Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Modeling and assessment of VWNN for signal processing of structural systems

  • Lin, Jeng-Wen (Department of Civil Engineering, Feng Chia University) ;
  • Wu, Tzung-Han (Department of Civil Engineering, Feng Chia University)
  • Received : 2012.06.11
  • Accepted : 2012.11.30
  • Published : 2013.01.10
⟨ Previous Next ⟩

Abstract

This study aimed to develop a model to accurately predict the acceleration of structural systems during an earthquake. The acceleration and applied force of a structure were measured at current time step and the velocity and displacement were estimated through linear integration. These data were used as input to predict the structural acceleration at next time step. The computation tool used was the Volterra/Wiener neural network (VWNN) which contained the mathematical model to predict the acceleration. For alleviating problems of relatively large-dimensional and nonlinear systems, the VWNN model was utilized as the signal processing tool, including the Taylor series components in the input nodes of the neural network. The number of the intermediate layer nodes in the neural network model, containing the training and simulation stage, was evaluated and optimized. Discussions on the influences of the gradient descent with adaptive learning rate algorithm and the Levenberg-Marquardt algorithm, both for determining the network weights, on prediction errors were provided. During the simulation stage, different earthquake excitations were tested with the optimized settings acquired from the training stage to find out which of the algorithms would result in the smallest error, to determine a proper simulation model.

Keywords

Acknowledgement

Supported by : National Science Council, Taiwan

References

  1. Adnani, A., Basri, M., Chaibakhsh, N., Abdul Rahman, M.B. and Salleh, A.B. (2011), "Artificial neural network analysis of lipase-catalyzed synthesis of sugar alcohol ester", Industrial Crops and Products, 33(1), 42-48. https://doi.org/10.1016/j.indcrop.2010.08.006
  2. Barrio, R., Rodríguez, M., Abad, A. and Blesa, F. (2011), "Breaking the limits: the Taylor series method", Applied Mathematics and Computation, 217(20), 7940-7954. https://doi.org/10.1016/j.amc.2011.02.080
  3. Chen, T.C., Han, D.J., Au, F.T.K. and Tham, L.G. (2003), "Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate", International Joint Conference on Neural Networks Proceedings, Portland, Oregon, July, 1873-1878.
  4. De Oliveira, M.A. (2011), "The influence of ARIMA-GARCH parameters in feed forward neural networks prediction", Neural Computing and Applications, 20, 687-701. https://doi.org/10.1007/s00521-010-0410-8
  5. Franz, M.O. and Scholkopf, B. (2006), "A unifying view of wiener and volterra theory and polynomial kernel regression", Neural Computation, 18(12), 3097-3118. https://doi.org/10.1162/neco.2006.18.12.3097
  6. Gao, Z. and Chen, X. (2011), "Structure data processing and damage identification based on wavelet and artificial neural network", Research Journal of Applied Sciences, Engineering and Technology, 3(10), 1203-1208.
  7. Haykin, S.O. (1994), Neural Networks: A Comprehensive Foundation, Prentice-Hall, Englewood Cliffs, NJ.
  8. Kosmatopoulos, E.B., Smyth, A.W., Masri, S.F. and Chassiakos, A.G. (2001), "Robust adaptive neural estimation of restoring forces in nonlinear structures", Journal of Applied Mechanics, 68, 880-893. https://doi.org/10.1115/1.1408614
  9. Lautour, O.R. and Omenzetter, P. (2010), "Damage classification and estimation in experimental structures using time series analysis and pattern recognition", Mechanical Systems and Signal Processing, 24, 1556-1569. https://doi.org/10.1016/j.ymssp.2009.12.008
  10. Lin, J.W. and Chen, H.J. (2009), "Repetitive identification of structural systems using a nonlinear model parameter refinement approach", Shock and Vibration, 16(3), 229-240. https://doi.org/10.1155/2009/174917
  11. Lin, J.W. (2010), "Mode-by-mode evaluation of structural systems using a bandpass-HHT filtering approach", Struct. Eng. Mech., 36(6), 697-714. https://doi.org/10.12989/sem.2010.36.6.697
  12. Lin, J.W. (2011), "A hybrid algorithm based on EEMD and EMD for multi-mode signal processing", Struct. Eng. Mech., 39(6), 813-831. https://doi.org/10.12989/sem.2011.39.6.813
  13. Lynch, J.P. and Loh, K.J. (2006), "A summary review of wireless sensors and sensor networks for structural health monitoring", The Shock and Vibration Digest, 38(2), 91-128. https://doi.org/10.1177/0583102406061499
  14. Masri, S.F., Smyth, A.W., Chassiakos, A.G., Nakamura, M. and Caughey, T.K. (1999), "Training neural networks by adaptive random search techniques", Journal of Engineering Mechanics, 125(2), 123-132. https://doi.org/10.1061/(ASCE)0733-9399(1999)125:2(123)
  15. MathWorks (1994), Document Central: Traingda, Retrieved October 30, 2012, from http://www.mathworks.com/help/nnet/ref/traingda.html
  16. Park, H.S., Lee, H.M. and Adeli, H. (2007), "A new approach for health monitoring of structures: terrestrial laser scanning", Computer-Aided Civil and Infrastructure Engineering, 22, 19-30. https://doi.org/10.1111/j.1467-8667.2006.00466.x
  17. Park, S., Ahmad, S., Yun, C.B. and Roh, Y. (2006), "Multiple crack detection of concrete structures using impedance-based structural health monitoring techniques", Experimental Mechanics, 46, 609-618. https://doi.org/10.1007/s11340-006-8734-0
  18. Pei, J.S., Smyth, A.W. and Kosmatopoulos, E.B. (2004), "Analysis and modification of Volterra/Wiener neural networks for the adaptive identification of non-linear hysteretic dynamic systems", Journal of Sound and Vibration, 275, 693-718. https://doi.org/10.1016/j.jsv.2003.06.005
  19. Peng, C.Y. (2010), "Risk assessment and modeling of debris flow", Master Thesis, Feng Chia University, Taiwan. (In Chinese)
  20. Qian, N. (1999), "On the momentum term in gradient descent learning algorithms", Neural Networks, 12, 145-151. https://doi.org/10.1016/S0893-6080(98)00116-6
  21. Vyas, N.S. and Chatterjee, A. (2011), "Non-parametric identification of rotor-bearing system through Volterra-Wiener theories", IUTAM Symposium on Emerging Trends in Rotor DynamicsIUTAM Bookseries, 1011, 309-320.
  22. Yeh, Y.C. (2003), Applications and Practices of Neural Network Models, Scholars Books, Taiwan. (In Chinese)

Cited by

  1. Assessment of steel components and reinforced concrete structures under steam explosion conditions vol.60, pp.2, 2016, https://doi.org/10.12989/sem.2016.60.2.337
  2. Data-Based Nonlinear Identification and Constitutive Modeling of Hysteresis in NiTiNOL and Steel Strands vol.142, pp.12, 2016, https://doi.org/10.1061/(ASCE)EM.1943-7889.0001170
  3. An evaluation of data-driven identification strategies for complex nonlinear dynamic systems vol.85, pp.2, 2016, https://doi.org/10.1007/s11071-016-2761-x
  4. Experimental modeling and evaluation of the afterglow phosphors using multiple single exponential equations vol.148, 2015, https://doi.org/10.1016/j.neucom.2014.06.064