Skip to main content
SpringerLink
Log in

Damage identification in concrete structures using a hybrid time–frequency decomposition of acoustic emission responses

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

Abstract

Concrete structures are subjected to various levels of damage during their life cycle due to exposure to different environmental and loading conditions. Hence, damage severity prediction and localization in concrete elements remain critical tasks to provide optimal maintenance in a timely manner, leading to reduced maintenance costs and avoiding any catastrophic failure that may cause serious loss and danger to the infrastructure owners. The Acoustic Emission (AE) technique is one of the powerful nondestructive testing (NDT) methodologies that can be used to predict the severity of damage and localize it in different systems. This paper proposes a damage identification approach by combining Multivariate Empirical Mode Decomposition (MEMD) and the Gaussian Mixture Model (GMM). Unlike existing methods that rely on a single AE measurement, MEMD is used to extract the key AE components that belong to cracks using AE data collected from multiple sensors. MEMD is an extended version of EMD that can analyze multichannel AE signals collected from different sensors. The statistical features (e.g., mean, root mean square, kurtosis, skewness, and crest factor) of AE components obtained from MEMD are then subsequently estimated. The selected features are used in the GMM model to predict the severity and qualitative location of the damage. The proposed method is validated using a suite of experimental studies and AE data collected from a full-scale concrete dam located in Ontario, Canada. The results show that the proposed approach can accurately predict the severity of damage and identify the potential location of damage and prove that the proposed method can be suitable for robust damage identification in concrete structures.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Karthik MK, Kumar CS (2022) A comprehensive review on damage characterization in polymer composite laminates using acoustic emission monitoring. Russ J Nondestruct Test 58(8):705–721

    Article  Google Scholar 

  2. Abdul Kudus S, Muhamad Bunnori N, Mustaffa NK, Jamadin A (2022) Investigation on acoustic emission parameters due to fatigue damage of concrete beams with variable notched depth. Int J Concr Struct Mater. https://doi.org/10.1186/s40069-022-00518-8

    Article  Google Scholar 

  3. Ma G, Wu C (2023) Crack type analysis and damage evaluation of BFRP-repaired pre-damaged concrete cylinders using acoustic emission technique. Constr Build Mater 362:129674

    Article  CAS  Google Scholar 

  4. Janeliukstis R, McGugan M (2021) Control of damage-sensitive features for early failure prediction of wind turbine blades. Struct Control Health Monit. https://doi.org/10.1002/stc.2852

    Article  Google Scholar 

  5. Wu Y, Gu S, Zhao G, Li S (2020) Damage assessment of the in-service brick masonry structure using acoustic emission technique. Mater Struct. https://doi.org/10.1617/s11527-020-01475-y

    Article  Google Scholar 

  6. Barbosh M, Sadhu A, Sankar G (2021) Time–frequency decomposition-assisted improved localization of proximity of damage using acoustic sensors. Smart Mater Struct 30(2):025021

    Article  Google Scholar 

  7. Md Nor N, Ibrahim A, Muhamad Bunnori N, Saman HM, Mat Saliah SN, Shahidan S (2014) Diagnostic of fatigue damage severity on reinforced concrete beam using acoustic emission technique. Eng Fail Anal 41:1–9

    Article  Google Scholar 

  8. Abouhussien AA, Hassan AA (2015) Evaluation of damage progression in concrete structures due to reinforcing steel corrosion using acoustic emission monitoring. J Civ Struct Heal Monit 5(5):751–765

    Article  Google Scholar 

  9. Goldaran R, Turer A (2020) Application of acoustic emission for damage classification and assessment of corrosion in prestressed concrete pipes. Measurement 160:107855

    Article  Google Scholar 

  10. Verstrynge E, Van Steen C, Vandecruys E, Wevers M (2022) Steel corrosion damage monitoring in reinforced concrete structures with the acoustic emission technique: a review. Constr Build Mater 349:128732

    Article  Google Scholar 

  11. Shateri M, Ghaib M, Svecova D, Thomson D (2017) On acoustic emission for damage detection and failure prediction in fiber reinforced polymer rods using pattern recognition analysis. Smart Mater Struct 26(6):065023

    Article  Google Scholar 

  12. Behnia A, Chai HK, GhasemiGol M, Sepehrinezhad A, Mousa AA (2019) Advanced damage detection technique by integration of unsupervised clustering into acoustic emission. Eng Fract Mech 210:212–227

    Article  Google Scholar 

  13. Abouhussien AA, Hassan AA (2020) Classification of damage in self-consolidating rubberized concrete using acoustic emission intensity analysis. Ultrasonics 100:105999

    Article  CAS  PubMed  Google Scholar 

  14. Vidya Sagar R, Basu DJ (2022) Damage assessment of reinforced concrete structures under elevated-amplitude cyclic loading using sentry values based on acoustic emission testing. Nondestruct Test Eval 38:612–630

    Article  Google Scholar 

  15. Barile C, Casavola C, Pappalettera G, Paramsamy Kannan V (2022) Damage monitoring of carbon fibre reinforced polymer composites using acoustic emission technique and deep learning. Compos Struct 292:115629

    Article  CAS  Google Scholar 

  16. Shetty N, Livitsanos G, Van Roy N, Aggelis DG, Van Hemelrijck D, Wevers M, Verstrynge E (2019) Quantification of progressive structural integrity loss in masonry with acoustic emission-based damage classification. Constr Build Mater 194:192–204

    Article  Google Scholar 

  17. Verstrynge E, Lacidogna G, Accornero F, Tomor A (2021) A review on acoustic emission monitoring for damage detection in masonry structures. Constr Build Mater 268:121089

    Article  Google Scholar 

  18. Van Steen C, Verstrynge E (2021) Degradation monitoring in reinforced concrete with 3D localization of Rebar corrosion and related concrete cracking. Appl Sci 11(15):6772

    Article  Google Scholar 

  19. Boniface A, Saliba J, Sbartaï ZM, Ranaivomanana N, Balayssac J (2020) Evaluation of the acoustic emission 3D localisation accuracy for the mechanical damage monitoring in concrete. Eng Fract Mech 223:106742

    Article  Google Scholar 

  20. Barbosh M, Dunphy K, Sadhu A (2022) Acoustic emission-based damage localization using wavelet-assisted deep learning. J Infrastruct Preserv Resil. https://doi.org/10.1186/s43065-022-00051-8

    Article  Google Scholar 

  21. Mahajan H, Banerjee S (2023) Acoustic emission source localisation for structural health monitoring of rail sections based on a deep learning approach. Meas Sci Technol 34(4):044010

    Article  Google Scholar 

  22. Melchiorre J, Manuello Bertetto A, Rosso MM, Marano GC (2023) Acoustic emission and artificial intelligence procedure for crack source localization. Sensors 23(2):693

    Article  PubMed  PubMed Central  Google Scholar 

  23. Pang D, Jiang Y, Cao Y, Li B (2022) A defect localization approach based on improved areal coordinates and machine learning. Sensors 2022:1–12

    Google Scholar 

  24. Tayfur S, Alver N, Türk E, Menteşoğlu M, Ercan E (2023) Failure behavior of CFRP-strengthened reinforced concrete beam–column joints under reversed-cyclic lateral loading: mechanical and acoustic emission observations. Struct Eng Int. https://doi.org/10.1080/10168664.2022.2164237

    Article  Google Scholar 

  25. Xu X, Jin Z, Yu Y, Li N (2023) Damage source and its evolution of ultra-high performance concrete monitoring by digital image correlation and acoustic emission technologies. J Build Eng 65:105734

    Article  Google Scholar 

  26. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A Math Phys Eng Sci 454(1971):903–995

    Article  MathSciNet  Google Scholar 

  27. Barbosh M, Singh P, Sadhu A (2020) Empirical mode decomposition and its variants: a review with applications in structural health monitoring. Smart Mater Struct 29(9):093001

    Article  Google Scholar 

  28. Barbosh M, Sadhu A, Vogrig M (2018) Multisensor-based hybrid empirical mode decomposition method towards system identification of structures. Struct Control Health Monit 25(5):e2147

    Article  Google Scholar 

  29. Sadhu A (2016) An integrated multivariate empirical mode decomposition method towards modal identification of structures. J Vib Control 23(17):2727–2741

    Article  MathSciNet  Google Scholar 

  30. Sony S, Sadhu A (2022) Multivariate empirical mode decomposition-based structural damage localization using limited sensors. J Vib Control 28(15–16):2155–2167

    Article  PubMed  Google Scholar 

  31. Rehman N, Rehman DP (2010) Multivariate empirical mode decomposition. Proc R Soc A Math Phys Eng Sci 466:1291–1302

    MathSciNet  Google Scholar 

  32. Christlein V, Bernecker D, Hönig F, Maier A, Angelopoulou E (2017) Writer identification using GMM Supervectors and Exemplar-SVMs. Pattern Recogn 63:258–267

    Article  Google Scholar 

  33. Povinelli R, Johnson M, Lindgren A, Ye J (2004) Time series classification using gaussian mixture models of reconstructed phase spaces. IEEE Trans Knowl Data Eng 16(6):779–783

    Article  Google Scholar 

  34. Terejanu G, Singla P, Singh T, Scott PD (2008) Uncertainty propagation for nonlinear dynamic systems using gaussian mixture models. J Guid Control Dyn 31(6):1623–1633

    Article  Google Scholar 

  35. Mayorga P, Druzgalski C, Morelos RL, Gonzalez OH, Vidales J (2010) Acoustics based assessment of respiratory diseases using GMM classification. In: 2010 Annual international conference of the IEEE engineering in medicine and biology

  36. Yang J, Zhao K, Yu X, Yan Y, He Z, Lai Y, Zhou Y (2022) Crack classification of fiber-reinforced backfill based on gaussian mixed moving average filtering method. Cement Concr Compos 134:104740

    Article  CAS  Google Scholar 

  37. Sadhu A, Prakash G, Narasimhan S (2016) A hybrid hidden Markov model towards fault detection of rotating components. J Vib Control 23(19):3175–3195

    Article  Google Scholar 

  38. Kuyuk HS, Yildirim E, Dogan E, Horasan G (2012) Application of k-means and gaussian mixture model for classification of seismic activities in Istanbul. Nonlinear Process Geophys 19(4):411–419

    Article  Google Scholar 

  39. Reynolds D (1991) Gaussian mixture models. Lexington, MIT Lincoln Laboratory

    Google Scholar 

  40. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodol) 39(1):1–22

    MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the University of Tripoli for funding the research through the Libyan Ministry of Education. The authors would also like to thank the St. Clair Conservation Authority for providing access to the dam.

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Western University, London, ON, N6A 3K7, Canada

    Mohamed Barbosh & Ayan Sadhu

Authors
  1. Mohamed Barbosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayan Sadhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayan Sadhu.

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 (e.g. a society or other partner) 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

Barbosh, M., Sadhu, A. Damage identification in concrete structures using a hybrid time–frequency decomposition of acoustic emission responses. J Civil Struct Health Monit 14, 237–253 (2024). https://doi.org/10.1007/s13349-023-00718-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13349-023-00718-0

Keywords

Search

Navigation