The paper deals with an application of the principal component analysis and neural networks to computation of fundamental natural periods of prefabricated medium-height buildings. The analysis is based on long-term tests performed on actual structures. The identification problem is formulated as the relation between structural and soil basement parameters, and the fundamenta! period. Back-propagation neural networks are applied to the analysis.
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Decoupled Extended Kalman Filter (DEKF) algorithm was used for the training of Feed-forward Layer Neural Network (FLNN). Data taken from [1] correspond to Displacement Response Spectra (DRS) computed on the base of vibration records measured on the ground level for paraseismic excitation (inputs to FLNN) and on the fourth floor of monitored buildings (outputs of FLNN). It was proved that the application of DEKF gives much more accurate predictions of DRS than standard NN discussed in [1].
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The paper deals with an application of neural networks for evaluation of soil-structure interaction in case of the transmission of ground vibrations from mining tremors to building basement. The problem of is analyzed with respect to typical prefabricated eleven-storey building with load bearing walls. The comparison of maximal values (amplitudes) of vibrations (accelerations) rccorded at the same time on the ground and on the basement level is the way of evaluation of differences between the ground and the basement vibrations.
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