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SVM-assisted damage identification in cantilever steel beam using vibration-based method

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Abstract

Structures naturally degrade over time, with the degree of deterioration influenced by factors such as location and construction quality. Additionally, unexpected loads can exacerbate this process, leading to varying degrees of failure. This study aims to improve the early detection of structural damage by integrating vibration-based methods with machine learning techniques, offering greater resilience compared to current approaches. Frequency-based analysis has its limitations, particularly in detecting damage farther from boundaries. To address this gap the study examines twenty-one cantilever beams with differing damage intensities and locations, alongside an undamaged beam, employing both experimental and numerical analyses. Frequency analysis conducted has shown that damage severity and location will affect the natural frequencies, especially revealing that while damage typically decreases fundamental frequency when it is nearer to the boundary and slowly frequency increases, while the damage moves farther from the boundary and for all the scenarios there exists one location where damage frequency is almost equal to undamaged. So, frequency analysis alone may sometimes fail to accurately detect damage. To overcome this limitation support vector machine algorithms are employed for the data obtained from the experimental analysis and found to achieve an accuracy of 85% in predicting the presence of damage, irrespective of its severity.

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Abbreviations

VBM:

Vibration-based method

SHM:

Structural health monitoring

NDT:

Non-destructive test

FFT:

Fast Fourier transform

EMA:

Experimental modal analysis

FEM:

Finite element method

FRF:

Frequency response function

MAC:

Modal assurance criteria

RFS:

Relative frequency shifts

IIRS:

Iterated improved reduction system

TMT:

Theoretical modal testing

PCA:

Principal component analysis

SVM:

Support vector machine

ANN:

Artificial neural network

ML:

Machine learning

M1:

Model-1 with damage intensity of 1 mm

M2:

Model-2 with damage intensity of 2 mm

M3:

Model-1 with damage intensity of 3 mm

UD:

Undamaged

References

  1. Raeisi F, Mufti A, Mustapha G, Thomson D (2017) Crack detection in steel girders of bridges using a broken wire electronic binary sensor. J Civil Struct Health Monit 7:1. https://doi.org/10.1007/s13349-017-0211-1

    Article  Google Scholar 

  2. Li Y, Ding Y, Zhao H-W, Sun Z (2022) Data-driven structural condition assessment for high-speed railway bridges using multi-band FIR filtering and clustering. Structures 41:1546–1558. https://doi.org/10.1016/j.istruc.2022.05.071

    Article  Google Scholar 

  3. Taher S, Li J, Jeong J, Laflamme S, Jo H, Bennett C, Collins W, Downey A (2022) Structural health monitoring of fatigue cracks for steel bridges with wireless large-area strain sensors. Sensors 22:5076. https://doi.org/10.3390/s22145076

    Article  CAS  Google Scholar 

  4. Katam R, Kalapatapu P, Pasupuleti VDK (2022) A review on technological advancements in the field of data-driven structural health monitoring. In: European workshop on structural health monitoring. Springer, Cham, pp 371–380. https://doi.org/10.1007/978-3-031-07322-9_38.

  5. Katam R, Pasupuleti VDK, Kalapatapu P (2023) A review on structural health monitoring: past to present. Innov Infrastruct Solut. https://doi.org/10.1007/s41062-023-01217-3

    Article  Google Scholar 

  6. Zhang C, Wan Le, Wan R-Q, Yu J, Li R (2022) Automated fatigue crack detection in steel box girder of bridges based on ensemble deep neural network. Measurement 202:111805. https://doi.org/10.1016/j.measurement.2022.111805

    Article  Google Scholar 

  7. Tong T, Lin J, Jiadong H, Gao F, Zhang H (2021) Crack identification for bridge condition monitoring using deep convolutional networks trained with a feedback-update strategy. Maintenan Reliabil Condit Monit 1:37–51. https://doi.org/10.21595/mrcm.2021.22032

    Article  Google Scholar 

  8. Quqa S, Martakis P, Movsessian A, Pai SG, Reuland Y, Chatzi E (2022) Two-step approach for fatigue crack detection in steel bridges using convolutional neural networks. J Civ Struct Heal Monit. https://doi.org/10.1007/s13349-021-00537-1

    Article  Google Scholar 

  9. Das S, Saha P, Patro S (2016) Vibration-based damage detection techniques used for health monitoring of structures: a review. J Civ Struct Health Monit 6:1. https://doi.org/10.1007/s13349-016-0168-5

    Article  CAS  Google Scholar 

  10. Cao M, Sha G, Gao Y, Ostachowicz W (2017) Structural Damage Identification Using Damping: A Compendium of Uses and Features. Smart Mater Struct 26:1. https://doi.org/10.1088/1361-665X/aa550a

    Article  Google Scholar 

  11. Kong X, Cai CS, Hu J (2017) The state-of-the-art on framework of vibration-based structural damage identification for decision making. Appl Sci 7(5):497. https://doi.org/10.3390/app7050497

    Article  Google Scholar 

  12. Al-Janabi I, Weli SS, Hamid F (2021) Cement-based materials for self-sensing and structural damage advance warning alert by electrical resistivity. Mater Today: Proc 46:615–620. https://doi.org/10.1016/j.matpr.2020.11.381

    Article  Google Scholar 

  13. Hou R, Xia Y (2020) Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019. J Sound Vib 491:1. https://doi.org/10.1016/j.jsv.2020.115741

    Article  Google Scholar 

  14. Zhao J, Zhang L (2012) Structural damage identification based on the modal data change. Int J Eng Manuf 2:59–66. https://doi.org/10.5815/ijem.2012.04.08

    Article  Google Scholar 

  15. Yan YJ, Wu Z, Yam LH (2007) Development in vibration-based structural damage detection technique. Mech Syst Signal Process 21:2198–2211. https://doi.org/10.1016/j.ymssp.2006.10.002

    Article  Google Scholar 

  16. Tawade S, Patil R, Kale S, Professor A (2022) Comparative analysis of natural frequency of transverse vibration of a cantilever beam by analytical and experimental methods

  17. Ishan N, Ritvik B, Stephen N, Chippa SP (2021) Experimental modal parameter identification and validation of cantilever beam. Mater Today: Proc 38(1):1. https://doi.org/10.1016/j.matpr.2020.07.396

    Article  Google Scholar 

  18. Gillich G-R, Maia N, Abdel WM, Tufisi C, Korka Z, Gillich N, Pop M (2021) Damage detection on a beam with multiple cracks: a simplified method based on relative frequency shifts. Sensors 21:5215. https://doi.org/10.3390/s21155215

    Article  Google Scholar 

  19. Chaudhari C, Gaikwad J, Bhanuse V, Kulkarni J (2014) Experimental investigation of crack detection in cantilever beam using vibration analysis. In: ICNSC 2014 - Proceedings 1st International Conference on Networks and Soft Computin, pp 130–134. https://doi.org/10.1109/CNSC.2014.6906685

  20. Das S, Dhang N (2021) Damage identification of structures using incomplete mode shape and improved TLBO-PSO with self-controlled multi-stage strategy. Structures 35:1. https://doi.org/10.1016/j.istruc.2021.07.089

    Article  Google Scholar 

  21. Siva SBC, Srinivasa RP, Bala KA (2021) Modal testing and evaluation of cracks on cantilever beam using mode shape curvatures and natural frequencies. Structures 32:1. https://doi.org/10.1016/j.istruc.2021.03.049

    Article  Google Scholar 

  22. Maes K, Meerbeeck L, Reynders E, Lombaert G (2022) Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51. Mech Syst Signal Process 165:108380. https://doi.org/10.1016/j.ymssp.2021.108380

    Article  Google Scholar 

  23. Khalkar V, Kumbhar S, Logesh K, Hariharasakhtisudhan P, Jadhav S, Danawade B, Gharat S, Jugulkar L, Borade J (2022) Experimental and numerical investigation of a cracked cantilever beam for damping factor to access its applicability in the crack detection. U Porto J Eng 8:1. https://doi.org/10.24840/2183-6493_008.002_0013.

  24. Kamariotis A, Chatzi E, Straub D (2023) A framework for quantifying the value of vibration-based structural health monitoring. Mech Syst Signal Process 184:109708. https://doi.org/10.1016/j.ymssp.2022.109708

    Article  Google Scholar 

  25. Rao M, Ch A (2022) A Novel Feature-Based SHM Assessment and Predication Approach for Robust Evaluation of Damage Data Diagnosis Systems. Wireless Pers Commun 124:1–25. https://doi.org/10.1007/s11277-022-09518-z

    Article  Google Scholar 

  26. Ghiasi R, Torkzadeh P, Noori M (2016) A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function. Struct Health Monit 15:302–316. https://doi.org/10.1177/1475921716639587

    Article  Google Scholar 

  27. Yam LH, Yan YJ, Jiang J (2003) Vibration-based damage detection for composite structures using wavelet transform and neural network identification. Compos Struct 60:403–412. https://doi.org/10.1016/S0263-8223(03)00023-0

    Article  Google Scholar 

  28. Yan YJ, Yam LH (2004) Detection of delamination damage in composite plates using energy spectrum of structural dynamic responses decomposed by wavelet analysis. Comput Struct 82:347–358. https://doi.org/10.1016/j.compstruc.2003.11.002

    Article  Google Scholar 

  29. Liu Q, Zhang J, Liu J, Yang Z (2022) Feature extraction and classification algorithm, which one is more essential? An experimental study on a specific task of vibration signal diagnosis. Int J Mach Learn Cybern 13:1–12. https://doi.org/10.1007/s13042-021-01477-4

    Article  Google Scholar 

  30. Pranesh H, Suresh K, Manian S, Jegadeeshwaran R, Gnanasekaran S, Manghi TM (2021) Vibration-based brake health prediction using statistical features—a machine learning framework. Mater Today Proc 46. https://doi.org/10.1016/j.matpr.2021.02.060.

  31. Katam R, Kalapatapu P, & Pasupuleti VDK. (2023). Smart Diagnosis of a Cantilever beam using SVM. International Conference on Emerging Techniques in Computational Intelligence. pp. 27–32. https://doi.org/10.1109/ICETCI58599.2023.10331129.

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Authors and Affiliations

  1. Center for Sustainable Infrastructure and Systems, Ecole Centrale School of Engineering, Mahindra University, Bahadurpally, Jeedimetla, Hyderabad, Telangana, 500043, India

    Rakesh Katam, Venkata Dilip Kumar Pasupuleti & Prafulla Kalapatapu

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  1. Rakesh Katam
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  2. Venkata Dilip Kumar Pasupuleti
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  3. Prafulla Kalapatapu
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Contributions

Rakesh Katam: Acquisition of data, and images, drafting of the manuscript, and providing the revised article content. Venkata Dilip Kumar Pasupuleti: Provided the area of study, Acquisition of data, and images, drafting of the manuscript, provided the revised article content, and final approval of the version to be submitted. Prafulla Kalapatapu: The manuscript provided the revised article content and final approval of the version to be submitted.

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Correspondence to Rakesh Katam.

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Katam, R., Pasupuleti, V.D.K. & Kalapatapu, P. SVM-assisted damage identification in cantilever steel beam using vibration-based method. Innov. Infrastruct. Solut. 9, 149 (2024). https://doi.org/10.1007/s41062-024-01459-9

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