Przeanalizowano różne przykłady ocen szkodliwości drgań na budynki mieszkalne na podstawie pomiarów drgań gruntu spowodowanych wstrząsami górniczymi. Wyrażono pogląd, że do wyznaczania odpowiedzi budynku na drgania wzbudzone wstrząsami górniczymi powinno się przyjmować wymuszenie kinematyczne w postaci przebiegów drgań fundamentów budynków.
EN
Different evaluation examples of detrimental vibration on dwelling houses on the base vibration survey caused by mining shocks have been analyzed. A view expressive is given that the building respond on vibrations inspired by mining vibration the kinematics force in the shape of course vibration of the house foundation should be accepted.
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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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Prospects of neural network applications in analysis of building vibration problems: Backpropagation neural networks are used to the analysis of two problems related to vibrations of prefabricated buildings subjected to seismic-type excitations. The first problem deals with simulation of vibrations of selected floors of a tall building. The second problem is associated with identification of the fundamental period of natural vibrations of both medium-height and tall buildings. Application of a regularization network and fuzzy networks are also discussed.
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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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