# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 3 Prior Studies on Applying ML Techniques in Structural Wind Engineering
### Prediction of Wind-Induced Pressure
The cross-validation process comprises two steps: first, the dataset is randomly shuffled and is divided into \(k\) subsets of similar sizes, then \(k-1\) sets are used for training and one set is used as the testing set to assess the performance of the model. The stability and the accuracy of the validation method depend mainly on the \(k\) value. Hence, the cross-validation method is usually referred to as \(k\)-fold cross-validation [19; 94] and is illustrated in Figure 5. Many of the reviewed studies used the 10-fold CV method following [PERSON] et al. 's [95] recommendation of using \(k=10\) as a good estimate. ANN is the most commonly used technique employed in the reviewed studies (see Table 1). A study by [PERSON] et al. [81] predicted the pressure coefficients on a gable roof using ANN. This was one of the most important and early studies for implementing ML models to predict wind-induced pressure on building surfaces. Later, [PERSON] et al. [96] interpolated pressure time series from existing buildings to different roof height buildings, and then successfully extrapolated to other buildings with different dimensions and roof slopes using ANN. [PERSON] and [PERSON] [82] evaluated the interference wind-induced effects, that were expressed by interference factor (IF) among tall buildings using radial basis function neural networks (RBF-NN). The RBF-NN is a feed-forward type neural network, but the activation function is different from those that are commonly used (i.e., tangent sigmoid or a logarithmic sigmoid). The RBF-NN was used first by [50] and it is a function whose response either decreases or increases with the distance from a center point [51; 52]. It was found that the predicted IF values were in very good agreement with the experimental counterparts. The interference index due to shielding between buildings was predicted from experimental data from wind tunnels using neural network models by English [97]. The study found that the neural network model was able to accurately predict the interference index for building configurations that have not been tested experimentally. The interference index can be calculated by subtracting 1 from the shielding (buffeting) factor. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Study No.** & **Ref.** & **Surface Type** & **Source of Data** & **Input Variables** & **Output Variables** & **ML Algorithm** \\ \hline
8 & [86] & Flat roof & Experimental data from BLWT & Terrain turbulence & \(\overline{Cp}\), \(\widetilde{C_{p}}\) and \(\widetilde{C_{p}}\) & ANN \\ \hline
9 & [87] & Flat roof & Experimental data from BLWT & \(x\), \(y\), \(z\), (\(\theta\)) and sampling time series & \(\overline{Cp}\), \(\widetilde{C_{p}}\) and pressure time series & GPR \\ \hline
10 & [66] & Circular cylinders & Previous experimental studies & \(Re\), \(T\) and cylinder circumferential angle & \(\overline{Cp}\) and \(\widetilde{C_{p}}\) & DT, RF, and GBRT \\ \hline
11 & [88] & High-rise building & TPU database & (\(S_{x}\) and \(S_{y}\)) and (\(\theta\)) & \(\overline{Cp}\) and \(\widetilde{C_{p}}\) & DT, RF, GANN, and XGBoost \\ \hline
12 & [89] & C-shaped building & Experimental data from BLWT & \(R\)/D, \(D\)/\(B\), \(d\)/\(b\) and \(D\)/\(H\) & \(\overline{Cp}\) & GMDH-NN \\ \hline
13 & [90] & Gable roof and walls & NIST database and DesignSafe-CI database & \(x\), \(y\), \(z\), and (\(\theta\)) & \(\overline{Cp}\) and \(\widetilde{C_{p}}\) & ANN \\ \hline
14 & [91] & Tall buildings & Experimental data from BLWT & (\(\theta\)) & Time series, power spectra and \(\overline{Cp}\) & ANN-GANN-RNN \\ \hline
15 & [92] & Gable roof & TPU database & \(C_{A}\), (\(\theta\)) & \(\overline{Cp}\), \(\widetilde{C_{p}}\) and \(\widetilde{C_{p}}\) and time series & GBDT \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._[PERSON] et al. [85] predicted the surface-averaged pressure coefficients of low-rise buildings with different types of roofs using ANN.