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1. Introduction

The ability to make cities smarter has never been easier than it is now, due to the advent of cutting-edge technology. The “smartification” of cities is essential for the modern era’s growth and the improvement of metropolitan areas and their underlying infrastructure. To better serve their inhabitants, towns might benefit from using recent technology. Without a doubt, smart cities have many good effects on the quality of life of their residents, such as making transportation better and reducing the damage they do to the environment [1,2,3,4,5,6]. One of the benefits of smart cities is that they can combine systems that check the health of buildings with artificial intelligence and other new technologies to make urban infrastructure work better [7]. The most important and the foundational component of a city’s resources is its infrastructure. The urban structure is the result of a network of interrelated structural variables. A city’s growth and development depend on the appropriate implementation, development, and maintenance of its infrastructure. Because of this, in addition to the construction of the infrastructure, it must also be both developed and maintained at the same time. As a kind of transportation infrastructure, bridges were among the first infrastructure that was constructed by humans. Nevertheless, in today’s urban management, the bridge is considered a structure for crossing geographical constraints in order to optimize the use of available space for mobility and access to destinations. Therefore, it can be claimed that bridges are a vital piece of urban infrastructure, and proper monitoring of their health can extend their service life. Also, one of the most important issues related to urban management in smart cities is the SHM of bridges, which can increase their lifespan and efficiency [8,9,10,11,12,13,14,15]. One way to prevent damage and cut down on the cost of fixing this infrastructure is to keep an eye on the bridge’s structure and look for signs of possible damage. Traditionally, procedures such as frequent visual inspections by technicians, the magnetic method, and the mechanical wave (vibration) method were used to keep track of, regulate, and test the state of bridges [16,17]. Hence, SHM bridges have been plagued by defects and inefficiencies in the past. Even though a substantial amount of research has been done on how to find and analyze bridge damage, most data on bridges that are already in use is still gathered by looking at them. However, despite the fact that these approaches have been used for decades to evaluate bridges, they have significant drawbacks and do not always work, even though they are used for a broad variety of examinations. Non-destructive testing (NDT) and non-destructive evaluation (NDE) are two ways to classify these kinds of controls [18,19,20]. The lack of accessibility and inspection of all bridge components is a major downside of eye-based monitors as traditional methods. In addition, there is a chance that a technician will make a mistake or misinterpret a sign of danger. It should also be noted that in some cases, damage may start from the internal elements of the bridge structure, and this issue cannot be seen by visual inspection. There are many other problems, including the suspension of operations and servicing of bridges when the bridges need to be tested and inspected. In the event that this process may lead to financial damage, or even in some cases due to the high volume of traffic, there may not be a service stop for the bridges [21,22].

The advancement of structural systems and the technology used in them has unquestionably led to the creation of the smart city. In the last few decades, thanks to a lot of technological progress, a good foundation for monitoring systems for bridge structures has been built. This will help bridges, which are an important part of urban infrastructure, work better. Researchers have been able to use a wide range of new technologies, such as wireless sensor networks (WSNs), optical fiber sensors (OFS), electromagnetic sensors, and scanners, because the number of new technologies has grown so quickly in the last few decades. On the other hand, the Internet of Things has made it much easier to communicate, check, and collect data remotely and automatically. Approaches based on artificial intelligence are a key part of every step of monitoring the health of a structure, from gathering data to preparing it and analyzing it. These methods, which include planning and optimizing data collection tools like sensors, preparing data in the data center, processing and analyzing data, and planning and optimizing maintenance tools like 3D printers, can make bridge structure health monitoring systems more effective [23,24,25,26].

The goal of this research is to explore the possible benefits, problems, current methods, and recent advances in SHM systems for bridges that use AI and new technologies. One further objective of this investigation is to provide researchers with resources that will enable them to get a more in-depth understanding of the monitoring systems that are present on bridges. In particular, as part of the SHM system evaluation process for bridges, AI, drones and 3D printing technology will be looked at and discussed. It should also be mentioned that there are sections of AI, drone technology, 3D printers, and SHM systems that need further investigation, as well as elements of these techniques that have not been looked into as extensively, and where the need for more extensive research is felt. Even though SHM has received a lot of attention and its literature has been looked at many times, this study tries to give a broad and complete view of AI and recent technologies in the SHM of bridges, as well as recent advances and trends in this field. Figure 1 shows a graphical illustration of the structure of the study.

According to the intended structure, the rest of this study is as follows: bridge structural health monitoring systems and conceptualizations are presented in Section 2. Section 3 describes AI, drone technology and the 3D printer. Discussion of cutting-edge AI and recent technologies in bridge structural health monitoring (SHM) is included in Section 4. There are concluding remarks to be found in Section 5.

2. Bridge Structural Health Monitoring (SHM) System

2.1. The Importance of Using the SHM System

In terms of infrastructure, bridges are among the costliest options. Bridges may serve their communities for decades or longer, depending on factors like location and construction materials. The traffic loads carried by many bridges now far exceed what they were originally designed to handle. Because operating condition stresses are getting higher, structural fatigue is now a threat to the whole transportation system, not just to a single structure [27,28]. Therefore, monitoring, analysis of failures that occurred in the past, and the knowledge of the structure’s condition in the future can play a key role in increasing the lifespan and performance of bridges. There are both static and dynamic modal variations brought on by traffic conditions. Changes in static pressure are directly related to mass [29]. On the other hand, changes caused by traffic have been shown to be nonlinear and may go down as load effects go up. Modal properties may also be altered when a healthy bridge interacts with a moving vehicle. Because of this, it becomes far more challenging to do vibration monitoring on bridges that are currently in service. Therefore, it is crucial to recognize any structural degeneration. The optimal system for monitoring, managing, and assessing conditions must be determined. One of the most effective systems for controlling structure performance is the structural health monitoring (SHM) of bridges. SHM is a deliberate procedure in which, firstly, the required static or dynamic reactions of the structure are recorded and gathered by employing sensors and electrical equipment. After this, computer tools and algorithms are used to process the data. Data processing is followed by interpretation to provide decision-useful insights about the state of the structure. In fact, the term “structural health monitoring” is used to describe a wide range of methods that give accurate information about a structure’s current state and how well it works, data which can then be studied in the near future. For accurate diagnosis and ongoing monitoring of bridge degradation, it is important to look at both how the bridges look and how they work. Innumerable beneficial studies have been carried out to explore the grounds for using SHM systems to monitor bridges as well as the manner in which these systems are used, and these studies have revealed the reasons [30,31,32,33]. Ko and Ni [34] reviewed recent technology developments in the field of SHM and their potential use in large-scale bridge projects. Their review revealed that new methods for inspecting and monitoring bridge safety are being made possible by advances in sensing systems, signal processing, communications, and data-mining technologies. In another study, Vazquez-Ontiveros et al. [35] created a new SHM technique by combining the Precise Point Positioning GNSS-Global Navigation Satellite System (PPP-GNSS) measurement technology with a different probabilistic strategy. They used the El Carrizo Bridge as a case study, and their results showed the precision of PPP-GNSS measurement technology as well as the potential advantages of the alternative probabilistic SHM technique. Mousa et al. [36] provided an overview of the vision and digital image correlation (DIC)-based SHM techniques for bridges. In their study, they considered different kinds of bridges, such as concrete, suspension, masonry, and steel bridges. Their study showed that the DIC could identify structural factors including vibration, deflection, and rotation as well as damage, like fractures and spalling. AlHamaydeh, and Ghazal Aswad [37] reviewed the most modern techniques and technologies for building large structures. They also talked about problems and limits, which helped them figure out some future research needs and directions. According to their findings after studying a considerable amount of literature on the topic, many elements of SHM approaches and technologies still need further research. Additionally, they have identified a few research gaps in the SHM approaches. Kamal, and Mansoor [38] presented a study on opportunities and challenges in SHM systems. They discussed conventional approaches to visual, destructive, and nondestructive evaluation in SHM systems. They also discussed how SHM can be used with IoT and what its pros and cons are based on what has already been conducted. After the final reviews, they decided that it was clear that SHM would benefit greatly from the IoT if technical problems like energy use, scalability, data security, and reliability were dealt with. In another review, Enshaeian et al. [39] reviewed the scientific research on the SHM systems that has been done on some US bridges over the last 20 years. A number of US monitored bridges were examined in terms of monitoring devices, monitoring scope, and key findings based on documentation in the scientific literature. Maroni et al. [40] introduced the development of a SHM and an event-based classification system for managing bridge scour. This system goes beyond and adds to the current risk-rating procedures by including information from different sensors and the different sources of uncertainty that affect scour estimates. Their proposed system was based on a probabilistic framework for estimating scour risk. This framework can be used to classify the scour risk of bridges in real time during a heavy flood.

A summary of the process of evaluating the performance of bridges under a SHM system is shown in Figure 2.

There are significant dangers to the current bridge infrastructure, such as damage and inefficiency. Natural or man-made disasters, such earthquakes and explosions, compound these issues. The reliability of public infrastructure like bridges is crucial in modern societies. Using these kinds of buildings, particularly in the aftermath of earthquakes and other natural catastrophes, helps to reduce the problems after these disasters as much as possible. In order to do this, the infrastructure’s problems need to be diagnosed and figured out. The SHM of bridges can increase the service life of bridges and reduce the costs required to repair and strengthen the bridge structure [41,42]. In fact, the use of SHM for bridges can reduce the unforeseen economic costs of maintenance, and by increasing the safety of bridges, it can prevent irreparable disasters. During the monitoring period, a full history of the bridges’ structures can be gathered. This means that the technicians will keep a record of all the inspections done while the bridge is in use. All the documents related to these inspections will also be kept in these records. This makes it possible to predict damage more accurately [43]. Here, structural health monitoring may be used as a novel non-destructive test (NDT) or non-destructive evaluation (NDE). The technology depends on a number of things, such as sensors, the ability to send and receive data, smart materials, intelligent computations, and the ability to process structures [44,45]. If bridges are not regularly inspected and maintained, they might become less effective, get damaged, or even be destroyed. This may have both immediate and long-term consequences for the individuals who rely on these systems in their daily lives and professional endeavors. Several disasters related to the lack of attention to structural health monitoring in recent decades caused a change in attitudes towards the importance of structural health monitoring. In the middle of rush hour on a warm day in 2007, the I-35W bridge in Minneapolis, Minnesota, crumbled into the Mississippi River below. Thirteen people were killed, and the collapse exposed to engineers how badly the U.S. [46]. infrastructure was aging. Because of this, engineers worked harder to control, fix, and improve the situation. As yet another tragic example, in the summer of 2018, the Polcevera bridge (Ponte Morandi or Ponte delle Condotte) in Genoa collapsed. This collapse, that killed 43 people, was caused by corrosion in some steel cables, which in turn snapped as a result of structural deficiencies in the building’s design, construction, and subsequent maintenance. Because of this, in 2020, engineers employed cutting-edge technologies and instruments to keep tabs on the condition of the replacement bridge for the Genoa structure [47]. After these kinds of incidents, engineers have tried to come up with ways to stop them from happening again.

2.2. Defining and Identifying the Damage

Among the traditional methods for identifying the location and extent of damage in the health monitoring system of bridges are visual inspections by expert observers. Although this method has advantages, such as a local and visual visit, human understanding of the damage in place, etc., it also has several disadvantages. Among the disadvantages of visual inspections, we can point out the lack of access or the difficulty of the inspector’s access to all points of the bridge. Furthermore, if the internal elements of the bridges are damaged, visual inspection cannot be used to monitor them. In the last few decades, researchers have made a lot of progress on improving the SHM system in the areas of monitoring, control, assessment, and making decisions. One of their most important contributions has been the development of artificial intelligence (AI) systems for this purpose [48,49]. Bridge damage monitoring and analysis have been greatly aided by technological developments and interdisciplinary efforts over the last several decades. On the other hand, new technologies like the Internet of Things (IoT) have helped SHM technology to grow significantly.

SHM may be divided into two broad types: diagnostic and prognostic. Diagnostic methods identify flaws, pinpoint their sites, and measure their severity. The ultrasonic method, radiography, penetrant liquid tests, magnetic particles, and eddy currents are all methods that are often used to find defects as diagnostic methods [50]. However, prognostication uses diagnostic data to predict how long a structure will remain functional. This technique allows for online testing of the structure’s behavior, allowing for remedial and preventative measures to be implemented in the event of any more defects that are detectable by the sensor in question. Damage is defined as any change to a system that detracts from its ability to perform its intended purpose, either now or in the future. Damage detection is an important part of SHM because figuring out if there is damage, where it is, and how bad it is, can have a big effect on the decisions made about monitoring and managing structural health. A meaningful assessment of damage requires comparison to the condition of the system before the harm occurred. A system is said to be in its “initial state” when it has not been modified or damaged in any way relative to its original condition [51]. Many times, a bridge’s damage is identified utilizing a hierarchical system. In order to properly identify problems at a higher level in a hierarchical system, it is generally required to have some understanding of the lower levels. Each subsequent level’s success may be affected by the prior levels’ results in comparison to the one being completed [52,53,54]. Damage has been categorized in various novel ways by researchers [52,53]. The classification system divides damage into five classes as follows:

  • -. Level I (Damage detection): This level is identified when a damage event occurs.

  • -. Level II (Damage location): This level is detected when damage occurs, and then the location and orientation of the damage are determined.

  • -. Level III (Damage typification): This level is detected when damage occurs, the location and orientation of the damage, and then damage severity is determined, and the kind of damage is estimated.

  • -. Level IV (Damage extent): This level considers the possibilities of limiting or postponing the extent of damage once previous levels have been completed.

  • -. Level V (Damage prediction): After completing the previous four levels, this level assesses the bridge’s remaining usable life or its viability status, depending on the situation.

2.3. Challenges and Different Sorts of SHM Systems

Many variables combine to make SHM a challenging procedure to perform. Data collection, sensors that are not always accurate and are not all the same, and the use of different ways to analyze data all make this task a lot harder. Visual inspections are still used for the great majority of in-service bridge data collection, despite the extensive academic work on bridge deterioration detection and identification [18,55]. There are several problems with this traditional approach of SHM, such as the high inaccuracy of this technique, the inaccessibility of certain bridge sections, the failure to detect many localized failures, and the inability to identify situations when damage originates from inside structural components. But in recent years, with the progress made in the scientific field, the SHM system of bridges has undergone fundamental changes. Advances in technology, such as optical sensors, lasers, image processing systems, and cheap sensors have allowed for advances in SHM system performance [56,57,58,59,60,61]. Also, in recent years, other technologies like blockchain technology and information security, as well as the rise of 5G Internet and the IoT, have made many researchers interested in using these technologies [62,63,64,65,66,67,68]. Using current methods may have a number of benefits, such as the ability to keep an eye on the structure in real time, the ability to find damage early on, shorter inspection times and costs, and lower repair and reinforcement costs. Generally, the best SHM system will be low-cost, non-invasive, and fully automated to eliminate the potential for human error. In particular, the bridge should not have to be shut down for any phase of the installation or operation of the system [69].

In general, SHM can be considered for two systems, including Model-driven SHM and data-driven SHM. The undamaged condition of the building must be assumed or created in order to locate the damage, as was indicated before. In Model-driven SHM, for damage detection, the vibration data is analyzed using a system identification paradigm to find the modal properties and evaluate trends. Researchers suggested using sensitivity matrices, which are the backbone of the new field of model updating, to find damage. The models used in model-driven SHM often use finite element analysis (FEA) [70]. By including the discrepancy between FEA predictions and experimental data, sensitivity matrices are utilized to refine and improve upon the original design. Appropriate research has been done using model-based SHM on bridges. It should be noted that this technique has not only been used in real studies but also works in the lab [71,72]. Despite its many benefits, the Model-driven SHM has certain drawbacks as well. Model updates require time, and Model-driven calculations are difficult and need to be confirmed with experimental data. Data-driven methods, on the other hand, are excellent at dealing with uncertainty and unpredictability. Recently, with the advent of computational intelligence and the use of data-driven methodologies based on machine learning techniques, SMH systems have made significant strides. There are a number of methods for keeping SHM system uncertainty under control. The use of AI and ML techniques is becoming more popular due to its efficacy. Due to their efficacy, these methods have seen widespread application in recent years. Building damage can be found using either only data-driven methods or a combination of data-driven methods and model-driven methods [73,74].

In recent decades, the rapid growth of technology as well as increasing progress in the field of artificial intelligence have had a direct and indirect effect on increasing the performance of SHM systems, especially data-driven SHM systems. So, this study looks into how artificial intelligence and other technologies affect data-driven SHM systems. Table 1 shows the abbreviations and acronyms used in this research, as well as some other abbreviations that we might see in SHM system reviews [75]. In the next sections, additional explanations are given about the role of artificial intelligence in SHM systems, as well as the relationship between artificial intelligence and other technologies for data-driven SHM systems.

3. SHM of Bridge, AI and Recent Technologies

Industry 4.0, also known as the Fourth Industrial Revolution, builds on the successes of the previous three revolutions by merging technologies that traditionally existed in separate domains (biology, computing, and the physical world) [76,77,78]. The existence of industry 4.0 in our era has created this possibility for many engineering disciplines, which has increased the performance of engineering projects by using new technologies and integrating them with each other [79]. The SHM of bridges, on the other hand, is one of the most important engineering measures, and Industry 4.0 has made this process better. In recent years, significant progress has been made in the field of AI. These improvements are due to the increased power of processors and the availability of large amounts of information [80,81,82,83]. Also, other new technologies are directly or indirectly related to artificial intelligence. Therefore, this study examines the progress of AI and its relationship with other new technologies in order to increase the performance of SHM bridges [84]. Table 2 indicates the summary of some of the most important recent research conducted on the structural health monitoring of bridges using new technologies.

As it is clear from Table 2, sensors and machine learning techniques played a key role in recent studies. Although other technologies were also investigated, it can be seen that the need for more extensive studies on the role of technologies such as the IoT, UAVs, and 3D printers is completely felt. More comprehensive explanations are provided in the following sections.

3.1. SHM of Bridge and AI

Because the SHM system makes use of innovative technologies like the internet of things (IoT), sensors, and computer processing, this system is a novel and inventive alternative to non-destructive evaluations [101]. As a result of SHM’s application of these technologies, the efficacy of this process is improved, and the certainty that there is no damage to the structure is increased. Maintenance and repair costs also go down. In the area of SHM, various new methods have been developed in recent years, the vast majority of which are based on finite element modeling or the use of modal features [102]. Because of the extensive number of computations required and the inherent unpredictability of the models, these approaches are relatively incompatible with real-world scenarios and cannot achieve the desired results. On the other hand, data-driven SHM systems do not need structural modeling and are a good alternative to model-based SHM systems for failure detection in real applications [103,104]. The damage to a building can be identified using data-driven methods, either on their own or in conjunction with model-driven strategies. SHM is a challenging system due to a lack of information and the associated challenges of modeling, measuring, and processing signals. Consequently, AI/ML-based methods may be helpful in SHM. Malekloo et al. demonstrated in their analysis that there are eight stages of the SHM process in which AI and ML technologies might potentially play a role [105]. Figure 3 shows these eight steps in brief.

Based on Figure 3, AI solutions and ML algorithms have the potential to play a substantial role in these eight processes, depending on the process involved in each of these steps. One of these eight processes is pattern recognition, and ML algorithms play a significant part in making pattern recognition more effective. Researchers in the area of structural health monitoring have been attempting to develop pattern recognition-based failure detection algorithms in recent years [106,107]. The pattern recognition operates on the premise that if a sufficient range of patterns are available and the investigated structure’s state is found to match or be near to one of those patterns, then the state gained from the structure and associated damage is a pre-determined unique. It is shown that using the information about the pattern, it is possible to figure out what kind of damage is in the structure and at what location it is [53]. In fact, SHM systems use either historical data or statistical data collected from a large number of samples to find patterns, like the deterioration of structures. Scientific research confirms the efficacy of using such algorithms to detect building deterioration. 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]. Wang 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.

3.2. SHM of Bridge and AI and Drone Technology

One of the most important technologies used in civil projects today is drone technology. Micro air vehicles (MAVs) and unmanned aerial vehicles (UAVs) fall under the category of drones [128,129,130]. Several categories have been introduced for drones. These categories can be based on the physical characteristics (weight, dimensions, and structure) or performance (duration, height, flight range, or controllability) of drones. Nowadays, with significant advances in the UAV industry, this technology can be present in a wide range of construction industry. One of the most important engineering sections that saw fundamental changes with the introduction of this technology was SHM [131,132,133]. Due to the structure of bridges, it may not be easy to access them from all angles for visual inspection. Drones are one of the best ways to get real-time information on the SHM system. Drones may be remotely piloted by a human operator or fly with varying degrees of autonomy. This level of independence can range from co-piloting (using the autopilot) to flying without a pilot. Drones are able to fly in any direction by harnessing the power of aerodynamics. Therefore, these airborne gadgets may be piloted remotely or with the help of pre-flight programming and dynamic automated systems. The employment of UAVs in SHM systems has several benefits, including their portability, rapid speed, high degree of maneuverability, and low power requirements [134,135]. UAVs have a wider range of applications than their image and video capabilities would suggest. They are also capable of being fitted with additional sensors for vibration-based analysis methods [105]. In a study, Malekloo et al. investigated the relationship between SHM systems and the applications of UAVs. They stated that UAVs have the potential to consolidate many stationary monitoring system components into one easily transportable mini-SHM. Also, they listed a set of different applications for damage detection and localization by UAVs in SHM systems. Figure 5 shows some of these applications [105].

The role of artificial intelligence in connection with drones can be discussed from several angles. The first place where artificial intelligence can be useful is that it can be used to develop software that controls the direction and route of drones in semi-autonomous and fully-autonomous systems. This software can help improve the performance of drones [105]. Even though drones are used on a frequent basis, it will be hard to quantify and analyze the data collected because there is so much of it. In addition, transferring the data obtained from the health monitoring of the structure by drones, such as photos and videos, to the sensors is another challenge in this field. In these cases, too, they can play an effective role in converting the obtained data and images and transferring them to the sensors by leveraging the capabilities of artificial intelligence systems [136,137]. Also, the systems that are based on the ground and perform control and evaluation of safety, security, and airspace awareness for UAV systems also need to use artificial intelligence solutions due to their complexity. It should also be noted that the simultaneous use of drones in SHM systems and emergence of new technologies such as image processing, combined with a new generation of machine learning algorithms (deep learning networks), and a new generation of sensors, the IoT, 5G and 6G networks, has caused more and more researchers to use drones to monitor the health of bridges.

3.3. SHM of Bridge and AI and 3D Printing

Researchers and engineers have been pursuing improvements in automated pavement crack sealing platforms from the early 1990s in some universities such as Carnegie Mellon University (CMU), the University of Texas, Austin (UT Austin) and University of California, Davis (UC Davis) [138,139,140]. With recent developments in additive manufacturing and great interest in automation of the structural maintenance, the use of 3D printing as a promising technology in bridge SHM systems has attracted the attention of researchers. Pavement maintenance has a special role as an indicator of bridge traffic in the SHM system process of bridges. As one of the automated maintenance methods, automated pavement crack sealing was developed using 3D printing technology [141]. Development of automated 3D printers has been based on two main models of platforms: a Cartesian table-based platform and a robot arm-based platform. The development of these two platforms has continued until today. Figure 6 is a simplified representation of the 3D printers used by DTU Wind and Energy Systems, which includes both a Cartesian table-based platform (Figure 6a) and a robot arm-based platform (Figure 6b).

After detecting damage on the pavement with a scanner or a camera, a 3D printer based on fused deposition modeling (FDM) is used to repair damages with asphalt sealant [138].

By preprocessing the damage image, the shapes of pavement damage are specified. A 3D model of the repair is generated according to the shape and severity of the damage. A combination of AI methods such as machine learning and damage detection methods such as ultrasonic techniques, X-rays, etc. can be used to detect the shape, size, and depth of the damage. Furthermore, AI methods can be used to specify the type of repair based on the severity and shape of the damage.

Through 3D slicing software, the 3D model of the repair is converted to G-codes, which are utilized to control the movement of the nozzle heads and deposition of the materials. Based on the depth and shape of the damage, different types of materials, e.g., asphalt sealant with tiny sand and outer asphalt sealant with larger sand, can be used by the 3D printer to repair the damage. The selection of the material based on the type ofdamage can be automated using AI methods to accelerate and optimize the repair process.

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. Although inspection by human inspectors has an important advantage, and that is the accompaniment of the inspector’s senses in the system of SHM, such as the inspector’s sense of sight, touch, and hearing, which can be an advantage over the use of drones. But it should be noted that an inspector could also make a mistake. One of the best alternatives to visual inspection of bridges is the use of drones. Drones are reliable and intelligent tools that can check on the condition of bridges online and effectively [151,152]. Since the first drone was built, manufacturing of them has increased, and now, during the fourth industrial revolution, companies are realizing that drones may benefit from a wider range of features and improvements. Drones can check the condition of the bridge from relatively any angle and pinpoint specific damage thanks to their high altitude of flight. In addition to being able to fly in a variety of weather situations, UAVs may also be equipped with high-tech cameras that allow them to continuously monitor the bridges from every angle [153]. Given recent developments in the fields of Internet of Things, 5G and 6G Internet, as well as artificial intelligence, on the one hand, and experts’ desire to use drones in SHM systems on the other, many developments and studies are still required. Without a doubt, these changes will make it easier for drones to check on the health of bridges. With the improvement of technology and the simplification and improvement of laws and rules, drones can be used in bridge SHM systems on a large scale and as a useful tool.

  • -. In an age when artificial intelligence (AI) techniques like machine learning are being employed to improve the value chain, 3D printing technology is an integral aspect of the fourth industrial revolution. As it is becoming more crucial as a decision maker, AI is becoming increasingly capable of processing enormous volumes of complicated data in a very short period. With the advent of new software and advances in AI, the system will be able to discern this need on its own and take over quality monitoring of the parts [154,155,156]. Using artificial intelligence, 3D printer technology can definitely help to fix many parts of bridges in the coming years in a better way.

5. Conclusions

This study provided a comprehensive review of the function of AI and its effects on data-based SHM systems, along with those of other technologies. The influence of ML algorithms, drones, and 3D printers on the way SHM systems work in bridges is the main emphasis of this review. Key ML approaches to pattern recognition were reviewed, together with the difficulties inherent to each approach, relevant theoretical frameworks, and relevant algorithms. In recent years, drones and 3D printers have been considered in the stages of monitoring and data collection as well as maintenance in the process of the SHM system of bridges. Hence, a review of how well these technologies work was performed, and the effects of AI on how well they work were also considered. These findings prove without a shadow of a doubt that SHM uses of AI have greatly improved system performance and given scientists new lines of inquiry. This study also demonstrates the increasing prevalence of AI applications in SHM studies. On the other hand, the use of artificial intelligence in the development of new technologies has increased the efficiency of these technologies. Drones and robots, as well as 3D printers, are examples of smart and small independent technologies that, when combined with artificial intelligence, have been able to open new horizons and opportunities for engineers in the processes of the SHM-system bridge. Lastly, it is expected that this review will give researchers a broad view of this topic by looking at the current state of SHM research and a wide range of ways to use artificial intelligence methods in data analysis or in combination with other technologies to make SHM systems work in a better manner. Also, despite significant advances in AI systems and new technologies, there is a serious gap in studies of the effects of environmental and operational changes (EOV) on the performance of SHM using these new technologies. Even though studies in this field have been beneficial and valuable, it has been suggested that this latter issue be looked at in more depth in future studies.

Author Contributions

Conceptualization, R.Z., S.S.H. and G.G.; methodology, S.S.H. and G.G.; investigation, S.S.H., G.G. and K.R.; resources, R.Z., S.S.H. and G.G.; data curation, S.S.H. and G.G.; writing—original draft preparation, R.Z., S.S.H., G.G. and K.R.; writing—review and editing, R.Z., S.S.H., G.G. and K.R.; supervision, R.Z., G.G., A.V. and A.S. All authors have read and agreed to the published version of the manuscript.

 Institutional Review Board Statement

Not applicable.

 Informed Consent Statement

Not applicable.

 Data Availability Statement

Not applicable.

 Acknowledgments

We’d like to thank Mahdi Ghaem very much for the great advice he gave us.

 Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables
View Image - Figure 1. Flow chart of study.Enlarge this image.

Figure 1. Flow chart of study.

View Image - Figure 2. A summary of the procedure of the SHM system to monitor the functioning of bridges.Enlarge this image.

Figure 2. A summary of the procedure of the SHM system to monitor the functioning of bridges.

View Image - Figure 3. An outline of the eight stages required to implement a data-driven SHM system.Enlarge this image.

Figure 3. An outline of the eight stages required to implement a data-driven SHM system.

View Image - Figure 4. An overview of the most popular learning algorithms in data-driven SHM systems.Enlarge this image.

Figure 4. An overview of the most popular learning algorithms in data-driven SHM systems.

View Image - Figure 5. The varied applications of UAVs in the field of damage assessment in SHM systems.Enlarge this image.

Figure 5. The varied applications of UAVs in the field of damage assessment in SHM systems.

View Image - Figure 6. Schematic view of DTU Wind and Energy Systems 3D printers: (a) Cartesian table-based platform and (b) robot arm-based platform.Enlarge this image.

Figure 6. Schematic view of DTU Wind and Energy Systems 3D printers: (a) Cartesian table-based platform and (b) robot arm-based platform.

List of common abbreviations and acronyms.

Acronyms and Abbreviations Definition Acronyms and Abbreviations Definition
AANN Auto associative neural network KNN K-nearest neighbors
AI Artificial intelligence LIN Linear
ANN Artificial neural network LSTM Long-Short Term Memory
ARMA Auto-regressive moving average MAVs Micro air vehicles
CART Classification and regression tree MLP Multilayer perceptron
CEEMDAN-HHT Complete ensemble empirical mode decomposition with adaptive noise—Hilbert Huang transform ML Machine learning
CHAID Chi-squared automated interaction detection NDE Non-destructive evaluation
CNN Convolutional neural network NDT Non-destructive testing
CRISP-DM Cross Industry Standard Process for Data Mining PCA Principal component analysis
CS Cuckoo search PPP Precise point positioning
DIC Digital image correlation PRNN Pattern recognition neural network
DL Deep Learning PSO Particle swarm optimization
DM Data Mining QUEST Quick, unbiased, efficient statistical tree
DTEs Decision tree ensembles RBF Radial basis function
ERA Eigensystem realization algorithm RBFNN Radial basis function neural network
FEA Finite element analysis RCNN Region Based Convolutional neural network
FNN Feed-forward neural networks ResNet Residual Networks
FCM Fuzzy c-means RF Random forest
FRF Frequency response function SDP Structural damage prediction
GAN Generative adversarial network SHM Structural health monitoring
GK Gustafson–Kessel SOM Self-organizing map
GMM Gaussian mixture models SVM Support vector machine
GNSS Global navigation satellite system UAVs Unmanned aerial vehicles
ICA Imperial competitive algorithm ULSCD Uniform load surface curvature difference
IoT Internet of Things VGGNet Visual graphics group network
ITS Intelligent transportation systems YOLO You only look once

Some recent studies on SHM of bridges vs. new technologies.

Research ML Technique Sensors UAVs IoT 3D Printers
Lin and Huang [85] +
Escarcega et al. [86] + +
Flah et al. [87] + + +
Wang et al. [88] + + + +
Civera et al. [89] + +
Ghiasi et al. [90] + +
Figueiredo et al. [91] + +
Bud et al. [92] + + +
Gomez-Cabrera, and Escamilla-Ambrosio [93] + +
Delgadillo and Casas [94] + +
Baba, and Kondoh [95] + +
Zhang, and Yuen [96] + + + +
Gordan et al. [97] + +
Bono et al. [98] + +
Zhuge et al. [99] + +
Modir, and Tansel [100] + + +
Overall + + + + +

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Abstract

The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

Details

Title
The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges
Author
Zinno, Raffaele 1   VIAFID ORCID Logo  ; Haghshenas, Sina Shaffiee 2   VIAFID ORCID Logo  ; Guido, Giuseppe 2   VIAFID ORCID Logo  ; Rashvand, Kaveh 3 ; Vitale, Alessandro 2   VIAFID ORCID Logo  ; Sarhadi, Ali 3 

1  Department of Environmental Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy 
2  Department of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende, Italy 
3  Department of Wind and Energy Systems, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark 
First page
97
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.3390/app13010097
ProQuest document ID
2761112458
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.