Fault detection in cracked structures under moving load through a recurrent-neural-networks-based approach

Document Type : Research Article

Authors

1 Department of Mechanical Engineering, Vardhaman College of Engineering, Hyderabad, India.

2 Department of Mechanical Engineering, National Institute of Technology, Rourkela, India.

Abstract

The current work is based on the development of an indirect approach in the domain of Recurrent Neural Networks (RNNs) to identify and quantify cracks on a multi-cracked cantilever beam structure subjected to transit mass. At first, the responses of the multi-cracked structure subjected to transit load are determined using fourth order Runge-Kutta numerical method and finite element analysis (FEA) has been executed using ANSYS software to authenticate the employed numerical method. The existences and positions of cracks are identified from the measured dynamic excitation of the structure. The crack severities are found out by FEA as forward problem. The modified Elman’s Recurrent Neural Networks (ERNNs) approach has been implemented as inverse problem to predict the locations and severities of cracks in the structure by applying Levenberg-Marquardt (LM) back propagation algorithm. The present analogy has been carried out in a supervised manner to check the convergence of the proposed algorithm. The proposed ERNNs method converge good results with those of theory and FEA.

Keywords

Main Subjects

References

References:
1. Chaudhari, T.D. and Maiti, S.K. "A study of vibration of geometrically segmented beams with and without crack", International Journal of Solids and Structures, 37, pp. 761-779 (2000).
2. Chinchalkar, S. "Determination of crack location in beams using natural frequencies", Journal of Sound and Vibration, 247(3), pp. 417-429 (2001).
3. Valoor, M.T., Chandrasekhar, K., and Agarwal, S. "Self-adaptive vibration control of smart composite beams using recurrent neural architecture", International Journal of Solids and Structures, 38, pp. 7857- 7874 (2001).
4. Lee, J.W., Kim, J.D., Yun, C.B., and Shim, J.M. "Health-monitoring method for bridges under ordinary traffic loadings", Journal of Sound and Vibration, 257(2), pp. 247-264 (2002).
5. Kao, C.Y. and Hung, S.L. "Detection of structural damage via free vibration responses generated by approximating artificial neural networks", Computers and Structures, 81, pp. 2631-2644 (2003).
6. Seker, S., Ayaz, E., and Turkcan, E. "Elman's recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery", Engineering Applications of Artificial Intelligence, 16, pp. 647-656 (2003).
7. Kim, J.T. and Stubbs, N. "Crack detection in beamtype structures using frequency data", Journal of Sound and Vibration, 259(1), pp. 145-160 (2003).
8. Law, S.S. and Zhu, X.Q. "Nonlinear characteristics of damaged concrete structures under vehicular load", Journal of Structural Engineering, 131(8), pp. 1277- 1285 (2005).
9. Nahvi, H. and Jabbari, M. "Crack detection in beams using experimental modal data and finite element model", International Journal of Mechanical Sciences, 47, pp. 1477-1497 (2005).
10. Chasalevris, A.C. and Papadopoulos, C.A., "Identification of multiple cracks in beams under bending", Mechanical Systems and Signal Processing, 20, pp. 1631-1673 (2006).
11. Schafer, A.M. and Zimmermann, H.G. "Recurrent neural networks are universal approximators", International Journal of Neural Systems, 17(4), pp. 253- 263 (2007).
12. Zhu, X.Q. and Law, S.S. "Damaged detection in simply supported concrete bridge structure under moving vehicular loads", Journal of Vibration and Acoustic, Transaction ASME, 129, pp. 58-65 (2007).
13. Li, Z. and Yang, X. "Damage identification for beams using ANN based on statistical property of structural responses", Computers and Structures, 86, pp. 64-71 (2008).
14. Talebi, H.A., Khorasani, K., and Tafazoli, S. "A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem", IEEE Transactions on Neural Networks, 20(1), pp. 45-60 (2009).
15. Sayyad, F.B. and Kumar, B. "Identification of crack location and crack size in a simply supported beam by measurement of natural frequencies", Journal of Vibration and Control, 18(2), pp. 183-190 (2010).
16. Perez, C.G. and Gonzalez, J.V. "Identification of structural damage in a vehicular bridge using artificial neural networks", Structural Health Monitoring, 10(1), pp. 33-16 (2011).
17. Shu, J., Zhang, Z., Gonzalez, I., and Karoumi, R. "The application of a damage detection method using artificial neural network and train-induced vibrations on a simplified railway bridge model", Engineering Structures, 52, pp. 408-421 (2013).
18. Asnaashari, E. and Sinha, J.K. "Crack detection in structures using deviation from normal distribution of measured vibration responses", Journal of Sound and Vibration, 333(18), pp. 4139-4151 (2014).
19. Oshima, Y., Yamamotoand, K., and Sugiura, K. "Damage assessment of a bridge based on mode shapes estimated by responses of passing vehicles", Smart Structures and Systems, 13(5), pp. 731-753 (2014).
20. Hakim, S.J.S., Razak, H.A., and Ravanfar, S.A. "Fault diagnosis on beam-like structures from modal parameters using artificial neural networks", Measurement, 76, pp. 45-61 (2015).
21. Kourehli, S.S. "Damage assessment in structures using incomplete modal data and artificial neural network", International Journal of Structural Stability and Dynamics, 15(6), Article no. 1450087 (2015).
22. Vosoughi, A.R. "A developed hybrid method for crack identification of beams", Smart Structures and Systems, 16(3), pp. 401-414 (2015).
23. Aydin, K. and Kisi, O. "Damage diagnosis in beamlike structures by artificial neural networks", Journal of Civil Engineering and Management, 21(5), pp. 591- 604 (2015).
24. Jena, S.P. and Parhi, D.R. "Comparative study on cracked beam with different types of cracks carrying moving mass", Structural Engineering and Mechanics, 56(5), pp. 797-81 (2015).
25. Koc, M.A., Ismail, E., and Cay, Y. "Tip deflection determination of a barrel for the effect of an accelerating projectile before firing using finite element and artificial neural network combined algorithm", Latin American Journal of Solids and Structures, 13, pp. 1968-1995 (2016).
26. He, W.Y. and Zhu, S. "Moving load-induced response of damaged beam and its application in damage localization", Journal of Vibration and Control, 22(16), pp. 3601-3617 (2016).
27. Limongelli, M.P., Siegert, D., Merliot, E., et al. "Damage detection in a post tensioned concrete beam - Experimental investigation", Engineering Structures, 128, pp. 15-25 (2016).
28. Amezquita-Sanchez, J.P., Valtierra-Rodriguez, M., Aldwaik, M., et al. "Neurocomputing in civil infrastructure", Scientia Iranica, A, International Journal of Science and Technology, 23(6), pp. 2417-2428 (2016).
29. Jena, S.P. and Parhi, D.R. "Parametric study on the response of cracked structure subjected to moving mass", Journal of Vibration Engineering & Technologies, 5(1), pp. 11-19 (2017).
30. Parhi, D.R. and Jena, S.P. "Dynamic and experimental analysis on response of multi-cracked structures carrying transit mass", Journal of Risk and Reliability, 231(1), pp. 25-35 (2017).
31. Yang, Q., Liu, J.K., Sun, B.X., et al. "Damage localization for beam structure by moving load", Advances in Mechanical Engineering, 9(3), pp. 1-6 (2017).
32. OBrien, E.J., Fitzgerald, P.C., Malekjafarian, A., et al. "Bridge damage detection using vehicle axle-force information", Engineering Structures, 153(15), pp. 71-80 (2017).
33. Toloue, I., Liew, M.S., Hamonangan Harahap, I., et al. "Damage detection in frame structures using noisy accelerometers and damage load vectors (DLV)", Scientia Iranaica, International Journal of Science and Technology (2018). DOI: 10.24200SCI.2018.50533.1741.
34. He, W.-Y, He, J., and Ren, W. "Damage localization of beam structures using mode shape extracted from moving vehicle response", Measurement, 121, pp. 276- 285 (2018).
35. Tehrani, H.A., Bakhshi, A., and Akhavat, M. "An effective approach for structural damage localization in flexural members based on generalized S-transform", Scientia Iranaica, International Journal of Science and Technology, 26(6), pp. 3125-3139 (2019). DOI:10.24200SCI.2017.20019.
36. Zhang, B., Qian, Y., Wu, Y., et al., "An effective means for damage detection of bridges using the contact-point response of a moving test vehicle", Journal of Sound and Vibration, 419, pp. 158-172 (2018).
37. Yu, H. and Wilamski, B.M. "Levenberg-Marquardt training", The Industrial Electronics Handbook, Intelligent Systems, 2nd Edition, Chapter-12, CRC Press, New York, US (2011).
Scientia Iranica
Volume 27, Issue 4
Transactions on Mechanical Engineering (B)
July and August 2020
Pages 1886-1896

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History

  • Receive Date: 29 January 2018
  • Revise Date: 20 December 2018
  • Accept Date: 04 February 2019

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