Smart Structures and Systems

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Predicting the buckling load of smart multilayer columns using soft computing tools

  • Received : 2012.06.17
  • Accepted : 2013.03.25
  • Published : 2014.01.25
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Abstract

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.

Keywords

References

  1. Alghamdi, A.A.A. (2001), "Adaptive imperfect column with piezoelectric actuators", J. Intel. Mat. Syst. Str., 12(3), 183-189. https://doi.org/10.1106/UA0K-QWXQ-P8KL-G3K2
  2. Baz, A. and Tampe, L. (1989), "Active control of buckling of flexible beams", Proceedings of the ASME Design and Technology Conference, DE-Vol. 16, Montreal, Canada.
  3. Baz, A., Ro, J., Mutua, M. and Gilheany, J. (1991), "Active buckling control of nitinol-reinforced composite beams", Proceedings of the Active Materials and Adaptive Structures Conference, Alexandria, Virginia.
  4. Berlin, A.A. (1994), Towards intelligent structures active control of buckling, Ph.D. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology.
  5. Berlin, A.A, Chase, J.G., Yim, M.H., Maclean, B.J., Olivier, M. and Jacobsen, S.C. (1998), "MEMS-Based control of structural dynamic instability", J. Intell. Mat. Syst. Str., 9(7), 574-586 https://doi.org/10.1177/1045389X9800900709
  6. Bilgehan, M. (2011), "Comparison of ANFIS and NN models-With a study in critical buckling load estimation", Appl. Soft Comput., 11(4), 3779-3791. https://doi.org/10.1016/j.asoc.2011.02.011
  7. Bilgehan, M., Gurel, M.A., Pekgokgoz, R.K. and Kisa, M. (2012), "Buckling load estimation of cracked columns using artificial neural network modeling technique", J. Civil Eng. Manage., 18(4), 568-579. https://doi.org/10.3846/13923730.2012.702988
  8. Cevik, A., Atmaca, N., Ekmekyapar, T. and Guzelbey, I.H. (2009), "Flexural buckling load prediction of aluminium alloy columns using soft computing techniques", Expert Syst. Appl., 36(3), 6332-6342. https://doi.org/10.1016/j.eswa.2008.08.011
  9. Chase, J.G. and Bhashyam, S. (2001), "Optimal stabilization of indefinite plate buckling problems", Smart Mater. Struct., 10(4), 786-793. https://doi.org/10.1088/0964-1726/10/4/324
  10. Chiu, S.L. (1994), "Fuzzy model identification based on cluster estimation". J. Intell. Fuzzy Syst., 2, 267-278. https://doi.org/10.1109/91.324806
  11. Fausett, L. (1994), Fundamentals of neural networks, Prentice Hall (Englewood Cliffs, NJ).
  12. Gurel, M.A., (2007), "Buckling of slender prismatic circular columns weakened by multiple edge cracks", Acta Mech., 188(1-2), 1-19. https://doi.org/10.1007/s00707-006-0386-8
  13. Jain, P. and Deo, M.C. (2006), "Neural networks in ocean engineering", SAOS, 1(1), 25-35.
  14. Jang, J.S.R., Sun, C.T. and Mizutani, E. (1997), Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence, Prentice Hall.
  15. Kapuria, S. and Achary, G.G.S. (2006), "Nonlinear coupled zigzag theory for buckling of hybrid piezoelectric plates", Compos. Struct., 74(3), 253-264. https://doi.org/10.1016/j.compstruct.2005.04.010
  16. Kundu, C.K., Maiti, D.K. and Sinha, P.K. (2007), "Post buckling analysis of smart laminated doubly curved shells", Compos Struct., 81(3), 314-322. https://doi.org/10.1016/j.compstruct.2006.08.023
  17. Maurini, C., Pouget, J. and Vidoli, S. (2007), "Distributed piezoelectric actuation of a bistable buckled beam". Eur. J. Mech. A - Solid, 26(5), 837-853. https://doi.org/10.1016/j.euromechsol.2007.02.001
  18. Meressi, T. and Paden, B. (1993), "Buckling control of afl exible beam using piezoelectric actuators", J. Guid. Control Dynam., 16(5), 977-980. https://doi.org/10.2514/3.21113
  19. Nezamabadi, A.R. and Karimi Khorramabadi, M. (2010), "Mechanical buckling of Engesser-Timoshenko beams with a pair of piezoelectric layers", Acad. Sci. Eng. Technol., 71, 491-494.
  20. Oh, I.K., Han, J.H. and Lee, I. (2000), "Postbuckling and vibration characteristics of piezolaminated composite plate subject to thermopiezoelectric load", J. Sound Vib., 233(1), 19-40. https://doi.org/10.1006/jsvi.1999.2788
  21. Oh, I.K., Han, J.H. and Lee, I. (2001), "Thermopiezoelastic snapping of piezolaminated plates using layerwise non-linear finite elements", AIAA J., 39(6), 1188-1197. https://doi.org/10.2514/2.1434
  22. Rajaee, T., Mirbagheri, S.A., Zounemat-Kermani, M. and Nourani, V. (2009), "Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models", Sci. Total Environ., 407(17), 4916-4927. https://doi.org/10.1016/j.scitotenv.2009.05.016
  23. Samarasinghe, S. (2007), Neural networks for applied sciences and engineering from fundamentals to complex pattern recognition, Auerbach Publications, Taylor & Francis Group, LLC.
  24. Shariyat, M. (2009), "Dynamic buckling of imperfect laminated plates with piezoelectric sensors and actuators subjected to thermo-electro-mechanical loadings, considering the temperature-dependency of the material properties", Compos. Struct., 88(2), 228-239. https://doi.org/10.1016/j.compstruct.2008.03.044
  25. Sheidaii, M.R. and Bahraminejad, R. (2012), "Evaluation of compression member buckling and post-buckling behavior using artificial neural network", J. Constr. Steel Res., 70, 71-77. https://doi.org/10.1016/j.jcsr.2011.10.020
  26. Shen, H.S. (2001), "Thermal postbuckling of shear-deformable laminated plates with piezoelectric actuators", Compos. Sci. Technol., 61(13), 1931-143. https://doi.org/10.1016/S0266-3538(01)00099-9
  27. Thompson, S.P. and Loughlan, J. (1995), "The active buckling control of some composite column strips using piezoceramic actuators", Compos. Struct, 32(1-4), 59-67. https://doi.org/10.1016/0263-8223(95)00048-8
  28. Varelis, D. and Saravanos D. (2002), "Nonlinear coupled mechanics and initial buckling of composite plate with piezoelectric actuators and sensors", Smart Mater. Struct., 11(3), 330-336. https://doi.org/10.1088/0964-1726/11/3/302
  29. Waszczyszyn, Z. and Bartczak, M. (2002), "Neural prediction of buckling loads of cylindrical shells with geometrical imperfection", Int. J. Nonlinear Mech., 37(4-5), 763-775. https://doi.org/10.1016/S0020-7462(01)00111-1
  30. Weiland, R. and Mirschel, W. (2008), "Adaptive fuzzy modelling versus artificial neural networks", Environ. Model. Softw., 23(2), 215-224. https://doi.org/10.1016/j.envsoft.2007.06.004

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