Research Articles | Challenge Journal of Concrete Research Letters

Load-deflection Analysis of CFRP Strengthened RC Slab Using Focused Feed-forward Time Delay Neural Network

Seyedvahid Razavitosee, M. Z. Jumaat, Ahmed H EI-Shafie
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Abstract


In this paper, the load-deflection analysis of the Carbon Fiber Reinforced Polymer (CFRP) strengthened Reinforced Concrete (RC) slab using Focused Feed-forward Time Delay Neural Network (FFTDNN) is investigated. Six reinforced concrete slabs having dimension 1800×400×120 mm 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 uploaded, normalized, and converted to a time sequence parameter in MATLAB software. Loading, time, and the effect of the different CFRP strip lengths on the slab moment of inertia 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 tapped delay line at the input layer 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 FFTDNN predicted the load-deflection analysis of the slabs in acceptable technique with a correlation of determination of 0.98. The ratio between predicted deflection by FFTDNN and experimental output was in the range of 0.92 to 1.23.

Keywords


FFTDNN; rc; CFRP

References


Abed, M. M., El-Shafie, A., and Osman, S.A. (2010). Creep Predicting Model in Masonry Structure Utilizing Dynamic Neural Network Journal of Computer Science 6 (5), 597-605.

Barai, S. V., and Pandey, P.C. (1996). Time-delay neural networks in damage detection of railway bridges. Advances in Engineering Software, 28, 1-10.

Chen, W. F. (1976). Plasticity in Reinforced Concrete. New York: McGraw-Hill.

Dutta, S., and Shekar, S. (1993). Bond rating: a non-conservative application of neural networks. Proceedings of IEEE International Conference on Neural Networks, 11, 567-576

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 Int Syst, 3, 199–208.

Freitag, S., Graf, W., Kaliske, M., and Sickert, J.U. (2011). Prediction of time-dependent structural behavior with recurrent neural networks for fuzzy data. Computers & Structures, 89(21-22): 1971-1981.

Graf, W., Freitag, S., Kaliske, M., and Sickert, J.U. (2010). Recurrent Neural Networks for Uncertain Time-Dependent Structural Behavior. Computer-Aided Civil and Infrastructure Engineering, 25(5): 322-323.

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.

Li, Q. S., Liu, D.K., Fang, J.Q., Jeary, A.P., and Wong, C.K. (1999). Using neural networks to model and predict amplitude dependent damping in building. wind and structures, 2(1), 25-40.

Lingras, P., and Mountford, P. (2001). Time delay neural networks designed using genetic algorithms for short term inter-city traffic forecasting. Engineering of Intelligent Systems, 2070, 290-299.

Medsker, L.R., and Jain, L.C., (2002). Recurrent neural networks design and applications, Washington D.C.: CRC Press 2001

Nelles, O. (2001). Nonlinear system identification. Germany: Springer.

Pan T.Y., Wang R.Y., and Lai J.S. (2007). A deterministic linearized recurrent neural network for recognizing the transition of rainfall-runoff processes. Advances in Water Resources, 30, 1797-1814.

Wium, J. A., and Eigeaar, E.M. (2010). An evaluation of the prediction of flat slab deflections 34th International Symposium on Bridge and Structural Engineering, Venice.

Yasdi, R. (1999). Prediction of road traffic using a neural network approach. Neural computing and applications, 8, 135-142.

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. Mathematic Computing Modelling, 27(9-11), 293-310.


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