Skip to main content
SpringerLink
Log in

A novel structural damage detection strategy based on VMD-FastICA and ESSAWOA

  • Original Paper
  • Published:
Journal of Civil Structural Health Monitoring Aims and scope Submit manuscript

Abstract

This paper proposes a novel two-stage structural damage detection strategy based on variational mode decomposition (VMD), fast independent component analysis (FastICA) and enhanced whale optimization algorithm integrated with Salp swarm algorithm (ESSAWOA). In the first stage, VMD and FastICA are utilized to decompose and process the initial response signals of the structure to detect the damage time preliminary. In the second stage, ESSAWOA algorithm is employed to identify the structural parameters (e.g., stiffness, mass or damping ratio) at different periods of time to determine the location and extent of the damage. To investigate the performance of the strategy, the simulation tests in six damage scenarios are carried out on a three-story numerical model. Then, the superiority of the parameter identification method based on ESSAWOA is verified on a seven-story simulation model. Finally, experimental verification on a laboratory seven-story steel frame is conducted to further validate the accuracy of the proposed strategy. The results in both numerical simulations and experimental validation prove that the two-stage strategy can effectively detect the time, location and extent of the damage in the frame structure. Furthermore, it has good applicability and robustness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Yu J, Meng X, Yan B, Xu B, Fan Q, Xie Y (2020) Global navigation satellite system-based positioning technology for structural health monitoring: a review. Struct Control Health Monit 27(1):e2467

    Article  Google Scholar 

  2. Grabowska J, Palacz M, Krawczuk M (2008) Damage identification by wavelet analysis. Mech Syst Signal Process 22(7):1623–1635. https://doi.org/10.1016/j.ymssp.2008.01.003

    Article  Google Scholar 

  3. Xin Y, Hao H, Li J (2019) Time-varying system identification by enhanced empirical wavelet transform based on synchroextracting transform. Eng Struct 196:109313. https://doi.org/10.1016/j.engstruct.2019.109313

    Article  Google Scholar 

  4. Magalhães F, Cunha A, Caetano E (2012) Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection. Mech Syst Signal Process 28:212–228. https://doi.org/10.1016/j.ymssp.2011.06.011

    Article  Google Scholar 

  5. Jiang T, Ren L, Wang J-j, Jia Z-g, Li D-s, Li H-n (2020) Experimental investigation of fiber Bragg grating hoop strain sensor–based method for sudden leakage monitoring of gas pipeline. Struct Health Monit 20(6):3024–3035. https://doi.org/10.1177/1475921720978619

    Article  Google Scholar 

  6. Qu C-X, Yi T-H, Li H-N, Chen B (2018) Closely spaced modes identification through modified frequency domain decomposition. Measurement 128:388–392. https://doi.org/10.1016/j.measurement.2018.07.006

    Article  Google Scholar 

  7. Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ (2021) A review of vibration-based damage detection in civil structures: from traditional methods to machine learning and deep learning applications. Mech Syst Signal Process 147:107077. https://doi.org/10.1016/j.ymssp.2020.107077

    Article  Google Scholar 

  8. Qu C-X, Yi T-H, Zhou Y-Z, Li H-N, Zhang Y-F (2018) Frequency identification of practical bridges through higher-order spectrum. J Aerosp Eng 31(3):04018018. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000840

    Article  Google Scholar 

  9. Qu C-X, Yi T-H, Li H-N (2019) Mode identification by eigensystem realization algorithm through virtual frequency response function. Struct Control Health Monit 26(10):e2429. https://doi.org/10.1002/stc.2429

    Article  Google Scholar 

  10. Tseng KH, Naidu ASK (2002) Non-parametric damage detection and characterization using smart piezoceramic material. Smart Mater Struct 11(3):317

    Article  Google Scholar 

  11. Ding Z, Li J, Hao H, Lu Z-R (2019) Structural damage identification with uncertain modelling error and measurement noise by clustering based tree seeds algorithm. Eng Struct 185:301–314. https://doi.org/10.1016/j.engstruct.2019.01.118

    Article  Google Scholar 

  12. Tran-Ngoc H, Khatir S, De Roeck G, Bui-Tien T, Abdel Wahab M (2019) An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. Eng Struct 199:109637. https://doi.org/10.1016/j.engstruct.2019.109637

    Article  Google Scholar 

  13. Zenzen R, Belaidi I, Khatir S, Abdel Wahab M (2018) A damage identification technique for beam-like and truss structures based on FRF and Bat algorithm. Comptes Rendus Méc 346(12):1253–1266. https://doi.org/10.1016/j.crme.2018.09.003

    Article  Google Scholar 

  14. Gerist S, Maheri MR (2019) Structural damage detection using imperialist competitive algorithm and damage function. Appl Soft Comput 77:1–23. https://doi.org/10.1016/j.asoc.2018.12.032

    Article  Google Scholar 

  15. Tiachacht S, Bouazzouni A, Khatir S, Abdel Wahab M, Behtani A, Capozucca R (2018) Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm. Eng Struct 177:421–430. https://doi.org/10.1016/j.engstruct.2018.09.070

    Article  Google Scholar 

  16. Kim N-I, Kim S, Lee J (2019) Vibration-based damage detection of planar and space trusses using differential evolution algorithm. Appl Acoust 148:308–321. https://doi.org/10.1016/j.apacoust.2018.08.032

    Article  Google Scholar 

  17. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544. https://doi.org/10.1109/TSP.2013.2288675

    Article  MATH  Google Scholar 

  18. Hyvarinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10(3):626–634. https://doi.org/10.1109/72.761722

    Article  Google Scholar 

  19. Mohanty S, Gupta KK, Raju KS (2018) Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD. Measurement 117:200–220. https://doi.org/10.1016/j.measurement.2017.12.012

    Article  Google Scholar 

  20. Huang N, Shen Z, Long S, Wu M, Shih H, Zheng Q, Yen N, Tung C, Liu H (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Roy Soc Lond Ser A Math Phys Eng Sci 454(1971):903–995

    Article  MATH  Google Scholar 

  21. Smith J (2005) The local mean decomposition and its application to EEG perception data. J R Soc Interface 2(5):443–454. https://doi.org/10.1098/rsif.2005.0058

    Article  Google Scholar 

  22. Feldman M (2006) Time-varying vibration decomposition and analysis based on the Hilbert transform. J Sound Vib 295(3):518–530. https://doi.org/10.1016/j.jsv.2005.12.058

    Article  MATH  Google Scholar 

  23. Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999–4010. https://doi.org/10.1109/TSP.2013.2265222

    Article  MATH  Google Scholar 

  24. Quqa S, Landi L, Paolo Diotallevi P (2021) Modal assurance distribution of multivariate signals for modal identification of time-varying dynamic systems. Mech Syst Signal Process 148:107136. https://doi.org/10.1016/j.ymssp.2020.107136

    Article  Google Scholar 

  25. Nassef M, Hussein T, Mokhiamar O (2020) An adaptive variational mode decomposition based on sailfish optimization algorithm and Gini index for fault identification in rolling bearings. Measurement 173:108514. https://doi.org/10.1016/j.measurement.2020.108514

    Article  Google Scholar 

  26. Huang Y, Deng Y (2021) A new crude oil price forecasting model based on variational mode decomposition. Knowl-Based Syst 213:106669. https://doi.org/10.1016/j.knosys.2020.106669

    Article  Google Scholar 

  27. Admasie S, Bukhari SBA, Haider R, Gush T, Kim C-H (2019) A passive islanding detection scheme using variational mode decomposition-based mode singular entropy for integrated microgrids. Electr Power Syst Res 177:105983. https://doi.org/10.1016/j.epsr.2019.105983

    Article  Google Scholar 

  28. Hu H, Wang L, Tao R (2021) Wind speed forecasting based on variational mode decomposition and improved echo state network. Renew Energy 164:729–751. https://doi.org/10.1016/j.renene.2020.09.109

    Article  Google Scholar 

  29. Wei W, Li L, Shi W-f, Liu J-p (2021) Ultrasonic imaging recognition of coal-rock interface based on the improved variational mode decomposition. Measurement 170:108728. https://doi.org/10.1016/j.measurement.2020.108728

    Article  Google Scholar 

  30. Tsai J-P, Hsiao C-T (2020) Spatiotemporal analysis of the groundwater head variation caused by natural stimuli using independent component analysis and continuous wavelet transform. J Hydrol 590:125405. https://doi.org/10.1016/j.jhydrol.2020.125405

    Article  Google Scholar 

  31. Sharma R (2020) Musical instrument sound signal separation from mixture using DWT and Fast ICA based algorithm in noisy environment. Mater Tod Proc 29:536–547. https://doi.org/10.1016/j.matpr.2020.07.310

    Article  Google Scholar 

  32. Han L, Li CW, Guo SL, Su XW (2015) Feature extraction method of bearing AE signal based on improved FAST-ICA and wavelet packet energy. Mech Syst Signal Process 62–63:91–99. https://doi.org/10.1016/j.ymssp.2015.03.009

    Article  Google Scholar 

  33. Yang Y, Nagarajaiah S (2014) Blind identification of damage in time-varying systems using independent component analysis with wavelet transform. Mech Syst Signal Process 47(1):3–20. https://doi.org/10.1016/j.ymssp.2012.08.029

    Article  Google Scholar 

  34. Sanchetta AC, Leite EP, Honório BCZ (2013) Facies recognition using a smoothing process through fast independent component analysis and discrete cosine transform. Comput Geosci 57:175–182. https://doi.org/10.1016/j.cageo.2013.03.021

    Article  Google Scholar 

  35. Fan Q, Chen Z, Zhang W, Fang X (2022) ESSAWOA: enhanced whale optimization algorithm integrated with Salp swarm algorithm for global optimization. Eng Comput 38(s1):s797–s814. https://doi.org/10.1007/s00366-020-01189-3

    Article  Google Scholar 

  36. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  37. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  38. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey–Wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  39. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513. https://doi.org/10.1007/s00521-015-1870-7

    Article  Google Scholar 

  40. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  41. Jutten C, Herault J (1991) Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture. Signal Process 24(1):1–10. https://doi.org/10.1016/0165-1684(91)90079-X

    Article  MATH  Google Scholar 

  42. Koh CG, Perry MJ (2009) structural identification and damage detection using genetic algorithms: structures and infrastructures book series, vol 6. CRC Press, London

Download references

Funding

This paper is supported by Open Foundation of Key Laboratory for Digital Land and Resources of Jiangxi Province (No. DLLJ201911), Fujian Provincial Transport Science and Technology Project (No. 202103), 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), Science and Technology Project of Longyan City (No. 2020LYF9005), Guangxi Key Laboratory of Spatial Information and Geomatics (No. 19-185-10-03), Science and Technology Project of Fuzhou City (No. 2020-GX-18). Funding was provided by National Natural Science Foundation of China (Grant No. 41404008).

Author information

Authors and Affiliations

  1. College of Civil Engineering, Fuzhou University, Fuzhou, 350108, China

    Qian Fan & Zhanghua Xia

  2. School of Civil Engineering, Southeast University, Nanjing, 211189, China

    Zhenjian Chen

  3. Fujian Academy of Building Research, Fuzhou, 350025, China

    Wei Zhang

Authors
  1. Qian Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenjian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhanghua Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qian Fan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, Q., Chen, Z., Xia, Z. et al. A novel structural damage detection strategy based on VMD-FastICA and ESSAWOA. J Civil Struct Health Monit 13, 149–163 (2023). https://doi.org/10.1007/s13349-022-00629-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13349-022-00629-6

Keywords

Search

Navigation