# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 3 Prior Studies on Applying ML Techniques in Structural Wind Engineering
### Aeroelastic Response Prediction Using ML  
The prediction of aeroelastic responses for buildings and structures by using ML models is also of interest to this review. The input that was used for the prediction of these responses is either CFD simulations (Table 2) or physical testing databases (Table 3). Similar to the previous two sections, Table 3 is meant to provide a summary of the attributes of the key studies reviewed in this section that is concerned with using ML for aeroelastic response prediction. [PERSON] et al. [135] used a BPNN that was built from a limited dataset of already existing dynamic responses of rectangular bridge sections. The results indicated that the ANN prediction scheme performed well in the prediction of dynamic responses. The authors claimed that such an approach may reduce cost and save time by not using extensive wind tunnel testing, especially in the preliminary design. [PERSON] and [PERSON] [131] developed a new approach utilizing ANN with cellular automata (CA) scheme to model the hysteretic behavior of bridge aerodynamic nonlinearities in the time domain. This approach was developed because the ANN is time-consuming until the ideal number of hidden layers and neurons between the input and output are determined. By embedding the CA scheme, which was originally proposed by [136] and later developed by [137] with ANN, the authors of that study aimed to improve the efficiency of the ANN models. The CA scheme is an approach that dynamically evolves in discrete space and time using a local rule belonging to a class of Boolean functions. This scheme is appealing as it could simulate very complicated problems with the simple local rule which is applied to the system consistently in space and time. The activation function used in the ANN training was bipolar sigmoid as shown in Equation (7). The CA scheme is an indirect encoding scheme that is based on the CA representative and could be designed using two cellular systems, i.e., the growing cellular \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Study No.** & **Ref.** & **Surface Type** & **Source of Data** & **Input Variables** & **Output Variables** & **ML Algorithm** \\ \hline
1 & [123] & Flat roof & 12 parameters & \(\widetilde{C_{p}}\) & ANN \\ \hline
2 & [124] & Spherical domes & \
\begin{tabular}{l} span/height ratio, \\ \(II\) and \(\varphi\) \\ \end{tabular} &
\begin{tabular}{l} \(\overline{C_{p}}\) \\ \end{tabular} & ANN \\ \hline
3 & [131] & Box-girder bridge & \
\begin{tabular}{l} Disp., velocities, \\ and accelerations \\ \end{tabular} &
\begin{tabular}{l} Flutter and buffeting \\ responses \\ \end{tabular} & ANN \\ \hline
4 & [132] & Bridges & \
\begin{tabular}{l} Response time \\ histories \\ \end{tabular} &
\begin{tabular}{l} Motion-induced forces \\ \end{tabular} & ANN \\ \hline
5 & [125] & Setback building &
\begin{tabular}{l} \(\overline{C_{p}}\) along the face, drag \\ and lift coefficients \\ \end{tabular} & ANN \\ \hline
6 & [133] & Bridges & \
\begin{tabular}{l} Displacements \\ \end{tabular} &
\begin{tabular}{l} Deck vibrations \\ \end{tabular} & LSTM \\ \hline
7 & [120] & \
\begin{tabular}{l} Circular \\ Cylinders \\ \end{tabular} &
\begin{tabular}{l} Vortex induced \\ vibrations \\ \end{tabular} & DT, RF and GBRT \\ \hline
8 & [127] & Tall buildings & \
\begin{tabular}{l} \(\overline{C_{p}}\) \\ \end{tabular} &
\begin{tabular}{l} LR-QR-RF-DNN \\ \end{tabular} \\ \hline
9 & [134] & Tall building & \
\begin{tabular}{l} Different nodes \\ on the surface \\ \end{tabular} &
\begin{tabular}{l} \(\overline{C_{p}}\) \\ \end{tabular} & RF-GP-LR-KNN-DT-SVR \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of studies reviewed for integrating ML models with CFD simulation. system and the pruning cellular system [138]. The ANN configuration based on the CA scheme was examined using a fitness index that is defined in Equation (8), which is a function of learning cycles and connections of ANN [139].