# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## 3 Prior Studies on Applying ML Techniques in Structural Wind Engineering
### Integration of CFD with Machine Learning  
Several studies integrated CFD simulations with ML techniques to predict either the wind force exerted on bluff bodies or the aeroelastic response of bridges and other flexible structures [117, 118, 119, 120, 121, 122]. [PERSON] et al. [123] predicted the peak pressure coefficients on a low-rise building using 12 output data types from a CFD model such as mean pressure coefficient, dynamic pressure, wind speed, etc. as input variables in the ANN model. The predicted peak pressures were in good agreement with the wind tunnel data. Similarly, [PERSON] et al. [124] used ANN to predict the pressure coefficient on domes of different span to height ratios. The data were generated from the CFD model by developing a dome and a wind flow through the model. The predicted mean pressure coefficients were used for training the ML model with a maximum number of epochs of 50,000 to achieve the specified error tolerance. There were three main inputs: the span/height ratio, the angle measured vertically with respect to the vertical axis of the dome to the ring beam, and the angle measured horizontally with respect to wind direction. The study used neuroscience software in the model training and testing, and it was found that the BPNN predicted the mean pressure coefficients accurately through different locations along the dome.  
[PERSON] and [PERSON] [125] investigated the effect of a setback in tall buildings by predicting pressure coefficients along the building's face. The study used ANN and Fast Fourier Transform (FFT) to validate the wind-induced pressure on different setback buildings predicted by CFD simulation models. The predicted wind pressures were validated before using similar experimental data. The study showed that CFD was capable to predict similar pressure coefficients to experimental data and showed that ANN was capable to predict and validate these pressure coefficients. The Levenberg-Marquardt algorithm was used as the training function, starting with 500 training epochs which were increased until the correlation coefficient exceeded the 99 th percentile. The model was trained using MATLAB neural network toolbox [126].  
A recent study [127] proposed a multi-fidelity ML approach to predict wind loads on tall buildings by integrating CFD models with ML models. The study combined data from a large number of wind directions using the computationally efficient Reynolds-averaged Navier-Stokes (RANS) model with a smaller number of wind directions using the more computationally intense Large Eddy Simulation (LES) method to predict the RMS pressure coefficients on a tall building. The study utilized four types of ML models: linear regression, quadratic regression, RF, and DNN, with the latter being the most accurate. In addition, a bootstrap algorithm was used to generate an ensemble of ML models with accurate confidence intervals. This study used the Adam optimization algorithm [128] and Rectified Linear Unit (ReLU) activation function [129; 130] with a learning rate of 0.001 and regularization strength of 0.01 to avoid overfitting. That was contrary to other studies that used the Levenberg-Marquardt algorithm and tangent sigmoid or logarithmic sigmoid activation functions and this is because the other studies used the ANN method of two or less hidden layers, while the latter study used a DNN with three hidden layers.  
To conclude this section, a summary of the attributes of the reviewed previous studies that integrate ML applications with CFD is provided in Table 2.