Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network
Abstract
:1. Introduction
2. Related Works
3. Materials and Methods
3.1. Wind Tunnel Test and Wind Pressure Data
3.2. Intelligent Data Prediction Model
4. Construction of Wind-Induced Pressure Prediction Model
4.1. GAIN
Missing Data Imputation Using GAIN
Algorithms 1 GAIN for data imputation. |
1. While training loss has not converged do 2. Discriminator (D) 3. Get samples from the dataset 4. Get independent and identically distributed samples of Z 5. Get independent and identically distributed samples of B 6. For j = 1… do 7. 8. 9. 10. End for 11. Update D using adaptive moment estimation optimization (Adam) using the loss obtained from the loss function of D 12. 13. Generator (G) 14. Draw samples from the dataset 15. Draw independent and identically distributed samples of Z 16. Draw independent and identically distributed samples of B 17. For j = 1… do 18. 19. End for 20. Update G using Adam (for fixed D) based on the loss obtained from the loss function of G 21. 22. End while |
4.2. MICE
4.3. KNN
5. Performance Discussions
5.1. Experimental Results of GAIN
5.2. Experimental Comparisons
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Technique | Learning Scenarios | Functionality | Pros | Cons |
---|---|---|---|---|
ANN/MLP | Supervised, unsupervised, reinforcement | Modeling data with simple correlations | Naïve structure, easy to build | Slow convergence rate, high complexity, and not suitable for heavy applications |
BPNN | Supervised, unsupervised | Modeling the learning derivatives | Fast and simple, efficient for a clean dataset | Sensitive to noisy data, difficult to fix the learning rate |
CNN | Supervised, unsupervised, reinforcement | Spatial data modeling | Weight sharing, customizable layer stack arrangement | High computational cost, difficult to optimize the hyperparameters |
RCNN | Supervised, unsupervised, reinforcement | Sequential data modeling | Good in capturing the temporal dependencies | Heavily complex model, stuck with vanishing gradient, exploding problems occurs on complex data |
ARN | Supervised, unsupervised | Modeling time series and interpretable model | Operates on variety of data and various conditions | Generating variable length output is difficult |
Autoencoder | Unsupervised | Dimensionality reduction, compression | Very effective in computation, powerful for unsupervised learning | Pretraining is expensive Stuck with performance for timeseries data |
DNN–LSTM | Supervised, unsupervised, reinforcement | Control problems with high dimensional inputs | Fully connected layer arrangement, can overcome vanishing gradient problem. | Depends on large amount of data, very expensive in computation |
XG-Boost | Supervised, unsupervised | Modelling less feature engineering applications | Fast in operations, less overfitting | Difficult to optimize the hyperparameters |
Randomforest | Supervised, unsupervised | Modelling applications for feature selection | Very effective in highly correlated features | Depend on highly correlated features |
KNN | Supervised, unsupervised | Modelling instance-based applications | Easy implementation, evolving model for new data points | Depends on homogeneous features |
MICE | Supervised, unsupervised | Data imputations | Flexible, can handle variables of varying types | Sensitive to outliers, depends on homogeneous features |
GAIN | Supervised, unsupervised | Data generations | Effective in generating the similar patterns | Convergence is difficult |
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Kim, B.; Yuvaraj, N.; Sri Preethaa, K.R.; Hu, G.; Lee, D.-E. Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network. Sensors 2021, 21, 2515. https://doi.org/10.3390/s21072515
Kim B, Yuvaraj N, Sri Preethaa KR, Hu G, Lee D-E. Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network. Sensors. 2021; 21(7):2515. https://doi.org/10.3390/s21072515
Chicago/Turabian StyleKim, Bubryur, N. Yuvaraj, K. R. Sri Preethaa, Gang Hu, and Dong-Eun Lee. 2021. "Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network" Sensors 21, no. 7: 2515. https://doi.org/10.3390/s21072515