## 4 Damage Detection Models Using Ultrasonic Guided Waves for SHM in
By training models with datasets containing known defects and normal conditions, these algorithms classify new data with high precision, facilitating the early detection of potential issues. Deep learning methods, such as Convolutional Neural Networks, automate feature extraction and enhance the robustness of SHM systems [121, 122]. CNNs, due to their hierarchical structure, can also automatically extract pertinent features from raw ultrasonic data, thus improving damage detection and localization accuracy [123]. Similarly, Support Vector Machines (SVMs) excel in classification tasks, especially with high-dimensional data. In ultrasonic NDT, SVMs classify different damage states using features from ultrasonic signals, maintaining robust performance even with limited training data [124]. These techniques aid in identifying previously unknown damage types and facilitate exploratory data analysis [125]. [PERSON] et al. developed a framework for a machine-learning-based model for damage localization in composites using a damage diagnosis technique that integrates guided wave-based SHM with Gramian Angular Field (GAF) and CNNs, as represented in Figure 9.  
These technologies facilitate the deployment of distributed sensor networks capable of autonomous communication and data processing, thereby enhancing the scalability and efficiency of SHM systems. Table 4 presents the classifications for popular damage detection models and techniques used for SHM in composite materials. \begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Model/Technique** & **Description** & **Application** \\ \hline \multirow{2}{*}{Data-Driven Models} & \multirow{2}{*}{Artificial Neural Networks} & Used for defect classification and damage localization in composites. & Impact localization in laminates [126] \\ \hline \multirow{2}{*}{Data-Driven Models} & Deep Neural Networks (DNNs) & Predict progressive damage and impact severity. & Progressive damage monitoring [127] \\ \hline \multirow{2}{*}{Data-Driven Models} & Convolutional Neural Networks (CNNs) & Process image data for crack detection and classification. & Crack detection in images and ultrasonic imaging data [128] \\ \hline \multirow{2}{*}{Data-Driven Models} & \multirow{2}{*}{SVMs} & Classify types of damage using extracted features. & Crack classification and damage size estimation [129] \\ \hline \multirow{2}{*}{Data-Driven Models} & Principal Component Analysis (PCA) & Reduces data dimensionality while preserving variance. & Damage clustering and anomaly detection in composite panels [130] \\ \hline \multirow{2}{*}{Data-Driven Models} & Long Short-Term Memory (LSTM) & Analyzes sequential ultrasonic wave data for damage progression over time. & Structural fatigue monitoring in aerospace [131] \\ \hline \multirow{2}{*}{Physics-Based Models} & \multirow{2}{*}{Time of flight (ToF)} & Pinpoints damage based on wave travel time. & Delamination detection in aerospace composites [64] \\ \hline \multirow{2}{*}{Physics-Based Models} & \multirow{2}{*}{FEMs} & Simulate ultrasonic wave propagation for detecting internal damage in composite materials. & Ultrasonic damage detection and material characterization in composite structures [132] \\ \hline \multirow{2}{*}{Signal Processing Techniques} & \multirow{2}{*}{STFTs} & Analyze signals in time-frequency domains using a moving window. & Identify cracks in thin laminates [133] \\ \hline \multirow{2}{*}{Signal Processing Techniques} & \multirow{2}{*}{WTs} & Decompose signals into wavelets to detect localized changes. & Monitor delamination and small-scale damage [82] \\ \hline \multirow{2}{*}{Signal Processing Techniques} & Frequency Response Function (FRF) & Examines frequency response to detect structural changes. & Detects fatigue damage and small cracks [134] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Taxonomy of damage detection models and techniques for SHM in composite materials.