1. Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network
- Author
-
Bubryur Kim, Dong-Eun Lee, K. R. Sri Preethaa, N. Yuvaraj, and Gang Hu
- Subjects
Computer science ,020209 energy ,data imputation ,wind-pressure coefficients ,020101 civil engineering ,02 engineering and technology ,lcsh:Chemical technology ,Biochemistry ,Standard deviation ,Article ,0201 civil engineering ,Analytical Chemistry ,law.invention ,law ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:TP1-1185 ,Imputation (statistics) ,Electrical and Electronic Engineering ,Instrumentation ,Wind tunnel ,Variance (accounting) ,Pressure sensor ,Atomic and Molecular Physics, and Optics ,Wind engineering ,Pressure measurement ,machine learning ,tall building ,wind load ,generative adversarial imputation network ,Generative grammar - Abstract
Wind tunnel testing techniques are the main research tools for evaluating the wind loadings of buildings. They are significant in designing structurally safe and comfortable buildings. The wind tunnel pressure measurement technique using pressure sensors is significant for assessing the cladding pressures of buildings. However, some pressure sensors usually fail and cause loss of data, which are difficult to restore. In the literature, numerous techniques are implemented for imputing the single instance data values and data imputation for multiple instantaneous time intervals with accurate predictions needs to be addressed. Thus, the data imputation capacity of machine learning models is used to predict the missing wind pressure data for tall buildings in this study. A generative adversarial imputation network (GAIN) is proposed to predict the pressure coefficients at various instantaneous time intervals on tall buildings. The proposed model is validated by comparing the performance of GAIN with that of the K-nearest neighbor and multiple imputations by chained equation models. The experimental results show that the GAIN model provides the best fit, achieving more accurate predictions with the minimum average variance and minimum average standard deviation. The average mean-squared error for all four sides of the building was the minimum (0.016), and the average R-squared error was the maximum (0.961). The proposed model can ensure the health and prolonged existence of a structure based on wind environment.
- Published
- 2021