Prediction of wind loads on high-rise building using a BP neural network combined with POD

According to the limited wind tunnel test results to obtain detailed data of surface wind pressure on buildings has important significance for the accurate calculation of cladding wind pressure and wind-induced response of structures. In this paper, a backpropagation neural network (BPNN) combined with proper orthogonal decomposition (POD-BPNN) is proposed for the prediction of the mean, root-mean-square (RMS) pressure coefficients and the time series of wind-induced pressures on a building surface, respectively. In this study, simultaneous pressure measurements are made on a high-rise building model in a boundary layer wind tunnel and parts of the model test data are used as the training input-output sets for BPNN and POD-BPNN models. Comparisons of the prediction results by the POD-BPNN approach and those from the wind tunnel test demonstrate that the BPNN combined with POD method can successfully and efficiently predict the time series of pressure data on all surfaces of a high-rise building on the basis of wind tunnel pressure measurements from a certain number of pressure taps.