## 3 Feature Selection Algorithms
### Empirical Mode Decomposition
#### 4.2.3 Support Vector Machines (SVMs)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Publication** & **Structure** & **Excitation Source** & **Signal Processing** & **Type of Kernel** & **Classification Error** & **Dataset Size** & **Purpose** & **Year** \\ \hline
[75] & 3D-reinforced concrete & \begin{tabular}{c} 3D-reinforced concrete \\ moment frame FE model \\ \end{tabular} & Simulation & \
\begin{tabular}{c} Cumulative intensity \\ measures \\ \end{tabular} & GRRF & ACC = 81.1\% & 6240 samples &
\begin{tabular}{c} Propose a structural damage \\ detection and localization STM \\ system using an SVM model \\ \end{tabular} & 2019 \\ \hline
[74] & 3D-reinforced concrete & \
\begin{tabular}{c} 3D-reinforced concrete \\ moment frame FE model \\ \end{tabular} & Simulation & GWN & GRRF & ACC = 92.48\% & 5400 samples &
\begin{tabular}{c} A framework was presented \\ damage prediction, reduction and \\ severity of building damage \\ \end{tabular} & 2018 \\ \hline
[119] & 3D-sharp planar brass FE model & Simulation & LS-SVM & Net specified & \
\begin{tabular}{c} MSE = 0.00051 planner \\ trusts), (RMSE = 0.0212) \\ (double layer grid) \\ \end{tabular} & 11,625 data points &
\begin{tabular}{c} Present a comparative study of \\ the application of several ML \\ techniques for damage \\ detection of structures \\ \end{tabular} & 2018 \\ \hline
[120] & \begin{tabular}{c} Three-story plane steel \\ frame FE model and system experimental \\ model \\ \end{tabular} & Simulation and \begin{tabular}{c} 3D-FG experts \\ model \\ \end{tabular} & LS-SVM & GRRF & \begin{tabular}{c} MSE = 3243 \(\times\) 10\({}^{-7}\) \\ (buildrel), MSE = 7.303 \\ \(\times\) 10\({}^{-9}\) (plane), MSE = 7.303 \\ \(\times\) 10\({}^{-9}\) (plane), MSE = (5-DOF \\ system) \\ \end{tabular} & \
\begin{tabular}{c} 1944 samples \\ (building), 2337 \\ \(\times\) 10\({}^{-9}\) (plane), MSE = 7.303 \\ \(\times\) 10\({}^{-9}\) (plane), MSE = (5-DOF \\ system) \\ \end{tabular} &
\begin{tabular}{c} Propose structural damage \\ detection, localization and \\ severity using a LS-SVM \\ approach \\ \end{tabular} & 2018 \\ \hline
[41] & \begin{tabular}{c} Offshore platform FE \\ model and an \\ instrumented scaled \\ model \\ \end{tabular} & Simulation and \begin{tabular}{c} 3D-FN, WPD, PCA \\ \end{tabular} & GRRF & \begin{tabular}{c} ACC = 78.12\% \\ (FE model), \\ ACC = 84.375\% \\ (structured model) \\ \end{tabular} & \
\begin{tabular}{c} 122 samples of \\ (FE model), \\ \end{tabular} &
\begin{tabular}{c} WP and PCA were used as \\ damage-sensitive feature \\ networks to train an SVM \\ model \\ \end{tabular} & 2018 \\ \hline
[121] & \begin{tabular}{c} Three-story frame \\ structure experimental \\ model \\ \end{tabular} & \begin{tabular}{c} Electrdynamic \\ shaker \\ \end{tabular} & \
\begin{tabular}{c} CA, PSO, grid search \\ techniques, RE \\ \end{tabular} & GRRF & ACC = 100\% & 510 samples &
\begin{tabular}{c} Three optimization techniques \\ were used to optimally select \\ SVM hyperparameters using \\ acceleration signals to perform \\ structural damage \\ detection tasks \\ \end{tabular} & 2017 \\ \hline
[122] & \begin{tabular}{c} LASC-ASCE STM \\ benchmark-dome trees \\ FE model \\ \end{tabular} & Simulation & WPRE, HM & Newly proposed & \
\begin{tabular}{c} MAE = 1.02 \(\times\) 10\({}^{-2}\) \\ (benchmark), \\ MAE = 1.02 \(\times\) 10\({}^{-2}\) \\ (demo trees) \\ \end{tabular} &
\begin{tabular}{c} Propose a new SVM kernel in \\ order to increase the speed and \\ the accuracy of LS-SVM for \\ structural damage detection \\ \end{tabular} & 2016 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._