# 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  
Wind-induced pressure prediction forms an essential area in structural wind engineering. In addition to field studies, different tools can be used for estimating wind loads and pressure coefficients on surfaces, such as atmospheric boundary layer wind tunnels (ABLWT) or CFD simulations. Both ABLWT and CFD are commonly used but in some cases may require significant time, cost and expertise [79]. As in other fields of civil engineering, studies using ML techniques have gained some momentum and wind engineers have shown interest in identifying a reliable approach to predict wind speeds and or wind-induced pressures for common wind-related structural applications. A summary of the key attributes and ML implementation in the studies that were included in the review related to the first category, i.e., the prediction of wind-induced pressures and time series from experimental testing or databases, is first provided in Table 1, then each study is discussed in more details in this section. The input variables used in each study are significant to the desired output needed from training the ML model. It depends mainly on the architecture of the model and the different inclusive parameters for each dataset. For predicting surface pressure it may depend mainly on either the coordinates of the pressure taps, or slope of the roof, wind direction, or building height. While for the aeroelastic responses of bridges, the input variables mainly depend on parameters such as displacement, velocity and acceleration response for the bridges. One of the studies used the dimension between the buildings as input variables (Sx, Sy) to predict the interference effect on surface pressure. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Study** & **Ref.** & **Surface Type** & **Source of Data** & **Input Variables** & **Output Variables** & **ML** \\
**No.** & & & & & & **Algorithm** \\ \hline
1 & [80] & Flat roof & Experimental data from BLWT & Sampling time series & Pressure time series & ANN \\ \hline
2 & [81] & Gable roof & Experimental data from BLWT & \(x,y,z\), and (\(\theta\)) & \(\overline{Cp}\) and \(\widetilde{C}_{p}\) & ANN \\ \hline
3 & [82] & Tall buildings & Previous experimental studies & \(S_{x}\), \(S_{y}\) and h & Interference effect & RBFNN \\ \hline
4 & [53] & Flat roof & Experimental data from BLWT & \(x,y,z\), and (\(\theta\)) & \(\overline{Cp}\), \(\widetilde{C}_{p}\) and power spectra of fluctuating wind pressures & ANN \\ \hline
5 & [83] & Gable roof & Experimental data from BLWT & \(x,y,z\), (\(\theta\)), and (\(\beta\)) & \(\overline{Cp}\) and \(\widetilde{C}_{p}\) & ANN \\ \hline
6 & [84] & High-rise building & Experimental data from BLWT & \(x,y,z\) and sampling time series & \(\overline{Cp}\), \(\widetilde{C}_{p}\) and pressure time series & POD-ANN \\ \hline
7 & [85] & Flat, gable and hip roofs and walls & NIST database, and TPU database & \(D/B_{s}\) (\(\theta\)) and (\(\beta\)) & \(\overline{C}_{p}\) & ANN \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of studies reviewed for wind-induced predictions. Figure 4: Number of published ML-related studies with wind engineering applications. Many methods can be used for predicting and interpolating multivariate modeling problems, such as linear interpolation and regression polynomials. However, linear interpolation cannot solve nonlinear problems and regression polynomials are common to obtain empirical equations, but these empirical equations lack the generality to be used with other data and a large number of variables [81]. Therefore, ML models generally and ANN particularly have the advantages over the latter methods in complex problems. Most of the studies have adopted the three-stage evaluation process of training, testing and validation (TTV), which was proposed by [93] to build a robust ML model.