# 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
\[f\left(u\right)\ =\ \frac{2}{1+\exp(-u)}-1 \tag{7}\]  
\[f_{i}\ =\ \frac{1}{\left(cont_{ij}+cont_{jk}\right)cyc} \tag{8}\]  
The dynamic response of tall buildings was studied by [PERSON] and [PERSON] [141, 147] using ANN and the proposed graphs were able to predict the along- and across-wind responses in terms of base shear and base bending moments according to the Indian Wind Code (IWC). Both studies found that the back propagation neural network algorithm was able to satisfactory estimate the dynamic along- and across-wind responses of tall buildings. Similarly, different ML models were applied by [PERSON] and [PERSON] [144] based on DT, KNN regression, RF, and GBRT to predict four types of crosswind vibrations (i.e., over-coupled, coupled, semi-coupled and decoupled) for rectangular cylinders. The data used in training and testing processes were extracted from wind tunnel data. It was found that GBRT can accurately predict crosswind responses which can supplement wind tunnel tests and numerical simulation techniques. One of the input variables used in that study was the Scruton number (Sc). [PERSON] et al. (2019) [140] studied the wind-induced response of tall buildings using CNN and focused on the structural safety evaluation. The trained model predicted the column strains using wind tunnel data such as wind speed and top floor displacements. The architecture of the trained model is composed of the input layer, two convolutional layers, two pooling layers, one fully connected layer, and the output layer. The input map forms the convolutional layer through convolution using the kernel operator. The ML-based model was utilized to overcome the uncertainties in the material, geometric properties and stiffness contribution of nonstructural elements which make it difficult to construct a refined finite element model. [PERSON] et al. [133] used LSTM--originally proposed by [PERSON] and [PERSON] [148]--to predict the nonlinear unsteady bridge aerodynamic responses to overcome the increasing difficulties that exist in the gradient-based learning algorithm in the recurrent neural network (RNN) face. The RNN was developed to introduce the time dimension into the \begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study No.** & **Ref.** & **Surface Type** & **Source of Data** & **Input Variables** & **Output Variables** & **ML Algorithm** \\ \hline
1 & [135] & Bridges & Experimental data from BLWT & _D/B_ & Flutter derivatives (H\({}_{1}\) and A\({}_{2}\)) & ANN \\ \hline
2 & [140] & Tall buildings & Experimental data from BLWT & \(V_{\text{b}}\) and top floor displacements & Column strains & CNN \\ \hline
3 & [141] & Tall buildings & IndianWind Code & \(H\), \(B\), \(L\), _V_\({}_{b}\) and _TC_ & Across wind shear and moment & ANN \\ \hline
4 & [142] & Long span bridge & Full scale data & Cross spectral density & Buffeting response & ANN and SVR \\ \hline
5 & [143] & Box girders & Experimental data from BLWT & Vertex coordinates (\(m_{i}\), \(n_{i}\)) & Flutter wind speed & SVR, ANN, RF and GBRT \\ \hline
6 & [144] & Rectangular cylinders & Previous experimental studies & \(T\), _B/D and Sc_ & Crosswind vibrations & DT-RF-KNN-GBRT \\ \hline
7 & [145] & Cable roofs & Experimental data from BLWT and (FEM) & 11 parameters & Vertical displacements & ANN \\ \hline
8 & [146] & Tall buildings & WERC database-TU & Terrain roughness, aspect ratio and D/B. & Crosswind force spectra & LGBM \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of studies reviewed for aeroelastic response. network structure, and it was found to be capable of predicting a full-time series where nonlinear relation exists between input and output. The study used displacement time series as input variables, and by weighting these time series, both the acceleration and velocity were obtained. The LSTM model was able to calculate the deck vibrations (i.e., lift displacement and torsional angle) under the unsteady nonlinear wind loads.