Neural simulation of dynamic response of prefabricated buildings subjected to paraseismic excitations

On the basis of measurements on a group of 5-storey flat buildings experimental data were collected. Back-propagation neural networks (BPNNs) are formulated as replicators for data compression. The compressed values of the discretized displacement response spectrum are associated with paraseismic excitations caused by explosions in nearby quarries. The compressed excitations are completed by buildings parameters and used in the master BPNN for simulation of compressed outputs which correspond to the building displacement response on the 4th floor of buildings. The BPNN replicator is also formulated for the compression of the building displacement records in time domain into the target vectors of the master BPNN. After the training the compressed outputs of the master BPNN are decompressed by means of the BPNN decompressor into the building displacement records. In the paper it was proved that the discussed application of BPNNs gives satisfactory neural simulation of displacement records in time domain for vibrations with large amplitudes without analysis of the building motion equations.