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Machine learning based algorithms for wind pressure prediction of high-rise buildings
https://orcid.org/0000-0001-7387-2115
https://orcid.org/0000-0002-4822-2863
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.
Machine learning based algorithms for wind pressure prediction of high-rise buildings
https://orcid.org/0000-0001-7387-2115
https://orcid.org/0000-0002-4822-2863
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.
Machine learning based algorithms for wind pressure prediction of high-rise buildings
https://orcid.org/0000-0001-7387-2115
https://orcid.org/0000-0002-4822-2863
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.
Machine learning based algorithms for wind pressure prediction of high-rise buildings
Li, Yi (author) / Huang, Xuan (author) / Li, Yong-Gui (author) / Chen, Fu-Bin (author) / Li, Qiu-Sheng (author)
Advances in Structural Engineering ; 25 ; 2222-2233
2022-07-01
12 pages
Article (Journal)
Electronic Resource
English
High-rise Buildings and Wind Effects
| British Library Online Contents | 2007
High-Rise Buildings and Wind Hindrance
| British Library Conference Proceedings | 1995