Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

ANN based prediction of moment coefficients in slabs subjected to patch load

  • Giri, Venkiteela (Department of Civil Engineering, Indian Institute of Technology Roorkee) ;
  • Upadhyay, Akhil (Department of Civil Engineering, Indian Institute of Technology Roorkee)
  • Received : 2006.01.17
  • Accepted : 2006.06.13
  • Published : 2006.11.10
⟨ Previous Next ⟩

Abstract

Keywords

References

  1. Adeli, H. (2001), 'Neural networks in civil engineering: 1989-2000', Computer-Aided Civil and Infrastructure Engineering, 16, 126-142 https://doi.org/10.1111/0885-9507.00219
  2. Amerijckx, C., Verleysen, M., Thissen, P. and Legat, J.D. (1998), 'Image compression by self organized kohonen map', IEEE Trans on Neural Networks, 9(3), 503-507 https://doi.org/10.1109/72.668891
  3. Cevik, M., Ozkaya, E. and Pakdemirli, M. (2002), 'Natural frequencies of suspension bridges: An artificial neural network approach', J Sound Vib., 257(3), 596-604 https://doi.org/10.1006/jsvi.2001.4237
  4. Cheng, Y.I. (1999), 'Design of high-performance concrete mixtures using neural networks and nonlinear programming', J Comput. Civil Eng., ASCE, 13(1), 36-42 https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36)
  5. Demuth, H. and Beale, M. (2003), Manual of Neural Network Tool Box - For Use with MATLAB, MATLAB User's Guide, Version 4, the Math Works Inc
  6. Garrett, J.H. (1994), 'Where and why artificial neural networks are applicable in civil engineering', J Camp. Civil Eng., ASCE, [special issue] 8(2), 29-30
  7. Hornik, K., Stinchcomb, E.M. and White, H. (1989), 'Multilayer feed-forward networks are universal approximators', Neural Netw, 2, 359-366 https://doi.org/10.1016/0893-6080(89)90020-8
  8. Hsu, D.S. and Tsai, C.H. (1997), 'Reinforced concrete structural damage diagnosis by using artificial neural network', IEEE
  9. Jerzy, H. and Krzysztof, S. (2005), 'New technique of nondestructive assessment of concrete strength using artificial intelligence', NDT&E Int., 38, 251-259 https://doi.org/10.1016/j.ndteint.2004.08.002
  10. Johnson, V.D. (1980), Essentials a/Bridge Engineering, 3rd ed., Oxford & IBK Publications Co., New Delhi
  11. Krishna, N.M. and Gangadhran, D. (1999), 'Analysis of infilled frames', J Struct. Eng., ASCE, 26, 173-178
  12. Muhammad Hadi, N.S. (2003), 'Neural networks applications in concrete structures', ACI J Comput. Struct., 81, 373-381 https://doi.org/10.1016/S0045-7949(02)00451-0
  13. Sirca, GF. and Adeli, H. (2004), 'Counter propagation neural network model for steel girder bridge structures', J Bridge Eng., ASCE, 9(1), 55-65 https://doi.org/10.1061/(ASCE)1084-0702(2004)9:1(55)
  14. Taha, M.M.R., Noureldin, A., El-Sheimy, N. and Shrive, N.G (2003), 'Artificial neural networks for predicting creep with an example application to structural masonry', Canadian Journal of Civil Engineering, 30(3), 523-532 https://doi.org/10.1139/l03-003

Cited by

  1. Prediction of moments in composite frames considering cracking and time effects using neural network models vol.39, pp.2, 2006, https://doi.org/10.12989/sem.2011.39.2.267
  2. Neural networks for the rapid seismic assessment of existing moment-frame RC buildings vol.67, pp.None, 2006, https://doi.org/10.1016/j.ijdrr.2021.102677