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Wind and Structures Volume 36, Number 6, June 2023 (Special Issue) pages 355-366 DOI: https://doi.org/10.12989/was.2023.36.6.355 |
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Machine learning-based prediction of wind forces on CAARC standard tall buildings |
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Yi Li, Jie-Ting Yin, Fu-Bin Chen and Qiu-Sheng Li
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| Abstract | ||
| Although machine learning (ML) techniques have been widely used in various fields of engineering practice, their applications in the field of wind engineering are still at the initial stage. In order to evaluate the feasibility of machine learning algorithms for prediction of wind loads on high-rise buildings, this study took the exposure category type, wind direction and the height of local wind force as the input features and adopted four different machine learning algorithms including k-nearest neighbor (KNN), support vector machine (SVM), gradient boosting regression tree (GBRT) and extreme gradient (XG) boosting to predict wind force coefficients of CAARC standard tall building model. All the hyper-parameters of four ML algorithms are optimized by tree-structured Parzen estimator (TPE). The result shows that mean drag force coefficients and RMS lift force coefficients can be well predicted by the GBRT algorithm model while the RMS drag force coefficients can be forecasted preferably by the XG boosting algorithm model. The proposed machine learning based algorithms for wind loads prediction can be an alternative of traditional wind tunnel tests and computational fluid dynamic simulations. | ||
| Key Words | ||
| high-rise building; hyper-parameters optimization; machine learning; predicting; wind load | ||
| Address | ||
| Yi Li:1)School of Civil Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China 2)School of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China Jie-Ting Yin:School of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China Fu-Bin Chen:1)School of Civil Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan, China 2)Key Laboratory of Bridge Engineering Safety Control by Department of Education, Changsha University of Science and Technology, Changsha, 410114, Hunan, China Qiu-Sheng Li:Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong | ||