Smart Structures and Systems

  • 제16권6호
  • /
  • Pages.1107-1132
  • /
  • 2015
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Damage detection in structural beam elements using hybrid neuro fuzzy systems

  • Aydin, Kamil (Erciyes University, Department of Civil Engineering) ;
  • Kisi, Ozgur (Canik Basari University, Department of Civil Engineering)
  • 투고 : 2014.12.19
  • 심사 : 2015.12.05
  • 발행 : 2015.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

키워드

참고문헌

  1. Abonyi, J., Andersen, H.C., Nagy, L. and Szeifert, F. (1999), "Inverse fuzzy process model based direct adaptive control", Math. Comput. Simulat., 51(1-2), 119-132. https://doi.org/10.1016/S0378-4754(99)00142-1
  2. Altug, S., Chen, M.Y. and Trussell, H.J. (1999), "Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis", IEEE T. Ind. Electron., 46(6), 1069-1079. https://doi.org/10.1109/41.807988
  3. Aydin, K. and Kisi, O. (2015), "Applicability of fuzzy genetic system for crack diagnosis in Timoshenko beams", J. Comput. Civil Eng.-ASCE, 29(5), in press.
  4. Babu, T.R. and Sekhar, A.S. (2008), "Detection of two cracks in a rotor-bearing system using amplitude deviation curve", J. Sound Vib., 314(3-5), 457-464. https://doi.org/10.1016/j.jsv.2008.03.011
  5. Bakhary, N. (2008), Structural condition monitoring and damage identification with artificial neural network, Ph.D. Dissertation, University of Western Australia, Perth, WA, Australia.
  6. Barai, S.V. and Pandey, P.C. (1995), "Vibration signature analysis using artificial neural networks", J. Comput. Civil Eng.-ASCE, 9(4), 259-265. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(259)
  7. Caddemi, S. and Morassi, A. (2013), "Multi-cracked Euler-Bernoulli beams: Mathematical modeling and exact solutions", Int. J. Solids Struct., 50, 944-956. https://doi.org/10.1016/j.ijsolstr.2012.11.018
  8. Carden, P.E. and Fanning, P. (2004), "Vibration based condition monitoring: A review", Struct. Health Monit., 3(4), 355-377. https://doi.org/10.1177/1475921704047500
  9. Chang, C.C., Chang, T.Y.P. and Xu, Y.G. (2000), "Structural damage detection using an iterative neural network", J. Intel. Mat. Syst. Str., 11, 32-42. https://doi.org/10.1177/104538900772664387
  10. Chen, J., Roberts, C. and Weston, P. (2008), "Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems", Control Eng. Pract., 16(5), 585-596. https://doi.org/10.1016/j.conengprac.2007.06.007
  11. Chiu, S. (1994), "Fuzzy model identification based on cluster estimation", J. Intel. Fuzzy Syst., 2, 267-278. https://doi.org/10.1109/91.324806
  12. Choi, F.C., Li, J., Samali, B. and Crews, K. (2008), "Application of the modified damage index method to timber beams", Eng. Struct., 30(4), 1124-1145. https://doi.org/10.1016/j.engstruct.2007.07.014
  13. Cobaner, M. (2011), "Evapotranspiration estimation by two different neuro-fuzzy inference systems", J. Hydrol., 398(3-4), 292-302. https://doi.org/10.1016/j.jhydrol.2010.12.030
  14. Czogaa, E., Czogala, E. and Leski, J. (2000), Fuzzy and Neuro-Fuzzy Intelligent Systems, Springer-Verlag, Heidelberg.
  15. Dackermann, U. (2010), "Vibration-based damage identification methods for civil engineering structures using artificial neural networks", Ph.D. Dissertation, University of Technology, Sydney, Australia.
  16. Dempsey, P. and Afjeh, A. (2004), "Integrated oil debris and vibration gear damage detection technologies using fuzzy logic", J. Am. Helicopter Soc., 49, 109-116. https://doi.org/10.4050/JAHS.49.109
  17. Dimarogonas, A.D. (1996), "Vibration of cracked structures: A state of the art review", Eng. Fract. Mech., 55, 831-857. https://doi.org/10.1016/0013-7944(94)00175-8
  18. Drake, J.T. (2000), Communications phase synchronization using the adaptive network fuzzy inference system, Ph.D. Dissertation, New Mexico State University, Las Cruces, New Mexico.
  19. Du, J. and Er, M.J. (2004), "Fault diagnosis in air-handling unit system using dynamic fuzzy neural network", Proceedings of the 6th International FLINS Conference, Blankenberge, Belgium, September.
  20. Fan, W. and Qiao, P. (2011), "Vibration-based damage identification methods: A review and comparative study", Struct. Health Monit., 10(1), 83-111. https://doi.org/10.1177/1475921710365419
  21. Fang, X., Luo, H. and Tang, J. (2005), "Structural damage detection using neural network with learning rate improvement", Comput. Struct., 83(25-26), 2150-2161. https://doi.org/10.1016/j.compstruc.2005.02.029
  22. Friswell, M.I. and Penny, J.E.T. (2002), "Crack modeling for structural health monitoring", Struct. Health Monit., 1(2), 139-148. https://doi.org/10.1177/1475921702001002002
  23. Ganguli, R. (2001), "A fuzzy logic system for ground based structural health monitoring of a helicopter rotor using modal data", J. Intell. Mat. Syst. Str., 12(6), 397-407. https://doi.org/10.1106/104538902022598
  24. Garesci, F., Catalano, L. and Petrone, F. (2006), "Experimental results of a damage detection methodology using variations in modal parameters", Exp. Mech., 46, 441-451. https://doi.org/10.1007/s11340-006-8151-4
  25. Gonzalez, M.P. and Zapico, J.L. (2008), "Seismic damage identification in buildings using neural networks and modal data", Comput. Struct., 86(3-5), 416-426. https://doi.org/10.1016/j.compstruc.2007.02.021
  26. Hamey, C.S., Lestari, W., Qiao, P. and Song, G. (2004), "Experimental damage identification of carbon/epoxy composite beams using curvature mode shapes", Struct. Health Monit., 3(4), 333-353. https://doi.org/10.1177/1475921704047502
  27. Haykin, S. (1998), Neural Networks - A Comprehensive Foundation, Prentice-Hall, Upper Saddle River, New Jersey.
  28. Jang, J.S.R. (1993), "ANFIS: adaptive-network-based fuzzy inference system", IEEE T. Syst. Man Cyb., 23(3), 665-685. https://doi.org/10.1109/21.256541
  29. Jang, J.S.R., Sun, C.T. and Mizutani, E. (1997), Neoro-Fuzzy and Soft Computing, Prentice Hall, Upper Saddle River, New Jersey.
  30. Kao, C.Y. and Hung, S.L. (2005), "A neural network-based approach for detection of structural damage", Proceedings of the 16th IASTED Conference on Modeling and Simulation, Cancun, Mexico, May.
  31. Karunanithi, N., Grenney, W.J., Whitley, D. and Bovee, K. (1994), "Neural networks for river flow prediction", J. Comput. Civil Eng.-ASCE, 8(2), 201-220. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(201)
  32. Kim, B.H., Park, T. and Voyiadjis, G.Z. (2006), "Damage estimation on beam-like structures using the multi-resolution analysis", Int. J. Solids Struct., 43(14-15), 4238-4257. https://doi.org/10.1016/j.ijsolstr.2005.07.022
  33. Kisi, O. (2006), "Daily pan evaporation modeling using a neuro-fuzzy computing technique", J. Hydrol., 329(3-4), 636-646. https://doi.org/10.1016/j.jhydrol.2006.03.015
  34. Lauwagie, T., Sol, H. and Dascotte, E. (2002), "Damage identification in beams using inverse methods", Proceedings of the International Seminar on Modal Analysis (ISMA), Leuven, Belgium, Sptember.
  35. Lei, Y., He, Z. and Zi, Y. (2008), "A new approach to intelligent fault diagnosis of rotating machinery", Expert Syst. Appl., 35(4), 1593-1600. https://doi.org/10.1016/j.eswa.2007.08.072
  36. Li, H., Yang, H. and Hu, S.L.J. (2006), "Modal strain energy decomposition method for damage localization in 3D frame structures", J. Eng. Mech.-ASCE, 132(9), 941-951. https://doi.org/10.1061/(ASCE)0733-9399(2006)132:9(941)
  37. Li, Q.S. (2001), "Buckling of multi-step cracked columns with shear deformation", Eng. Struct., 23, 356-364. https://doi.org/10.1016/S0141-0296(00)00047-X
  38. Marseguerra, M., Zio, E. and Avogadri, P. (2004), "Model identification by neuro-fuzzy techniques: predicting the water level in a steam generator of a PWR", Prog. Nucl. Energ., 44(3), 237-252. https://doi.org/10.1016/S0149-1970(04)90012-1
  39. Nanda, B., Maity, D. and Maiti, D.K. (2012), "Vibration-based structural damage detection technique using particle swarm optimization with incremental swarm size", Int. J. Aeronaut. Space Sci., 13(3), 323-331. https://doi.org/10.5139/IJASS.2012.13.3.323
  40. Ni, Y.Q., Zhou, X.T., Ko, J.M. and Wang, B.S. (2000), "Vibration based damage localization in Ting Kau Bridge using probabilistic neural network", Adv. Struct. Dyn., 2, 1069-1076.
  41. Nyongesa, H.O., Otieno, A.W. and Rosin, P.L. (2001), "Neural fuzzy analysis of delaminated composites from shearography imaging", Compos. Struct., 54(2-3), 313-318. https://doi.org/10.1016/S0263-8223(01)00103-9
  42. Oruganti, K., Mehdizadeh, M., John, S. and Herszberg, I. (2009), "Vibration-based analysis of damage in composites", Mater. Forum., 33, 496-504.
  43. Pawar, P.M. and Ganguli, R. (2005), "Matrix cracking detection in thin-walled composite beam using genetic fuzzy system", J. Intell. Mat. Syst. Str., 16(5), 381-468.
  44. Pawar, P.M. and Ganguli, R. (2011), Structural Health Monitoring Using Genetic Fuzzy Systems, Springer-Verlag, London.
  45. Ramu, S. and Johnson, V. (1995), "Damage assessment of composite structures using fuzzy logic integrated neural-network approach", Comput. Struct., 57, 491-502. https://doi.org/10.1016/0045-7949(94)00624-C
  46. Saadat, S.A., Buckner, G.D. and Noori, M.N. (2007), "Structural system identification and damage detection using the intelligent parameter varying technique: An experimental study", Struct. Health Monit., 6(3), 231-243. https://doi.org/10.1177/1475921707081980
  47. Saeed, R.A., Galybin, A.N. and Popov, V. (2012), "Crack identification in curvilinear beams by using ANN and ANFIS based on natural frequencies and frequency response functions", Neural Comput. Appl., 21(7), 1629-1645. https://doi.org/10.1007/s00521-011-0716-1
  48. Saeed, R.A. and George, L.E. (2011), "The use of ANN for cracks predictions in curvilinear beams based on their natural frequencies and frequency response functions", J. Comput., 3(12), 113-125.
  49. Sahin, M. and Shenoi, R.A. (2003), "Vibration-based damage identification in beam-like composite laminates by using artificial neural networks", J. Mech. Eng. Sci., 217(6), 661-676. https://doi.org/10.1243/095440603321919581
  50. Sawyer, J. and Rao, S. (2000), "Structural damage detection and identification using fuzzy logic", AIAA J., 38, 2328-2335. https://doi.org/10.2514/2.902
  51. Shi, Z.Y., Law, S.S. and Zhang, L.M. (2000), "Damage localization by directly using incomplete mode shapes", J. Eng. Mech.-ASCE, 126(6), 656-660. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:6(656)
  52. Shim, M.B. and Suh, M.W. (2002), "Crack identification using neuro-fuzzy-evolutionary technique", KSME Int. J., 16(4), 454-467. https://doi.org/10.1007/BF03185075
  53. Sinha, J.K., Friswell, M.I. and Edwards, S. (2002), "Simplified models for the location of cracks in beam structures using measured vibration data", J. Sound Vib., 251(1), 13-38. https://doi.org/10.1006/jsvi.2001.3978
  54. Soh, C. and Bhalla, S. (2005), "Calibration of piezo-impedance transducers for strength prediction and damage assessment of concrete", Smart Mater. Struct., 14, 671-684. https://doi.org/10.1088/0964-1726/14/4/026
  55. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W. and Nadler, B. R. (2004), A review of structural health monitoring literature: 1996-2001, Los Alamos National Laboratory Report No. LA-13976-MS, Los Alamos, New Mexico.
  56. Su, Z. and Ye, L. (2004), "Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm", Compos. Struct., 66(1-4), 627-637. https://doi.org/10.1016/j.compstruct.2004.05.011
  57. Thatoi, D.N., Ojha, A.A., Bhanjadeo, A., Mahapatra, R., Sahoo, S. and Mohapatra, S. (2013), "Application of artificial intelligence techniques for detection of cracks-A review", IACSIT Int. J. Eng. Tech., 5(1), 57-59.
  58. Tran, V.T., Yang, B.S., Oh, M.S. and Tan, A.C.C. (2009), "Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference", Expert Syst. Appl., 36(2), 1840-1849. https://doi.org/10.1016/j.eswa.2007.12.010
  59. Tsou, P. and Shen, H.M.H. (1994), "Structural damage detection and identification using neural networks", AIAA J., 32(1), 176-183. https://doi.org/10.2514/3.11964
  60. Vieira, J., Dias, F.M. and Mota, A. (2004), "Artificial neural networks and neuro-fuzzy systems for modeling and controlling real systems: a comparative study", Eng. Appl. Artif. Intel., 17, 265-273. https://doi.org/10.1016/j.engappai.2004.03.001
  61. Vinayak, H.K., Kumar, A., Agarwal, P. and Thakkar, S.K. (2008), "NN based damage detection from modal parameter changes", Proceedings of the 14th World Conference on Earthquake Engineering, Beijing, China, October.
  62. Wang, J.H. and Chuang, S.C. (2004), "Reducing errors in the identification of structural joint parameters using error functions", J. Sound Vib., 273(1-2), 295-316. https://doi.org/10.1016/S0022-460X(03)00502-9
  63. Weaver, W., Timoshenko, S.P. and Young, D.H. (1990), Vibration Problems in Engineering. Wiley, New York.
  64. Wei, M., Bai, B., Sung, A.H., Liu, Q., Wang, J. and Cather, M.E. (2007), "Predicting injection profiles using ANFIS", Inform. Sci., 177, 4445-4461. https://doi.org/10.1016/j.ins.2007.03.021
  65. Wu, X., Ghaboussi, J. and Garrett, J.H. (1992), "Use of neural networks in detection of structural damage", Comput. Struct., 42, 649-659. https://doi.org/10.1016/0045-7949(92)90132-J
  66. Yager, R.R. and Filev, D.P. (1994). Essentials of Fuzzy Modeling and Control, John Wiley & Sons, New York.
  67. Yan, Y., Cheng, L., Wu, Z. and Yam, L. (2007), "Development in vibration-based structural damage detection technique", Mech. Syst. Signal Pr., 21, 2198-2211. https://doi.org/10.1016/j.ymssp.2006.10.002
  68. Ye, Z., Sadeghian, A. and Wu, B. (2006), "Mechanical fault diagnostics for induction motor with variable speed drives using adaptive neuro-fuzzy inference system", Electr. Pow. Syst. Res., 76, 742-752. https://doi.org/10.1016/j.epsr.2005.10.011
  69. Zadeh, L.A. (1996), "Fuzzy logic = computing with words", IEEE T. Fuzzy Syst., 4(2), 103-111. https://doi.org/10.1109/91.493904
  70. Zang, C. and Imregun, M. (2001a), "Combined neural network and reduced FRF techniques for slight damage detection using measured response data", Arch. Appl. Mech., 71(8), 525-536. https://doi.org/10.1007/s004190100154
  71. Zang, C. and Imregun, M. (2001b), "Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection", J. Sound Vib., 242(5), 813-827. https://doi.org/10.1006/jsvi.2000.3390
  72. Zapico, J.L., Gonzalez, M.P. and Worden, K. (2003), "Damage assessment using neural network", Mech. Syst. Signal Pr., 17(1), 119-125. https://doi.org/10.1006/mssp.2002.1547
  73. Zhao, Z. and Chen, C. (2002), "A fuzzy system for concrete bridge damage diagnosis", Comput. Struct., 80, 629-641. https://doi.org/10.1016/S0045-7949(02)00031-7
  74. Zio, E. and Gola, G. (2006), "Neuro-fuzzy pattern classification for fault diagnosis", Ann. Nucl. Energy, 33, 415-426. https://doi.org/10.1016/j.anucene.2005.12.008
  75. Zio, E. and Gola, G. (2009), "A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery", Reliab. Eng. Syst. Safe., 94, 78-88. https://doi.org/10.1016/j.ress.2007.03.040

피인용 문헌

  1. Approach to establish a hybrid intelligent model for crack diagnosis in a fix-hinge beam structure pp.1757-9864, 2019, https://doi.org/10.1108/IJSI-05-2018-0029
  2. An integral based fuzzy approach to evaluate waste materials for concrete vol.19, pp.3, 2015, https://doi.org/10.12989/sss.2017.19.3.323
  3. Fuzzy rule based seismic risk assessment of one-story precast industrial buildings vol.18, pp.3, 2015, https://doi.org/10.1007/s11803-019-0526-5
  4. Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models vol.22, pp.8, 2015, https://doi.org/10.1631/jzus.a2000408