Machine learning based algorithms for wind pressure prediction of high-rise buildings

Abstract

In recent years, machine learning (ML) techniques have been used in various fields of engineering practice. In order to evaluate the feasibility of machine learning algorithms for prediction of wind-induced effects on high-rise buildings, four ML algorithms including ridge regression, decision tree, random forest and gradient boosting regression tree are adopted in this study to predict wind pressures on Commonwealth Advisory Aeronautical Research Council standard tall building. The gradient boosting regression tree model is proved to be well performed in predicting both mean wind pressures and fluctuating wind pressures. Compared to expensive wind tunnel tests and time-consuming computational fluid dynamic simulations, it is expected that the gradient boosting regression tree model is an efficient and economical alternative for predicting wind pressures on high-rise buildings.

A Hybrid Prediction Model for Local Resistance Coefficient of Water Transmission Tunnel Maintenance Ventilation Based on Machine Learning

“…Therefore, there are many scholars in the field of fluid mechanics using machine-learning methods to carry out relevant research [13][14][15], using the black box model of machine learning to replace complex physical mechanisms in fluid mechanics, to overcome the shortcomings of numerical simulations and model-testing methods. Li et al [16] used ridge regression, decision tree, random forest, gradient boosting regression tree, and other machine-learning methods to predict the average wind pressure and fluctuating wind pressure of high-rise buildings. Zhu et al [17] conducted research on the surface wind pressure of low-rise buildings, obtained the surface wind pressure under different wind forces by using numerical simulation methods, established a surrogate model based on machine learning, and applied it to optimize the placement of building surface pressure sensors.…”