## 4 Discussion and Remarks  
Valuable studies that have been conducted in the past years clearly show that the use of artificial intelligence methods, either directly or indirectly, through integration with other technologies, has led to significant improvements in SHM systems. For example, it is possible to say that the technology reduces the role of human interpretation in data analysis, makes the system more efficient, speeds up control and monitoring, and cuts costs. Machine learning is fast and effective, especially when compared to traditional methods [142]. Therefore, there are some things that may be expressed or improved based on evaluations of previous research, and there are certain ideas that may be studied in future studies:  
In the data-driven method, the data patterns of the changes in bridges are directly examined, and since failure detection can become a statistical pattern recognition problem, conventional data-driven methods are derived from multivariate statistics. But their applications are not perfect because they use a large volume of data and take a long time to process. Since the introduction of cloud computing, traditional computing concerns have taken a back seat. Furthermore, new algorithms that incorporate greater parameters provide interesting answers to the issues of inefficient data processing in huge data sets. These advances are aided by the use of big data and AI technology [143,144]. The review of the past literature showed that AI is a promising way to analyze the huge datasets that come from monitoring the health of bridges, which is difficult and complicated when using with traditional methods. Based on these intelligent approaches, computational techniques are used to create a framework for SHM systems that are based on big data and solve computational problems. These techniques can also be used to create new ways to analyze data. In data-driven methods, one of the most important capabilities of artificial intelligence methods is pattern recognition that can identify damage. In fact, damage detection aims to train a model that can draw a decision boundary between the damaged and undamaged states. In the realm of data analysis, a plethora of machine learning and artificial intelligence techniques and algorithms may be found. Meanwhile, deep learning is one approach to machine learning and AI that models how the human brain acquires knowledge and excels at processing massive datasets. One of Deep Learning's most valuable features is that, as more data is supplied, the model's performance improves [145,146]. Automatic actions are also included in the modeling processes and feature extraction. So, it is suggested that more in-depth studies be done on how deep learning, one of the newest types of AI, can be used to improve the analysis and damage detection in SHM for bridges. Bridge structure health monitoring is long-term monitoring to ensure that the bridge is always operational. Sensors are one of the most important components of this system, one which plays a key role in monitoring bridges in order to obtain sufficient, accurate, and reliable data [147,148]. Therefore, choosing the type of data collection sensor is very important. These sensors should be durable enough to deal with environmental factors such as temperature, humidity, and corrosive substances, Also, choosing the location of these sensors is one of the other important points that should be considered. Although there are suggestions, instructions, and rules for choosing the right type and location of sensors, due to the importance of the issue and the relatively high cost of their preparation and installation, more studies should be done in the field of finding more optimal solutions [149,150]. Optimization algorithms such as genetic algorithm, particle swarm optimization algorithm, and harmony search algorithm are among the optimization algorithms that can be very effective in this regard. In SHM bridge systems, visual inspections are often expensive, take a long time, and are not always easy.