## 1 Introduction
In previous studies, dynamic-based methods have been based on frequency measurements. This is mainly due to the higher accuracy of frequency-measuring instruments when compared to geometrical or mode-measuring instruments. With the subsequent advancement of such instrumentation, vibration-based methods have also been considered. In this regard, we propose damage assessment using either natural frequency [14], or from the parameters of the vibration characteristics [15]. The potential of applying vibration-based methods to a real-life bridge is an attractive prospect, and significant attention is being paid to it by many researchers [16; 17; 18; 19; 20]. Numerous variables, such as the weather, the wind, the amount of traffic, and data quality (signal-to-noise ratio), have an impact on this methodological approach. The use of natural frequency, mode shape, mode shape curvature, and dynamically measured flexibility has led to the identification of several different vibration-based approaches. These methods appeared during the late 1970s, examining the significance of statistics for vibration-based methods applied on forty highway bridges in New Mexico [21; 22]. Since then, various parts of these vibration-based techniques have been researched, using experimental data collected from existing bridges [23; 24]. The vibration data collection techniques have also been researched and developed. The two main groups of methods that have been used are direct vibration measurement and indirect vibration measurement [25]. Only monitoring the change in frequency stops at determining the mere occurrence of damage [26; 27; 28]. Several similar applications have been introduced, as follows: a method of analysing natural frequencies of beam structures with some random cracks based on the transfer matrix method and a rotational spring model of cracks [29]; using a vector of the first natural frequencies as the multivariable input damage determination on two real works [30]; using the natural frequency of the texture to determine the existence of damage [31; 32; 33; 34; 35; 36; 37; 38]. However, these approaches require preliminary tests to determine the natural frequency of the structure before failure occurs. For aged structure, without data, this is very difficult to achieve. In addition, few researchers have applied natural frequency to such complex structures as spatial frame bridges. Furthermore, another disadvantage of this method is that natural frequency information is impractical, because some combinations of failure phenomena (such as cracks at different locations) can produce similar changes in natural frequency. Along with the contemporary development of information technology, artificial intelligence and machine learning attempt to bring computers a little closer to the brain's capabilities, using a highly simplified imitation of some components of information processing in the brain. These neural networks study the human brain's capabilities and imitates them in hardware, software, or other devices. Once trained, the neural network can identify similarities in fresh input patterns and predict the output pattern as a result. The learning process and the testing process are the two fundamental functions of a neural network. Learning is the process of gaining knowledge from pre-existing information. This procedure has three steps: computing the output, comparing it to the intended target, and adjusting the weight. This procedure is then repeated for the metrics sample data set. A target function or a function error expresses the quality of the learning process. In recent years, interest has risen in using AI and machine learning in SHM [39]. In a lot of research, methods for detecting damage in beams and bridges have been combined with machine learning and vibration-based damage-detection techniques [40; 41].