# 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 GMDH-NN is a self-organized system that provides a parametric equation to predict the output and can solve extremely complex problems [103]. This ML algorithm was established using the GMDH shell software [104] and it is based on the principle of termination [104; 105; 106] to find the nonlinear relation between pressure coefficients and the input variables. Termination is the process where the parameters are seeded, reared, hybridized, selected, and rejected to determine the input variables. The study investigated in detail the effect of curvature and corners on pressure distribution and obtained an equation with different variables to predict the mean pressure coefficients. One major difference between ANN and GMDH-NN is that the neurons are filtered simultaneously based on their ability to predict the desired values, and then only those beneficial neurons are fed forward to be trained in the following layer, while the rest are discarded. One other method to predict wind-induced pressures and full dynamic response, i.e., time history on high-rise building surfaces, was proposed by [PERSON] et al. [84] using a backpropagation neural network (BPNN) combined with proper orthogonal decomposition (POD-BPNN). POD was utilized by [PERSON] [107] and later by [PERSON] [108] to deal with wind turbulence-related issues. The advantage of the POD-BPNN method over the ANN is its capability to predict pressure time series for trained data with time parameter t. POD is an approach that is based on a linear combination of a series of orthogonal load modes, where the spatial distributed multivariable random loads can be reconstructed through it and loading principle coordinates [109]. The orthogonal load modes are space-related and time-independent, while the loading principal coordinates are time-varying and space-independent. Before applying the BPNN, the wind loads were decomposed using POD where the interdependent variables are transformed into a weighted superposition of several independent variables. More details about the POD background theory can be found in the literature [110; 111; 112]. The training algorithm applied in that study was the improved global Levenberg-Marquardt algorithm, which can achieve a faster convergence speed [113; 114]. A similar study by [PERSON] et al. [87] investigated the wind pressure-time history using both gaussian process regression (GPR) and BPNN on a low-rise building with a flat roof. The study concluded that GPR has high accuracy for time history interpolation and extrapolation. The wind pressure time series and power spectra were again recently simulated and interpolated on tall buildings by [PERSON] et al. [91] using three ML methods: BPNN, genetic algorithm (GANN), and wavelet neural network (WNN). The WNN produced the most accurate results within the three methods. The WNN combines the advantages of ANN with wavelet transformation, which has time-frequency localization property and focal features which are different from neural networks that have self-adaptive, fault tolerance,robustness and strong inference ability [115]. The reviewed literature showed that the developed BPNN models could generalize the complex, multivariate nonlinear functional relationships among different variables such as wind-induced pressures and locations of pressure taps. Predicting pressure time series at different roof locations was achieved using ANN and the robustness of the models was able to overcome the problems associated with linear interpolation for low-resolution data. A recent study [92] developed an ML model to predict the wind-induced mean and peak pressure for non-isolated buildings, considering the interference effect of neighboring structures using GBDT combined with the grid search algorithm (GSA). The study used wind tunnel data from TPU for non-isolated buildings. The data were split by a ratio of 9:1, where 90% of the dataset was used for training and 10% of the dataset was used for testing.