DOI QR코드

DOI QR Code

Load-deflection analysis prediction of CFRP strengthened RC slab using RNN

  • Razavi, S.V. (Jundi-Shapur University of Technology) ;
  • Jumaat, Mohad Zamin (Civil Engineering Department, University Malaya(UM)) ;
  • El-Shafie, Ahmed H. (Civil Engineering Department, Universiti Kebangsaan Malaysia(UKM)) ;
  • Ronagh, Hamid Reza (School of Civil Engineering, The University of Queensland)
  • 투고 : 2014.07.03
  • 심사 : 2016.06.26
  • 발행 : 2015.06.25

초록

In this paper, the load-deflection analysis of the Carbon Fiber Reinforced Polymer (CFRP) strengthened Reinforced Concrete (RC) slab using Recurrent Neural Network (RNN) is investigated. Six reinforced concrete slabs having dimension $1800{\times}400{\times}120mm$ with similar steel bar of 2T10 and strengthened using different length and width of CFRP were tested and compared with similar samples without CFRP. The experimental load-deflection results were normalized and then uploaded in MATLAB software. Loading, CFRP length and width were as neurons in input layer and mid-span deflection was as neuron in output layer. The network was generated using feed-forward network and a internal nonlinear condition space model to memorize the input data while training process. From 122 load-deflection data, 111 data utilized for network generation and 11 data for the network testing. The results of model on the testing stage showed that the generated RNN predicted the load-deflection analysis of the slabs in acceptable technique with a correlation of determination of 0.99. The ratio between predicted deflection by RNN and experimental output was in the range of 0.99 to 1.11.

키워드

참고문헌

  1. Abed, M.M., El-Shafie, A. and Osman, S.A. (2010), "Creep predicting model in masonry structure utilizing dynamic neural network", J. Comput. Sci. Techol., 6(5), 597-605. https://doi.org/10.3844/jcssp.2010.597.605
  2. Barai, S.V. and Pandey, P.C. (1996), "Time-delay neural networks in damage detection of railway bridges", Adv. Eng. Softw., 28(1), 1-10. https://doi.org/10.1016/S0965-9978(96)00038-5
  3. Cevik, A. and Guzelbey,I.H. (2008), "Neural network modeling of strength enhancement for CFRP confined concrete cylinders", Build. Environ., 43(5), 751-763. https://doi.org/10.1016/j.buildenv.2007.01.036
  4. El-Shafie, A., Noureldin, A., Taha, M.R., Aini, H. and Basri, H. (2008), "Performance enhancement for masonry creep predicting model using recurrent neural networks", Eng. Intell. Syst. Elec., 3, 199-208.
  5. Freitag, S., Graf, W., Kaliske, M. and Sickert, J.U. (2011), "Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data", Comput. Struct., 89(21-22), 1971-1981. https://doi.org/10.1016/j.compstruc.2011.05.013
  6. Graf, W., Freitag, S., Kaliske, M. and Sickert, J.U. (2010), "Recurrent neural networks for uncertain timedependent structural", Comput. Aided. Civil. Infra. Eng., 25(5), 322-323 https://doi.org/10.1111/j.1467-8667.2009.00645.x
  7. Hegazt (1996), "Predicting load-deflection behavior of concrete slab using neural networks", Proceedings of the 3rd Canadian Conference on Computing in Civil and Building Engineering.
  8. Hinton, G.E. (1992), "Hour neural networks learn from experience", Sci. Am., 267(3), 145-151.
  9. Ishak, S., Kotha, P. and Alecsandru, C. (2003), "Optimization of dynamic neural networks performance for short-term traffic prediction", Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington DC.
  10. ISIS Canada (2001), Design manuals parts 1 - 4, intelligent sensing of innovative structures, University of Manitoba, Winnipeg.
  11. Jamal, A.A., Elsanosi, A. and Abdelwahab, A. (2007), "Modeling and simulation of shear resistance of R/C beams using artificial neural network", J. Franklin. I., 334(5), 741-756.
  12. Lingras, P. and Mountford, P. (2001), "Time delay neural networks designed using genetic algorithms for short term inter-city traffic forecasting", Eng. Intell. Syst. Elec., 2070, 290-299. https://doi.org/10.1007/3-540-45517-5_33
  13. Medsker, L.R. and Jain, L.C. (2002), "Recurrent neural networks design and applications", Washington D.C., CRC Press 2001
  14. Mehmet, I. (2007), "Modeling ultimate deformation capacity of RC columns using artificial neural networks", Eng. Struct., 29(3), 329-335. https://doi.org/10.1016/j.engstruct.2006.05.001
  15. Nelles, O. (2001), Nonlinear System Identification, Springer, Germany.
  16. White, H. (1989). "Leatnmg in atnhcial neural networks: A statisrical petspecrive", Neural. Comput., 1, 425-464. https://doi.org/10.1162/neco.1989.1.4.425
  17. Wium, J.A. and Eigeaar, E.M. (2010), "An evaluation of the prediction of flat slab deflections", Proceedings of the 34th International Symposium On Bridge And Structural Engineering, Venice.
  18. Yasdi, R. (1999), "Prediction of road traffic using a neural network approach", Neural. Comput. Appl., 8(2), 135-142. https://doi.org/10.1007/s005210050015
  19. Yun, S.Y., Namkoong, S., Rho, J.H., Shin, S.W. and Choi, J.U. (1998), "A performance evaluation of neural network models in traffic volume forecasting", Math. Comput. Model., 27(9-11), 293-310. https://doi.org/10.1016/S0895-7177(98)00065-X

피인용 문헌

  1. Tests of concrete slabs reinforced with CFRP prestressed prisms vol.18, pp.3, 2016, https://doi.org/10.12989/cac.2016.18.3.355
  2. Shear strengthening of deficient concrete beams with marine grade aluminium alloy plates vol.7, pp.4, 2015, https://doi.org/10.12989/acc.2019.7.4.249
  3. Repair, retrofitting and rehabilitation techniques for strengthening of reinforced concrete beams - A review vol.8, pp.2, 2015, https://doi.org/10.12989/acc.2019.8.2.101