# A Novel Chipless Hybrid RFID Sensor for Metal Crack Detection
## 1 Introduction  
Structural Health Monitoring (SHM) is an emerging research area that uses wireless sensors for condition-based, reduced-cost maintenance [1]. Surface crack formation and propagation in civil structures affect the integrity of the structures, causing critical damages and accidents. Hence, there is a need for technologies that can perform early detection of failure cracks in over-stressed materials [2]. Furthermore, cracks expedite the moisture ingress that results in corrosion in steel reinforcement structures over time. The fatigue and structural deformations caused due to different environmental loads and working conditions lead to catastrophic damage to the structures [3]. Hence, early crack detection and identification of structural damage is crucial in critical applications like aircraft [4], bridges [5, 6], and railway structures [7]. To deal with such problems, there is a strong motivation to implement pervasive crack detection in the zones that are most prone to structural failures. [PERSON] et al. showed about 50-90% fatigue crack growth caused about 5-90% of mechanical failures [8]. State-of-the-art SHM methods are advancing rapidly with technologies like artificial intelligence, machine learning, and advanced sensor systems which emphasize high-precision data acquisition. These methods focus on improving the real-time evaluation of the structural integrity in infrastructures, such as historical monuments, bridges, railways, offshore structures, etc. Data-driven approaches using machine learning and deep learning are gaining popularity in handling large datasets, enhancing damage detection, and predicting potential failures [9]. Piezoelectric sensors as smart materials for precision defect detection are being investigated for their precision in detecting structural changes [10]. For large-scale structures, such as bridges, modal analysis and AI-powered tools are used to monitor the stress variations [11; 12]. Furthermore, new bio-inspired sensing paradigms are emerging, offering innovative approaches in SHM [13] to tackle the limitations of traditional SHM methods. Energy harvesting technologies have also been advancing to ensure sustainability, reducing the reliability on conventional power sources [14]. Conventional contact and non-contact methods, such as ultrasonic testing [15], eddy current testing, and visual testing, require periodic inspections that are time-consuming, and tedious for monitoring large-scale structures [16; 17; 18; 19]. Unlike wired sensors that require cabling, wireless sensors have the advantages of being compact, involve low-cost installation, and being free of wires and they can be placed in hard-to-reach areas because of their wireless nature [20]. There has been a rapid development of the Internet of Things (IoT) in various applications like smart cities, healthcare, industries, non-destructive testing and evaluation (NDT&E ), etc. [21; 22; 23; 24]. Passive wireless sensors provide life-long monitoring of structures and are quite desirable for SHM [25; 26]. Radio-Frequency IDentification (RFID) technology uses wireless capability by using electromagnetic waves to transfer the sensing and data information suitable for making low-cost passive sensors. This technology has been widely used in object tracking [27; 28], supply chains [29; 30], automatic identification of tags in the food industry [31; 32], NDT&E [33; 34; 35; 36; 37], and SHM [38; 39; 40]. RFID typically consists of an RFID reader that transmits electromagnetic signals for wireless communication and power; [PERSON] et al., for example, described an RFID communication platform [41]. RFID tags can be mass-produced with low cost, integrated deployment, and scalability compared to their wired counterparts. Over the last few years, this technology has been harnessed for sensing applications. The changes in sensing parameters are read in terms of the tag signal properties that are wirelessly detected by the RFID reader placed in the interrogation area.