Accurately and rapidly estimating bridge flexibility is crucial for structural health diagnosis and performance evaluation. Traditional impact testing methods for flexibility identification use numerous sensors placed on the bridge deck, these methods suffer from low efficiency. To address this issue, a substructure mobile impact testing strategy using noncontact vision-based structural displacement measurements is proposed to estimate bridge flexibility. The advantages of this method include (1) impact testing is performed using only a movable single camera and an impact hammer, improving testing efficiency; (2) the use of multipoint vision-based noncontact measurements of dynamic displacements based on the adaptive kernel correlation filters (AKCF), reduces testing costs; and (3) overcoming field-of-view limitations, the flexibility matrix of the entire bridge is obtained through a reference-free mobile impact testing method by integrating the substructure results. Finally, the effectiveness of this method is validated through laboratory experiments with a simply supported beam model.
HeZGLiWSalehiH, et al. Integrated structural health monitoring in bridge engineering. Autom Constr2022; 136: 104168.
2.
GattulliVChiaramonteL.Condition assessment by visual inspection for a bridge management system. Comput Aided Civ Inf2005; 20(2): 95–107.
3.
SunLShangZXiaY, et al. Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection. J Struct Eng2020; 146(5): 04020073.
4.
YuJYMengXYanB, et al. Global Navigation Satellite System-based positioning technology for structural health monitoring: a review. Struct Control Health Monit2020; 27(1): e2467.
5.
SomanRKyriakidesMOnoufriouT, et al. Numerical evaluation of multi-metric data fusion based structural health monitoring of long span bridge structures. Struct Infrastruct Eng2018; 14(6): 673–684.
6.
ConteJPHeXMoaveniB, et al. Dynamic testing of Alfred Zampa memorial bridge. J Struct Eng2008; 134(6): 1006–1015.
7.
GulMCatbasFN.Ambient vibration data analysis for structural identification and global condition assessment. J Eng Mech2008; 134(8): 650–662.
8.
SiringoringoDMFujinoY.System identification of suspension bridge from ambient vibration response. Eng Struct2008; 30(2): 462–477.
9.
BayraktarAAltunişikACSevimB, et al. Environmental effects on the dynamic characteristics of the Gülburnu Highway Bridge. Civ Eng Environ Syst2014; 31(4): 347–366.
10.
AktanAEFarheyDNHelmickiAJ, et al. Structural identification for condition assessment: experimental arts. J Struct Eng1997; 123(12): 1674–1684.
11.
LenettMTurerABrownD.Condition assessment of steel stringer highway bridges using experimental modal analysis. J Acoust Soc Am1999; 106(4_Supplement): 2215–2215.
12.
CatbasFNBrownDLAktanAE.Use of modal flexibility for damage detection and condition assessment: case studies and demonstrations on large structures. J Struct Eng2006; 132(11): 1699–1712.
13.
ZhangJMoonFL.A new impact testing method for efficient structural flexibility identification. Smart Mater Struct2012; 21(5): 055016.
14.
YeSLaiX, et al. Technology for condition and performance evaluation of highway bridges. J Civ Struct Health Monit2020; 10: 573–594.
15.
SinghMPElbadawyMZBishtSS.Dynamic strain response measurement-based damage identification in structural frames. Struct Control Health Monit2018; 25(7): e2181.
16.
CatbasFNBrownDLAktanAE.Parameter estimation for multiple-input multiple-output modal analysis of large structures. J Eng Mech2004; 130(8): 921–930.
17.
ZhaoWZhangCZhangJ.Continuous measurement of tire deformation using long-gauge strain sensors. Mech Syst Signal Process2020; 142: 106782.
18.
ZhouLMZhangJ.Continuous force excited bridge dynamic test and structural flexibility identification theory. Struct Eng Mech2019; 71(4): 391–405.
19.
ParlooEVerbovenPGuillaumeP, et al. Sensitivity-based operational mode shape normalisation. Mech Syst Signal Process2002; 16(5): 757–767.
20.
ZhangQHouJJankowskiŁ.Bridge damage identification using vehicle bump based on additional virtual masses. Sensors2020; 20(2): 394.
21.
López-AenlleMBrinckerRPelayoF, et al. On exact and approximated formulations for scaling-mode shapes in operational modal analysis by mass and stiffness change. J Sound Vib2012; 331(3): 622–637.
22.
ZhangJZhangQQGuoSL, et al. Structural identification of short/middle span bridges by rapid impact testing: theory and verification. Smart Mater Struct2015; 24(6): 065020.
23.
ZhangJGuoSLZhangQQ.Mobile impact testing for structural flexibility identification with only a single reference. Comput Aided Civ Inf2015; 30(9): 703–714.
24.
GuoSLZhangXZhangJ, et al. Mobile impact testing of a simply-supported steel stringer bridge with reference-free measurement. Eng Struct2018; 159: 66–74.
25.
XuYBrownjohnJMWHesterD, et al. Long-span bridges: enhanced data fusion of GPS displacement and deck accelerations. Eng Struct2017; 147: 639–651.
26.
PsimoulisPAStirosSC.Measuring deflections of a short-span railway bridge using a robotic total station. J Bridge Eng2013; 18(2): 182–185.
27.
LeeJLeeKCLeeS, et al. Long-term displacement measurement of bridges using a LiDAR system. Struct Control Health Monit2019; 26(10): e2428.
28.
ZhouLMJiangYQJiaHW, et al. UAV vision-based crack quantification and visualization of bridges: system design and engineering application. Struct Health Monit2024; 24: 1083–1100.
29.
GoricanecJEreizSOrsagM, et al. Identification of the dynamic parameters of bridge elements using unmanned aerial vehicle. J Sound Vib2023; 566: 117901.
LiHZhuBLChenZ, et al. Realtime in-plane displacements tracking of the precision positioning stage based on computer micro-vision. Mech Syst Signal Process2019; 124: 111–123.
32.
ShaoYLiLLiJ, et al. Computer vision based target-free 3D vibration displacement measurement of structures. Eng Struct2021; 246: 113040.
33.
LeeJJShinozukaM.Real-time displacement measurement of a flexible bridge using digital image processing techniques. Exp Mech2006; 46(1): 105–114.
34.
NgeljaratanLMohamedAM.Structural health monitoring and seismic response assessment of bridge structures using target-tracking digital image correlation. Eng Struct2020; 213: 110551.
35.
HosseiniAMostofinejadDHajialilue-BonabM.Displacement and strain field measurement in steel and RC beams using particle image velocimetry. J Eng Mech2014; 140: 04014086.
36.
TianYDZhangJYuSS.Rapid impact testing and system identification of footbridges using particle image velocimetry. Comput Aided Civ Inf2019; 34(2): 130–145.
37.
DavidGL.Distinctive image features from scale-invariant keypoints. Int J Comput Vis2004; 60(2): 91–110.
38.
BayHEssATuytelaarsT, et al. Speeded-up robust features (surf): similarity matching in computer vision and multimedia. Comput Vis Image Und2008; 110(3): 346–359.
39.
BakerSMatthewsI.Lucas-Kanade 20 years on: a unifying framework. Int J Comput Vis2004; 56(3): 221–255.
40.
GuoJZhuCA.Dynamic displacement measurement of large-scale structures based on the Lucas-Kanade template tracking algorithm. Mech Syst Signal Process2016; 66–67: 425–436.
TianYDZhangJYuSS.Vision-based structural scaling factor and flexibility identification through mobile impact testing. Mech Syst Signal Process2019; 122: 387–402.
43.
FengDFengMQ.Experimental validation of cost-effective vision-based structural health monitoring. Mech Syst Signal Process2017; 88: 199–211.
44.
HanYTWuGFengDM.Structural modal identification using a portable laser-and-camera measurement system. Measurement2023; 214: 112768.
45.
LuoKKongXWangX.Cable vibration measurement based on broad-band phase-based motion magnification and line tracking algorithm. Mech Syst Signal Process2023; 200: 110575.
46.
DongCZBasSCatbasFN.Investigation of vibration serviceability of a footbridge using computer vision-based methods. Eng Struct2020; 224: 111224.
47.
LiPJYanSHZhangJ.A quantitative comparison study for structural flexibility identification using accelerometric and computer vision-based vibration data. J Sound Vib2024; 576: 118288.
48.
BolmeDSBeveridgeJRDraperBA, et al. Visual object tracking using adaptive correlation filters. In: The twenty-third IEEE conference on computer vision and pattern recognition (CVPR2010), San Francisco, CA, 13–18 June 2010, pp. 2544–2550. San Francisco, CA: IEEE.
49.
HenriquesJFCaseiroRMartinsP, et al. Exploiting the circulant structure of tracking-by-detection with kernels. In: 12th European conference on computer vision (ECCV 2012), Florence, Italy, 7–13 October 2012, pp. 702–715. Berlin; Heidelberg: Springer.
50.
KalalZMikolajczykKMatasJ.Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell2012; 34(7): 1409–1422.
HenriquesJFCaseiroRMartinsP, et al. High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell2014; 37(3): 583–596.
53.
SunSGLeeEH.Small target tracking using KCF and adaptive thresholding. In: 14th International conference on quality control by artificial vision (QCAV2019), Mulhouse, France, 15–17 May 2019, pp. 350–357. Mulhouse: SPIE.
54.
YanPMYaoSBZhuQY, et al. Real-time detection and tracking of infrared small targets based on grid fast density peaks searching and improved KCF. Infrared Phys Techn2022; 123: 104181.
55.
CaiWXieWHeTT.KCF-based identification approach for vibration displacement of double-column bents under various earthquakes. Struct Control Health Monit2023; 2023(1): 1–22.
