Predicting roof-surface wind pressure induced by conical vortex using a BP neural network combined with POD

This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network (BPNN) combined with proper orthogonal decomposition (POD), in which a 1:150 scaled model with a large-span retractable roof was tested in wind tunnel under both laminar and turbulent flow conditions. The distributions of mean and fluctuating wind pressure coefficients were first described, and the effects of inflow turbulence, wind direction, roof opening were examined separately. For the prediction of wind pressure, the POD-BPNN model was trained using measurement data from adjacent points. The prediction results are overall satisfactory. The root-mean-square-error (RMSE) between test and predicted data lies mostly within 10%. In particular, the prediction of mean wind pressure is found to be better than that of fluctuating wind pressure. The outcomes in this study highlight that the proposed POD-BPNN model can be well used as a useful tool to predict surface wind pressure.