APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS

Authors

1 Department of Civil Engineering, University of Shahrekord, Shahrekord, Iran

2 Ph.D. Student, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Lavizan, Iran

3 M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran

Abstract

The general goal of this paper is to determine natural frequency of a plate by artificial neural network with various supporting conditions. One of the most famous training of neural network is back propagation algorithm. This algorithm is a systematic method for training multi-layer artificial neural network. Back propagation algorithm is based on gradient descant which means that it moves downward on the error declination and regulates the weights for the minimum error. In this research, the real frequency is calculated using ANSYS program and is defined as a goal function for neural network so that all outputs of the network can be compared to this function and the error can be calculated. Then using a set of inputs including dimensions or specifications of plate, a neural network is made. After the determination of algorithm and quantities of the network, the phases of training and testing of the results are carried out and the output of the network is created. It is concluded that the results show the performance of the neural network and that the time of frequency calculation is considerably reduced.

Keywords

References

 
[1] Taylor, JG., Mannion, CLT. (1992). “Theory and Application of Neural Networks”, Springer-Verlag, New York,.
[2] Freeman, JA. (1994) “Simulating Neural Networks”, Addison-Wesley Publishing Company, Inc., New York.
[3] Fausett, L. (1994). “Fundamentals of Neural Networks”, Prentice Hall Company, New York.
[4] Salajegheh, E., Heidari, A. (2004). “Approximate dynamic analysis of structures against earthquake by discrete wavelet neural network”, The Third International Conference on advances in Structural Engineering and Mechanics (ICSCS’04), 2-4 September, Ed. Chang-Koon Choi, Seoul, Korea, pp. 775-788, (Paper on CD).
[5] Heidari, A., Salajegheh, E. (2006). “Time history analysis of structures for earthquake loading by wavelet networks”, Asian Journal of Structural Engineering, Vol. 7, No. 2, pp. 155-168.
[6] Heidari, A., Salajegheh, E. (2007). “Approximate Dynamic Analysis of Structures for Earthquake Loading using FWT”, International Journal of Engineering (IJE), I.R.I., Vol. 20, No. 2, pp. 1-11.
[7] Gholizadeh, S., Sheidaii, M.R., Farajzadeh, M.R. (2012). “Seismic design of double layer grids by neural networks”, International Journal of Optimization in Civil Engineering, Vol. 2, pp. 29-45.
[8] Heidari, A. (2011). “Calculation of Frequency of Retaining Wall by Back Propagation Neural Network”, Asian Journal of Civil Engineering, Vol. 12, No. 3, pp. 267-278.
[9] Lagaros, N. D., Papadrakakis, M. (2012). “Neural network based prediction schemes of the non-linear seismic response of 3D buildings”, Advances in Engineering Software, Vol. 44, No. 1, pp. 92-115.
[10] Naderpour, H., Kheyroddin, A., GhodratiAmiri, G. (2010). Prediction of FRP-Confined Compressive Strength of Concrete Using Artificial Neural Networks, Composite Structures (Elsevier), Vol. 92, pp. 2817–2829.
]۱۱[ حسن‌آبادی، م.، حداد، ع.، نادرپور، ح. (۱۳۹۰)، استفاده از شبکه‌های عصبی مصنوعی در تخمین ظرفیت باربری شالوده‌های سطحی واقع بر بستر‌های چند لایه چسبنده، مجله مدل‌سازی در مهندسی، دانشگاه سمنان، سال ۹، شماره ۲۴.
]۱۲[ مرتضایی، ع.، خیرالدین، ع. (۱۳۹۱)، مدل‌سازی و تخمین طول مفصل پلاستیک ستون‌های بتن‌آرمه به کمک شبکه‌های عصبی مصنوعی، مجله مدل‌سازی در مهندسی، دانشگاه سمنان، سال ۱۰، شماره ۲۹.
[13] Hagan, M.T., Demuth, H. B., Beale, M.H. (1996). Neural Network Design, Boston, MA: PWS Publishing,.
[14] Kohonen, T. (1997).  Self-Organizing Maps, Second Edition, Berlin: Springer-Verlag.
[15] Medsker, L.R., Jain, L.C. (2000). Recurrent neural networks: design and applications, Boca Raton, FL: CRC Press.
]۱۶[ معاونی، س. راهنمای سریع ویژوالANSYS، تهران، انتشارات ناقوس، 1381.
[17] Demuth, H., Beale, M. (2006). “Neural Network Toolbox Users Guide: for Use with MATLAB”, The Math. Works.
[18] Wasserman, PD. (1989) “Neural Computing: Theory and Practice”, Van Nostrand Reinhold, New York.
Journal of Modeling in Engineering
Volume 11, Issue 35
December 2013
Pages 49-62

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History

  • Receive Date: 28 January 2017
  • Revise Date: 25 September 2017
  • Accept Date: 28 January 2017

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