Title: Predicting wind pressures around circular cylinders using machine learning techniques,
Abstract: Numerous studies have been carried out to measure wind pressures around
circular cylinders since the early 20th century due to its engineering
significance. Consequently, a large amount of wind pressure data sets have
accumulated, which presents an excellent opportunity for using machine learning
(ML) techniques to train models to predict wind pressures around circular
cylinders. Wind pressures around smooth circular cylinders are a function of
mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident
wind, and circumferential angle of the cylinder. Considering these three
parameters as the inputs, this study trained two ML models to predict mean and
fluctuating pressures respectively. Three machine learning algorithms including
decision tree regressor, random forest, and gradient boosting regression trees
(GBRT) were tested. The GBRT models exhibited the best performance for
predicting both mean and fluctuating pressures, and they are capable of making
accurate predictions for Re ranging from 10^4 to 10^6 and Ti ranging from 0% to
15%. It is believed that the GBRT models provide very efficient and economical
alternative to traditional wind tunnel tests and computational fluid dynamic
simulations for determining wind pressures around smooth circular cylinders
within the studied Re and Ti range.