DOI QR코드

DOI QR Code

Application of lattice probabilistic neural network for active response control of offshore structures

  • Kim, Dong Hyawn (Department of Ocean System Engineering, Kunsan National University) ;
  • Kim, Dookie (Department of Civil and Environmental Engineering, Kunsan National University) ;
  • Chang, Seongkyu (Department of Civil and Environmental Engineering, Kunsan National University)
  • Received : 2007.04.10
  • Accepted : 2008.12.27
  • Published : 2009.01.30

Abstract

The reduction of the dynamic response of an offshore structure subjected to wind-generated random ocean waves is of extreme significance in the aspects of serviceability, fatigue life and safety of the structure. In this study, a new neuro-control scheme is applied to the vibration control of a fixed offshore platform under random wave loads to examine the applicability of the proposed method. It is called the Lattice Probabilistic Neural Network (LPNN), as it utilizes lattice pattern of state vectors as the training data of PNN. When control results of the LPNN are compared with those of the NN and PNN, LPNN showed better performance in effectively suppressing the structural responses in a shorter computational time.

Keywords

Acknowledgement

Supported by : Korea Science & Engineering Foundation (KOSEF)

References

  1. Bang, J.M. (1994), 'Active control of fixed offshore structure', Master's Thesis. Dept. of Civil and Environ. Eng., Korea Adv. Inst. of Sci. and Tech., Korea
  2. Kim, D.H., Han, S.H., Park, W.S., Seo, S.N. and Lee, I.W. (2001), 'Learning rule of neuro-controller for structural control', Korean Society of Civil Engineers, 21(5-1), 657-663
  3. Kim, D.H., Kim, D., Chang, S. and Jung, H.Y. (2008), 'Active control strategy of structures based on lattice type probabilistic neural network', Probabilist. Eng. Mech., 23, 45-50 https://doi.org/10.1016/j.probengmech.2007.10.004
  4. Kim, D.H., Seo, S.N. and Lee, I.W. (2004), 'Optimal neurocontroller for nonlinear benchmark structure', J. Eng. Mech., ASCE, 130, 424-429 https://doi.org/10.1061/(ASCE)0733-9399(2004)130:4(424)
  5. Kim, D.K., Chang, S.K. and Chang, S.K. (2007), 'Vibration control of offshore structures for wave response reduction using probabilistic neural network', Workshop on High Performance Data Mining and Application (HPDMA'07), Nanjing, China, 222-232
  6. Kim, D.K. (2005), Dynamics of Structures. Goomibook, 425-440
  7. Li, H.J., Hu, S.J. and Takayama, T. (1999), 'The optimal design of TMD for offshore structures', China Ocean Eng., 13(2), 133-144
  8. Madan, A. (2005), 'Vibration control of building structures using self-organizing and self-learning neural networks' J. Sound Vib., 287(4/5), 759-784 https://doi.org/10.1016/j.jsv.2004.11.031
  9. Mohamed, A.R. (1996), 'Structural control of a steel jacket platform', Struct. Eng. Mech., 4(2), 125-138 https://doi.org/10.12989/sem.1996.4.2.125
  10. Morison, J.R., O'Brien, M.P., Johnson, J.W. and Schaaf, S.A. (1950), 'The forces exerted by surface waves on piles', AIME Trans, Petroleum Branch, 189, 149-154
  11. Petersen, N.R. (1980), 'Design of large scale tuned mass dampers', In Structural Control, Liepholz HH North-Holland, Amsterdam, 581-598
  12. Soong, T.T. (1990), 'Active structural control', Longman Scientific and Technical
  13. Specht, D.F. (1990), 'Probabilistic neural networks', Neural Networks, 3, 109-118 https://doi.org/10.1016/0893-6080(90)90049-Q
  14. Wang, S.Q., Li, H.J., Ji, C.Y. and Jiao, G.Y. (2002), 'Energy analysis for TMD-structure systems subjected to impact loading', China Ocean Eng., 16(3), 301-310
  15. Yun, C.B., Choi, J.H. and Ryu, J.S. (1985), 'Dynamic analysis of fixed offshore structures subjected to random waves', Korean Soc. Civil Eng., 5(2), 1-9 https://doi.org/10.1080/03601217708907309

Cited by

  1. Design forces for groups of six cylindrical silos by artificial neural network modelling vol.165, pp.10, 2012, https://doi.org/10.1680/stbu.10.00049
  2. Recent advances in vibration control of offshore platforms vol.89, pp.2, 2017, https://doi.org/10.1007/s11071-017-3503-4
  3. Prediction of moments in composite frames considering cracking and time effects using neural network models vol.39, pp.2, 2009, https://doi.org/10.12989/sem.2011.39.2.267
  4. Explicit expressions for inelastic design quantities in composite frames considering effects of nearby columns and floors vol.64, pp.4, 2009, https://doi.org/10.12989/sem.2017.64.4.437
  5. Neural networks for the rapid seismic assessment of existing moment-frame RC buildings vol.67, pp.None, 2009, https://doi.org/10.1016/j.ijdrr.2021.102677