## 3 Feature Selection Algorithms
### Empirical Mode Decomposition
#### 4.2.2 Convolutional Neural Networks (CNNs)
proposed a physics-guided CNN architecture for structural response modeling. Structural physics provide a set of constraints for CNN training, which avoid overfitting problems and reduce the size of the training dataset. [PERSON] et al. [98] presented a 1D-CNN network with an electromechanical impedance-based methodology to achieve damage identification under various environmental temperature conditions, proposing a robust structural damage identification scheme against temperature effects. [PERSON] et al. [99] designed a distributed SHM damage identification system using 1D-CNN and a four-step methodology. In the first step, a multiclass classification of the vibration data is performed, and the second step consists of training the CNN with patterns of damaged and undamaged data. The damage location is identified in the third step. The first three steps are performed on each node, and in the final step, a combined data set from all nodes are used to train a global damage identification model. Focusing on joint damage identification, [PERSON] and [PERSON] [100] proposed a joint damage identification method based on using a 1D-CNN with robustness to signal noise. Single and multiple damage cases were successfully classified. Using the modal shapes and curvature differences as input to the CNN, [PERSON] et al. [101] proposed a damage identification system based on CNN, detecting that the modal shapes contain damage-sensitive features, which are sensitive to all degrees of damage, thereby allowing high detection accuracy. Additionally, it was shown that the parameters of the first-order modal signal have helpful information for the localization of structural damage. [PERSON] et al. [102] presented the idea of building an optimal FE model to generate training data for a CNN. A comparative case was analyzed comparing this generated optimal data set against a FE data set, showing that the latter is unreliable for CNN training given model uncertainties. This optimal FE model was obtained by initial structure measurements and, subsequently, the use of the covariance matrix adaptation evolution strategy. [PERSON] et al. [2] evaluated several machine-learning approaches using low-cost accelerometers and noise filtering techniques for structural evaluation after excitation events, including SVM, k-nearest neighbors and CNN. This work demonstrated that the CNN-based detection method in this structure outperformed both SVM and the k-nearest neighbors' method using noise-contaminated raw measurements. In a study by [PERSON] and [PERSON] [103], the Fourier amplitude spectrum was used with a CNN to perform damage state structure identification. This work showed that the use of FAS outperforms traditional CNN proposals using time-history acceleration responses and that the proposed FAS-CNN SHM system was robust against noise effects. [PERSON] et al. [104] proposed a damage identification and location method that used a CNN architecture and was trained by analytical simulations. The presented method consisted of two steps for damage identification: the first to detect the presence of damage as a binary classification problem and the second as a regression problem to estimate the damage location by predicting the damage boundaries in the structure. Using the stiffness histogram of the measured structural response and stiffness degradation as an indicator of damage, [PERSON] et al. [105] proposed a deep learning network to provide a real-time alarm in case of structure compromise. [PERSON] and [PERSON] [106] proposed a 1D-CNN for structural state identification using raw time-domain data. GANs were used to generate training data and improve the robustness and effectiveness of structural damage identification. To avoid the manual selection and elaboration of damage-sensitive features from the raw measurements, [PERSON] et al. [107] proposed a CNN architecture to identify and localize structural damage. The input to the proposed network consisted of frequency-domain features obtained by FFT on the raw measurement signals.