Abstract
The prestressed cables used for external strengthening and the suspender cables used for arch bridges may suffer damage resulting from the corrosion or fracture of steel wires. Under these scenarios, the effective areas of the cables will decrease, but the cable forces will remain almost constant, which limits the ability to detect this damage with traditional frequency domain analysis. Due to the lack of understanding of the time and frequency domain characteristics, damage indexes and damage quantification for this kind of cable, hidden cable damage may not be detected in time, which can threaten bridge safety. To solve these problems, a series of performance experiments for prestressed cables were designed. The dynamic response signals of these cables to various damage levels, cable forces and cable lengths were obtained and analysed in the time domain, frequency domain and energy domain. Depending on the test, the change rate of wavelet packet total energy (RWE) was determined to be sensitive to cable damage and was chosen as the damage index for the cables. The damage level was quantified by a neural network algorithm with RWE, and a prediction procedure for cable damage was finally established. The damage detection method for external cables proposed in this paper will aid in the damage assessment and long-term monitoring of cable-supported bridges.
Similar content being viewed by others
References
Wickramasinghe WR, Thambiratnam D, Chan T (2014) Damage detection in cable structures using vibration characteristics. In: Proceedings of the 4th International Conference on Structural Engineering and Construction Management, 2014, Nethwin Printers Ltd., Sri Lanka.
Xu F, Wang X (2012) Inspection method of cable-stayed bridge using magnetic flux leakage detection: principle, sensor design, and signal processing. J Mech Sci Technol 26(3):661–669
Xu J, Wu XJ (2011) Nondestructive testing of bridge cables Using magnetostrictive guided wave technique. Appl Mech Mater 134:2015–2018
Ho HN, Kim KD, Park YS, Lee JJ (2013) An efficient image-based damage detection for cable surface in cable-stayed bridges. NDT&E Int 58:18–23
Li X, Gao C, Guo Y, Shao Y, He F (2019) Cable surface damage detection in cable-stayed bridges using optical techniques and image mosaicking. Opt Laser Technol 110:36–43
Ni Y, Zhang Q, Xin R (2021) IMagnetic flux detection and identification of bridge cable metal area loss damage. Measurement 167:108443
Mehrabi AB (2006) In-service evaluation of cable-stayed bridges, overview of available methods and findings. J Bridge Eng 11(6):716–724
Xu F, Wang X, Wang L (2011) Cable inspection robot for cable-stayed bridges: design, analysis, and application. J Field Robot 28(3):441–459
Guo YL (2014) Cable corrosion analysis and damage monitoring. Appl Mech Mater 579:1302–1305
Shull PJ, Li D, Ou J, Wu HF, Diaz AA, Vogel DW (2008) Acoustic emission monitoring and critical failure identification of bridge cable damage. SPIE Proc 6934:69340J
Nazarian E, Ansari F, Zhang X, Taylor T (2016) Detection of tension loss in cables of cable-stayed bridges by distributed monitoring of bridge deck strains. J Struct Eng 142(6):e04016018
Huang Y, Yang W, Fu J, Liu A, Wei G (2018) Measurement of the real-time deflection of cable-stayed bridge based on cable tension variations. Measurement 119:S0263224118300836
Hua X, Ni Y, Chen ZQ, Ko JM (2009) Structural damage detection of cable-stayed bridges using changes in cable forces and model updating. J Struct Eng 135(9):1093–1106
Sanayei M, Saletnik MJ (1996) Parameter estimation of structures from static strain measurements. I: formulation. J Struct Eng 122(5):555–562
Wu X, Ghaboussi J, Garrett JH (1992) Use of neural networks in detection of structural damage. Comput Struct 42(11):578–581
Xiang Y, Jia Y (2017) Damage detection of hangers in arch bridges based on relative variation of wavelet total energy. J Zhejiang Univ (Eng Sci) 51(5):870–878 (in Chinese)
Lepidi M, Gattulli V (2009) Damage identification in elastic suspended cables through frequency measurement. J Vib Control 15(6):867–896
Tan D, Qu W, Zhang J, Liu J (2012) Damage identification of cable of long span cable-stayed bridge. J Wuhan Univ Technol 34(7):107–110 (in Chinese)
Hui L, Zhang F, Jin Y (2014) Real-time identification of time-varying tension in stay cables by monitoring cable transversal acceleration. Struct Control Health Monit 21(7):1100–1117
Lin S, Yi T, Li H, Ren L (2017) Damage detection in the cable structures of a bridge using the virtual distortion method. J Bridge Eng 22(8):04017039
Zhang S, Shen R, Dai K, Wang L, De Roeck G, Geert L (2019) A Methodology for cable damage identification based on wave decomposition. J Sound Vib 442:527–551
Liu C, Liu J, Sun X (2009) Study on the Damage Identification of Long-Span Arch Bridge Based on Variation Ratio of Curvature and RBF Neural Network. In: First IEEE International Conference on Information Science Engineering.
Xie X, Yan X (2008) Cable tension prediction of Hongfeng Lake cable-stayed bridge using BP neural network. In: 2nd International Conference on Anti-counterfeiting, Security and Identification.
Zhu J, Xiao R (2007) Damage detection of a large-span concrete cable-stayed bridge based on genetic algorithm. Front Arch Civil Eng China 1(2):170–175
Tan DM, Qu WL, Zhang JB, Liu J (2012) Damage diagnosis of cable of large span cable-stayed bridge based on the support vector machine. Appl Mech Mater 191:958–961
Arangio S, Bontempi F (2015) Structural health monitoring of a cable-stayed bridge with Bayesian neural networks. Struct Infrastruct E 11(4):575–587
Huang J, Li D, Li H, Song G, Liang Y (2018) Damage identification of a large cable-stayed bridge with novel cointegrated Kalman filter method under changing environments. Struct Cont Health Monit 25(5):e2152
Duan Y, Chen Q, Zhang H, Yun CB, Wu S, Zhu Z (2019) CNN-based damage identification method of tied-arch bridge using spatial-spectral information. Smart Struct Syst 23(5):507–220
Yi T, Li H, Sun H (2013) Multi-stage structural damage diagnosis method based on “energy-damage” theory. Smart Struct Syst 12(3–4):345–361
Manimaran P, Panigrahi PK, Parikh JC (2005) Wavelet analysis and scaling properties of time series. Phys Rev E 72(4):046120
Ding Y (2005) Alarming theory, method and application for structure damage of long-span cable-stayed bridges. Southeast University, Nanjing (in Chinese)
Liu T, Li A, Ding Y (2007) Alarming method for cable damage of long-span cable-stayed bridges based on wavelet packet energy spectrum. J Southeast Univ (Natural Science Edition) 37(2):270–274 (in Chinese)
Vallabhaneni V, Maity D (2011) Application of radial basis neural network on damage assessment of structures. Procedia Eng 14:3104–3110
Li Y, Li P, Zhang N, Li G, Li Y (2009) Safety assessment of thermal power enterprise based on BP neural network. J Inf Comput Sci 6(1):553–560
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (No. 41404008), Science and Technology Project of Xiamen Construction Bureau (No.XJK2020-1-7), Science and Technology Research and Development Project of Fujian Provincial Housing and Construction Department (No.2020-K-73).
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
Xia, Z., Lin, Y., Wang, Q. et al. Damage detection method for cables based on the change rate of wavelet packet total energy and a neural network. J Civil Struct Health Monit 11, 593–608 (2021). https://doi.org/10.1007/s13349-021-00471-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13349-021-00471-2