PREDICTION OF THE DAMPING REDUCTION FACTOR BY NEURAL NETWORKS

The damping reduction factor is used in earthquake engineering in order to estimate the response of buildings with high damping ratio from its with damping ratio equal to 5%. This factor has been studied by a number of researchers, and many expressions were given to this factor as function of many parameters, All the formulations found in the literature were based on a linear or nonlinear regression; this regression analysis is conducted to find a formulation for DRF. The aim of the work reported in this paper is to develop a new method to estimate the damping reduction factor (DRF) using the neural networks. In order to measure the quality of the network prediction; a correlation analysis is performed between the network outputs and the corresponding targets DRF. The simulation results are then presented for different values of damping and conclusions are given. This method gives very interesting results compared to the exact results and thos given by the formulation of Lin et al. (2007). This formulation is one of the best formulation that describe the DRF. On the other hand, Lin in his paper gives the detailed database used to its DRF formulation that allows to use the same data base to estimate the DRF with ANN and make the comparison.

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