Computers and Concrete

  • 제13권6호
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  • Pages.765-780
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  • 2014
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Application of artificial neural networks for dynamic analysis of building frames

  • Joshi, Shardul G. (Department of Civil Engineering, Vishwakarma Institute of Information Technology) ;
  • Londhe, Shreenivas N. (Department of Civil Engineering, Vishwakarma Institute of Information Technology) ;
  • Kwatra, Naveen (Department of Civil Engineering, Thapar University)
  • 투고 : 2012.08.05
  • 심사 : 2014.05.12
  • 발행 : 2014.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Many building codes use the empirical equation to determine fundamental period of vibration where in effect of length, width and the stiffness of the building is not explicitly accounted for. In the present study, ANN models are developed in three categories, varying the number of input parameters in each category. Input parameters are chosen to represent mass, stiffness and geometry of the buildings indirectly. Total numbers of 206 buildings are analyzed out of which, data set of 142 buildings is used to develop these models. It is demonstrated through developed ANN models that geometry of the building and the sizes of the columns are significant parameters in the dynamic analysis of building frames. The testing dataset of these three models is used to obtain the empirical relationship between the height of the building and fundamental period of vibration and compared with the similar equations proposed by other researchers. Experiments are conducted on Mild Steel frames using uniaxial shake table. It is seen that the values obtained through the ANN models are close to the experimental values. The validity of ANN technique is verified by experimental values.

키워드

참고문헌

  1. Adeli, H. (2001), "Neural networks in civil engineering: 1989-2000", Comput. Aided Civil Infrastr. Eng., 16, 126-142. https://doi.org/10.1111/0885-9507.00219
  2. Ahamadi, N., Moghadas, R.K. and Lavaei, A. (2008), "Dynamic analysis of structures using neural network", Am. J. Appl. Sci., 5(9), 1251-1256. https://doi.org/10.3844/ajassp.2008.1251.1256
  3. Alireza, M. and Kimia, M. (2012), "Neural network evaluation of FRP strengthened RC Buildings subjected to near-fault ground motions having fling step", World Academy of Science, Eng. Technol., 62, 58-62.
  4. Annan, C.D, Youssef, M.A. and Naggar, M.H.EL. (2009), "Seismic vulnerability assessment of modular steel buildings", J. Earthq. Eng., 13(8), 1065-1088. https://doi.org/10.1080/13632460902933881
  5. Arslan, M.H. (2009), "Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings", Nat. Hazards Earth Syst. Sci., 9, 967-977. https://doi.org/10.5194/nhess-9-967-2009
  6. Caglar, N., Elmas, M., Dere, Z.Y. and Saribiyik, M. (2008), "Neural Networks in 3- dimensional dynamic analysis of reinforced concrete buildings", Construct. Build. Mater., 22, 788-800. https://doi.org/10.1016/j.conbuildmat.2007.01.029
  7. Chakraverty, S., Marwala, T. and Gupta, P. (2006), "Response predictions of structural system subject to earthquake motions using artificial neural network", Asian J. Civil Eng., (Building and Housing), 7(3), 301-308, Errata in 2006, 7(6), 685-693.
  8. Dawson, C.W. and Wilby, R.L. (2001), "Hydrological modeling using artificial neural networks", Progr. Phys. Geogr., 25(1), 80-108. https://doi.org/10.1177/030913330102500104
  9. Flood, I. and Kartam, N. (1994a), "Neural networks in civil engineering 1: principles and understanding", ASCE J. Comput. Civil Eng., 8(2), 131-148 https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(131)
  10. Flood, I. and Kartam, N. (1994b), "Neural networks in civil engineering II: systems and applications", ASCE J. Comput. Civil Eng., 8(2), 149-162 https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(149)
  11. Ghaboussi, J. and Lin, C.C.J. (1998), "New method of generating spectrum compatible accelerograms using neural networks", Earthq. Eng. Struct. Dyn., 27, 377-396. https://doi.org/10.1002/(SICI)1096-9845(199804)27:4<377::AID-EQE735>3.0.CO;2-2
  12. Gilles, D. and McClure, G. (2008), "Development of a period database for buildings in montreal using ambient vibrations", The 14th World Conference on Earthquake Engineering, October 12-17, Beijing, China.
  13. Goel, R.K. and Chopra, A.K. (1997), "Period formulas for moment resisting frame buildings", J. Struct. Eng., 123(11), November, 1454-1461. https://doi.org/10.1061/(ASCE)0733-9445(1997)123:11(1454)
  14. Heidari, A. and Salajegheh, E. (2006), "Time history analysis of structures for earthquake loading by wavelet networks", Asian J. Civil Eng. (Building & Housing), 7(2), 155-168.
  15. Joghataie, A. and Farrokh, M. (2008), "Dynamic analysis of nonlinear frames by prandtl neural networks", J. Eng. Mech., 134(11), 961-969. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:11(961)
  16. Kameli, I., Miri, M. and Raji, A. (2011), "Prediction of target displacement of reinforced concrete frames using artificial neural networks", Adv. Mater. Res., 255-260, 2345-2349. https://doi.org/10.4028/www.scientific.net/AMR.255-260.2345
  17. Kamyab, M.R. and Gholizadeh, S. (2008), "A new wavelet back propagation neural networks for structural dynamic analysis", Eng. Letters, 16(1), 12-17.
  18. Lagaros, N.D. and Papadrakakis, M. (2012), "Neural network based prediction schemes of the non-linear seismic response of 3D buildings", Adv, Eng, Softw., 44(1), 92-115. https://doi.org/10.1016/j.advengsoft.2011.05.033
  19. Lee, S.C. and Han, S.W. (2002), "Neural-network-based models for generating artificial earthquakes and response spectra", Comput. Struct., 80, 627-638.
  20. Sameh, S.F.M. (2012), "Are theoretically calculated periods of vibration for skeletal structures error-free?', Earthq. Struct., 3(1), 17-35. https://doi.org/10.12989/eas.2012.3.1.017
  21. Verderame, G.M., Iervolino, I. and Manfredi, G. (2010), "Elastic period of sub-standard reinforced concrete moment resisting frame buildings'', Bull. Earthq. Eng., 8, 955-972. https://doi.org/10.1007/s10518-010-9176-8
  22. Crowley, H. and Pinho, R. (2006), "Simplified Equations for Estimating the Period of Vibration of Existing Buildings", First European Conference on Earthquake Engineering and Seismology, Geneva, Switzerland, Paper Number: 1122.
  23. Rogerio, B. and Carlos, T.V. (2000), "Shaking table testing of civil engineering structures-The LNEC 3D simulator experience", 12th World Conference on Earthquake Engineering 2000, Paper number 2129.
  24. Solomatine, D.P. (2002), "Data driven modelling: paradigm, methods, experiences", Proceeding of the 5th International Conference on Hydroinformatics, Cardiff, UK.
  25. IS 1893 (Part 1) (2002), Criteria for Earthquake Resistant Design of Structures-Part 1: General Provisions and Buildings (fifth revision), Bureau of Indian Standards, New Delhi, India.
  26. http: //www.mathworks.com/products/neuralnet

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