## 1 Introduction  
Structural health monitoring (SHM) is a field of science that focuses its efforts on evaluating and monitoring the integrity of a structure of interest [1]. Structural health monitoring systems are based on the design of sensing systems and structural models to evaluate machines and structures.  
Although SHM systems are not a new field of research, computational advances in sensing hardware and the computational power of embedded devices drive the generation of reliable data for developing models based on classification and prediction data, including machine-learning algorithms in SHM systems. Moreover, sensors, such as accelerometers, are inexpensive compared to other sensors and can be effectively deployed in a sensing system to implement vibration-based SHM systems [2]. Accelerometers, or when combining them with other sensors, are the dominant sensing approaches for these SHM applications [3]. Because vibration-based systems date back to the late 1970s [4], technological advances represent a field of opportunities to improve existing solutions in the field of damage identification.  
Previous studies have been conducted in the field of SHM using machine-learning (ML) techniques. However, the importance of this study is to compare the application of artificial neural networks (ANNs), convolutional neural networks (CNNs) and support vector machine (SVM) techniques considering the input data, feature selection techniques, the structure of interest, data size, the level of damage identification and the accuracy of the ML model. In addition to the fact that each of the above ML techniques has been used for similar tasks in structural damage and system identification, some of them perform better when the data comes from data generated by multi-sensor data fusion or when the data are processed with damage-sensitive feature extraction techniques, such as Hilbert-Huang transform (HHT) or wavelet packet transform (WPT). In addition, this study could provide a starting point for the selection of ML techniques and signal processing techniques for future SHM ML-based solutions where structural configuration or data features have similarity with previous studies that achieved good structural damage or system identification performance.  
This paper is structured as follows. First, a brief description of the concepts related to SHM systems is presented in Section 2. In Section 3, the most common feature selection algorithms are presented, and a review of related works is given. In Section 4, the two main branches of SHM system models are presented, namely, the physics-related model (Section 4.1) and the data-driven models (Section 4.2). In Section 4.2, three ML techniques and their application in SHM systems are explored: ANN, CNN and SVM. At the end of Section 4, a comparison between the two main model branches is established in Section 4.3. Section 5 includes a brief review of the use of signal processing techniques to mitigate uncertainties effects from the environment and noise. In Section 6, a summary of the most important features of the application of damage identification techniques are presented in a list. Finally, Section 7 contains conclusions and future work.