Prediction of wind pressure coefficients on building surfaces using artificial neural networks

•A novel method to predict the mean pressure coefficients on building surfaces is presented.•Data for flat-, gable- and hip-roofed low-rise buildings are processed from TPU database for the case studies.•Based on the experimental data, three artificial neural network models for the case studies are developed.•The method overcomes the accurate than other current ones to include mean Cp data in AFN or BES programs. Knowing the pressure coefficient on building surfaces is important for the evaluation of wind loads and natural ventilation. The main objective of this paper is to present and to validate a computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings. This approach makes use of artificial neural network (ANN) to estimate the surface-average pressure coefficient for each wall and roof according to the building geometry and the wind angle. Three separate ANN models were developed, one for each roof type, and trained using an experimental database. Applied to a wide variety of buildings, the current ANN models were proved to be considerably more accurate than the commonly used parametric equations for the estimation of pressure coefficients. The proposed ANN-based methodology is as general and versatile as to be easily expanded to buildings with different shapes as well as to be coupled to building performance simulation and airflow network programs.