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Computers and Concrete
  Volume 13, Number 4, April 2014 , pages 547-567

Optimum seismic design of unbonded post-tensioned precast concrete walls using ANN
Jamal A. Abdalla, Elias I. Saqan and Rami A. Hawileh

    Precast Seismic Structural Systems (PRESSS) provided an iterative procedure for obtaining optimum design of unbonded post-tensioned coupled precast concrete wall systems. Although PRESSS procedure is effective, however, it is lengthy and laborious. The purpose of this research is to employ Artificial Neural Network (ANN) to predict the optimum design parameters for such wall systems while avoiding the demanding iterative process. The developed ANN model is very accurate in predicting the non-dimensional optimum design parameters related to post-tensioning reinforcement area, yield force of shear connectors and ratio of moment resisted by shear connectors to the design moment. The Mean Absolute Percent Error (MAPE) for the test data for these design parameters is around %1 and the correlation coefficient is almost equal to 1.0. The developed ANN model is then used to study the effect of different design parameters on wall behavior. It is observed that the design moment and the concrete strength have the most influence on the wall behavior as compared to other parameters. Several design examples were presented to demonstrate the accuracy and effectiveness of the ANN model.
Key Words
    seismic design; precast concrete wall; unbonded post-tensioned; neural network; PRESSS
Jamal A. Abdalla and Rami A. Hawileh: Department of Civil Engineering, American University of Sharjah, UAE

Elias I. Saqan: Department of Civil Engineering, American University in Dubai, UAE

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