# A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology
###### Abstract  
Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures. structural health monitoring; wireless sensing; RFID; crack monitoring; passive sensing +
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Cracks are generally caused by the following three factors: the first is the external loads (such as static and dynamic loads) caused by direct stress or structural internal forces; the second is caused by deformation factors (temperature and humidity changes, shrinkage and expansion, uneven settlement of the foundation, etc. ); and the third is caused by durability factors (such as steel corrosion, construction process quality, and other factors). The variables in crack monitoring and detection mainly include the crack location, the crack width and length, the crack depth, whether it is stable, whether there is exudate in the crack, the apparent quality of concrete around the crack, and so on. The location, number, and direction of cracks are generally recorded in the form of photographs and drawing fracture development maps. The length is measured with a straight ruler and tape measure, and the width can be measured with a crack width comparison card, a crack width meter, and a feeler ruler. The depth of the crack can be determined by an ultrasonic method or a local chiseling method, and a core sample can be drilled to verify, if necessary. The crack monitoring method based on traditional manual detection not only is time-consuming and laborious but also has the shortcomings of inaccuracy, incomplete memory, and low efficiency [4]. There have been many studies on crack monitoring methods. The point monitoring method is the earliest and most widely used method at present. However, in the actual concrete engineering structure, cracks in the concrete structure may not appear at the key points predicted by theory because of the non-uniformity of the material and calculation errors. Because of the limitation of the point monitoring method, distributed sensors have become a new means of crack monitoring. The commonly used crack monitoring methods are ultrasonic detection, the shock elastic wave method, the acoustic emission detection method, the photographic detection method, sensing instrument monitoring, optical fiber sensor network monitoring, etc. With the rapid development of computer hardware capabilities and intelligent algorithms, crack identification algorithms based on deep learning have been widely investigated and applied [5; 6]. In recent years, as neural networks with deeper layers have been proposed one after another, the accuracy of crack image classification has been getting higher and higher. On the basis of image classification algorithms, object detection has further advanced, and it can not only judge image categories but also use boundary boxes to locate target instances.