Share This Article:

Unsupervised Neural Network Approach to Frame Analysis of Conventional Buildings

Abstract Full-Text HTML XML Download Download as PDF (Size:2144KB) PP. 203-211
DOI: 10.4236/ijcns.2014.77022    3,226 Downloads   4,155 Views   Citations

ABSTRACT

In this paper, an Artificial Neural Network (ANN) model is used for the analysis of any type of conventional building frame under an arbitrary loading in terms of the rotational end moments of its members. This is achieved by training the network. The frame will deform so that all joints will rotate an angle. At the same time, a relative lateral sway will be produced at the rth floor level, assuming that the effects of axial lengths of the bars of the structure are not altered. The issue of choosing an appropriate neural network structure and providing structural parameters to that network for training purposes is addressed by using an unsupervised algorithm. The model’s parameters, as well as the rotational variables, are investigated in order to get the most accurate results. The model is then evaluated by using the iteration method of frame analysis developed by Dr. G. Kani. In general, the new approach delivers better results compared to several commonly used methods of structural analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Pinto, L. and Zambrano, A. (2014) Unsupervised Neural Network Approach to Frame Analysis of Conventional Buildings. International Journal of Communications, Network and System Sciences, 7, 203-211. doi: 10.4236/ijcns.2014.77022.

References

[1] Cross, H. (1949) Analysis of Continuous Frames by Distributing Fixed-End Moments. Numerical Methods of Analysis in Engineering. Successive Corrections. Grinter, L.B., Ed., Macmillan, New York.
[2] Kani, G. (1957) Die Berechnung Mehrstockinger, Rahmen, Konrad Wittwer Verlag. Verlag Konrad Wittwer, Stuttgart.
[3] Felippa, C.A. (1995) Parametrized Unification of Matrix Structural Analysis: Classical Formulation and D-Connected Elements. Finite Elements in Analysis and Design, 21, 45-74.
http://dx.doi.org/10.1016/0168-874X(95)00027-8
[4] Duncan, W.J. and Collar, A.R. (1934) A Method for the Solution of Oscillations Problems by Matrices. Philosophical Magazine Series 7, 17, 865.
[5] Argyris, J.H. and Kelsey, S. (1960) Energy Theorems and Structural Analysis. Butterworths, London. http://dx.doi.org/10.1007/978-1-4899-5850-1
[6] Turner. M.J. (1959) The Direct Stiffness Method of Structural Analysis. Structural and Materials Panel Paper, AGARD Meeting, Aachen.
[7] Eymard, R., Gallouet, T. and Herbin, R. (2000) Handbook for Numerical Analysis. Ciarlet, P.G., Lions, J.L., et al., Eds., North Holland, Amsterdam, 715-1022.
[8] Freeman, J.A. and Skapura, D.M. (1992) Neural Networks Algorithms, Applications, and Programming Techniques. Addison-Wesley Publishing Company, Inc., Boston.
[9] Karunanithi, N., Grenney, W.J., Whitly, D. and Bovee, K. (1994) Neural Network for River Flow Prediction. Journal of Computing in Civil Engineering, 215-220.
[10] Nagy, H., Watanabe, K. and Hirano, M. (2002) Prediction of Sediment Load Concentration in Rivers Using Artificial Neural Network Model. Journal of Hydraulic Engineering, 128, 588-595.
http://dx.doi.org/10.1061/(ASCE)0733-9429(2002)128:6(588)
[11] AFCA International (1988) DARPA Neural Network Study. Library of Congress Cataloging, USA, 131-133.
[12] Gupta, T. and Sharma, R.K. (2011) Structural Analysis and Design of Buildings Using Neural Network: A Review. International Journal of Engineering and Management Sciences, 2, 216-220.
[13] Chandwani, V., Agrawal, V. and Nagar, R. (2013) Applications of Soft Computing in Civil Engineering: A Review. International Journal of Computer Applications, 81, 13-20.
[14] Arangio, S. (2013) Neural Network-Based Techniques for Damage Identification of Bridges: A Review of Recent Advances. Civil and Structural Engineering Computational Methods, Chapter 3. Saxe-Coburg Publications, Stirlingshire, 37-60.
[15] Sirca Jr., G.F. and Adeli, H. (2012) System Identification in Structural Engineering. Scientia Iranica, 19, 1355-1364. http://dx.doi.org/10.1016/j.scient.2012.09.002
[16] Salajegheh, E. and Gholizadeh, S. (2005) Optimum Design of Structures by an Improved Genetic Algorithm Using Neural Networks. Advances in Engineering Software, 36, 757-767.
http://dx.doi.org/10.1016/j.advengsoft.2005.03.022

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.