeling and Imaging: Modern Techniques and Applications, GMAI 4568612:92–97
Kim BH, Park T (2007) Estimation of cable tension force using the frequency-based system identification method. J Sound Vib 304:660–676
Kim S-W, Jeon B-G, Cheung J-H, Kim S-D, Park J-B (2017) Stay cable tension estimation using a vision-based monitoring system under various weather conditions. J Civil Struct Health Monit 7(3):343–357
Kim S-W, Jeon B-G, Kim N-S, Park J-C (2013) Vision-based monitoring system for evaluating cable tensile forces on a cable-stayed bridge. Struct Health Monit 12(5–6):440–456
Kim S-W, Kim N-S (2013) Dynamic characteristics of suspension bridge hanger cables using digital image processing. NDT E Int 59:25–33
Kohut P, Holak K, Uhl T, Ortyl Ł, Owerko T, Kuras P, Kocierz R (2013) Monitoring of a civil structure’s state based on noncontact measurements. Struct Health Monit 12(5–6):411–429
Ko J, Ni YQ (2005) Technology developments in structural health monitoring of large-scale bridges. Eng Struct 27(2005):1715–1725
Li H, Ou J (2016) The state of the art in structural health monitoring of cable-stayed bridges. J Civil Struct Health Monit 2016(6):43–67
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Lynch JP, Loh KJ (2006) A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib Digest 38(2):91–128
Mukherjee D, Wu QMJ, Wang G (2015) A comparative experimental study of image feature detectors and descriptors. Mach Vis Appl. https://doi.org/10.1007/s00138-015-0679-9
Nassif HH, Gindy M, Davis J (2005) Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration. NDT E Int 38(2005):213–218
Pham-Gia T, Hung TL (2001) The median and absolute deviation. Math Comput Model 34:921–936
Ren W-X, Liu H-L, Chen G (2008) Determination of cable tensions based on frequency differences. Eng Comput 25(2):172–189
Ren W-X, Peng X-L, Lin Y-Q (2005) Experimental and analytical studies on dynamic characteristics of a large span cable-stayed bridge. Eng Struct 27:535–548
Sabato A, Niezrecki C, Fortino G (2016) Wireless MEMS-based accelerometer sensor boards for structural vibration monitoring: a review. IEEE Sens. https://doi.org/10.1109/JSEN.2016.2630008
Shi J, Tomasi C (1994) Good features to track. IEEE Conference on Computer Vision and Pattern Recognition (CVPR94), Seattle
Shimada T (2000) Estimating method of cable tension from natural frequency of high mode. JSCE 501(1–29):163–171
Welch PD (1967) The use of fast fourier transform for the estimation of power spectra: a method based on time average over short, modified periodograms. IEEE Trans Audio Electroacoust 15(2):70–73
Xu Y, Brownjohn J, Kong D (2018) A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge. Struct Control Health Monit. https://doi.org/10.1002/stc.2155
Xu Y, Brownjohn JM, Hester D, Koo K-Y (2016) Dynamic displacement measurement of a long-span bridge using vision-based system. 8th European Workshop on Structural Health Monitoring, EWSHM 2016:1434–1443
Yoon H et al (2016) Target-free approach for vision-based structural system identification using consumer-grade cameras. Struct Control Health Monit 2016(23):1405–1416
Zhou T, Zhu L (2015) Conditional median absolute deviation. J Stat Comput Simul 85(10):2101–2114
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chu, C., Ghrib, F. & Cheng, S. Cable tension monitoring through feature-based video image processing. J Civil Struct Health Monit 11, 69–84 (2021). https://doi.org/10.1007/s13349-020-00438-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13349-020-00438-9