## 3 SHM of Bridge, AI and Recent Technologies
### SHM of Bridge and AI
Variability in environmental and user settings during data collecting is one of the challenges that must be addressed when putting pattern recognition systems to work in the real world. The failure might be misdiagnosed if this factor is not taken into account [108]. With the significant progress of AI and technology in recent years and the importance of the topic, it has become clear that there is a pressing need to develop new approaches to reduce the effect of errors caused by human justifications. This is despite the fact that valuable research has been done based on the diagnosis and classification of different types of damage, particularly with the assistance of data-based SHM systems based on AI that have the ability to generalize to unpredicted conditions and different configurations. Pattern recognition is a well-known field that has been used in many machine learning methods [109, 110, 111, 112, 113, 114, 115, 116, 117, 118]. For a SHM system's pattern recognition system to function successfully, it requires data from sensors with access to the physical environment. This sort of system can process and draw conclusions from a broad range of data, including pictures, videos, figures, and words. There are many different perspectives from which to investigate pattern recognition [119, 120, 121, 122]. One of the most crucial aspects of pattern recognition is the analytical methods and algorithms used to study and identify patterns. These analytical algorithms and systems may be roughly classified into three broad classes:  
* **Regression** (Supervised learning techniques): Regression techniques are used to predict the output values based on the input characteristics found in the data that is put into the system. * **Classification** (Supervised learning techniques): Classification, in contrast to regression, yields a category (label) rather than a numeric value. It is important to know that binary classification means predicting one of two classes, while multi-class classification means predicting one of more than two classes. * **Clustering** (Unsupervised learning techniques): Clustering is the technique of organizing a set of things such that items within the same group (called a cluster) are more comparable to each other than those within any other grouping. In Figure 4, the most popular learning algorithms are summarized according to the two broad categories of supervised and unsupervised algorithms. On the other hand, advanced artificial intelligence techniques such as deep learning algorithms are among the techniques that can play a key role in pattern recognition based on whether they are supervised, semi-supervised, or unsupervised [123]. Deep learning algorithms get high-level features directly from data by traversing through a layered hierarchy of concepts. This is different from traditional machine learning algorithms, which rely on feature engineering. There are many deep learning algorithms that were used in SHM due to their ability to analyze statistical data, images, and videos, such as Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) [124, 125]. Many valuable studies have been conducted in the field of applying deep learning algorithms to SHM and pattern recognition [126]. [PERSON] et al. [127] used bridge deflections to establish a deep learning-based strategy for structural health monitoring. Damage was determined using a LSTM framework applied to time-series data of deflection and temperature. Over the course of 15 months, they collected data on the Chongqing Egongyan Rail Transit Suspension Bridge's deflection and temperature to inform their suggested solution. Their study showed that the statistical properties of the SE index are related to the amount of damage and are only sensitive to changes in deflection that are out of the ordinary. It should be noted that the data that is used in pattern recognition by artificial intelligence systems can be included in several categories, including statistical data analysis, signal processing, and image and video analysis.