Prediction of wind loads on a large flat roof using fuzzy neural networks

Fuzzy neural networks (FNN) have capability to develop complex, nonlinear functional relationships between input–output patterns based on limited and sometimes inconsistent data, and are therefore suitable to predict wind loads on buildings on the basis of data obtained from model tests in wind tunnels. In this study, simultaneous pressure measurements are made on a large flat roof model in a boundary layer wind tunnel. An FNN approach is developed for prediction of mean pressure distributions on the roof model, and parts of the wind tunnel test results are used as the training sets for the FNN to recognize the pressure distribution patterns. The procedure is further extended to predict the power spectra and cross-power spectra of fluctuating wind pressures for some typical tap locations in the roof corners and leading edge areas under different wind directions. It is found that the developed FNN approach can generalize functional relationships of wind loads varying with incident wind directions and spatial locations on the roof, and can successfully predict the wind loads on the roof which are not fully covered by the wind tunnel measurements. It is demonstrated from this study that the adoption of the FNN approach can lead to a significant reduction of the pressure measurement programs (e.g., incident wind direction configurations and number of required pressure taps) in wind tunnel tests.