Structural health monitoring (SHM) plays a critical role in ensuring the safety of large-scale structures during their operational lifespan, such as pipelines, railways and buildings. In the last few years, radio frequency identification (RFID) combined with sensors has attracted increasing interest in SHM for the advantages of being low cost, passive and maintenance-free. Numerous scientific papers have demonstrated the great potential of RFID sensing technology in SHM, e.g., RFID vibration and crack sensing systems. Although considerable progress has been made in RFID-based SHM, there are still numerous scientific challenges to be addressed, for example, multi-parameters detection and the low sampling rate of RFID sensing systems. This paper aims to promote the application of SHM based on RFID from laboratory testing or modelling to large-scale realistic structures. First, based on the analysis of the fundamentals of the RFID sensing system, various topologies that transform RFID into passive wireless sensors are analyzed with their working mechanism and novel applications in SHM. Then, the technical challenges and solutions are summarized based on the in-depth analysis. Lastly, future directions about printable flexible sensor tags and structural health prognostics are suggested. The detailed discussion will be instructive to promote the application of RFID in SHM.

Radio frequency identification (RFID) sensors for pipeline corrosion monitoring have been considered an attractive area due to the advantages of battery-free and maintenance-free. Due to the skin effect of high-frequency electromagnetic waves, existing RFID sensors can only detect the surface corrosion of pipelines. To realize internal corrosion monitoring, a magnetic permeability perturbation-based RFID sensing method was developed in this article. Under direct-current magnetization, internal corrosion causes magnetic field distortions, and incurs magnetic permeability perturbations in the skin layer of surface. These perturbations can be detected by the RFID sensor. Considering the resonance frequency shift (RFS) as a feature, a patch antenna sensitive to magnetic permeability was designed. Moreover, steel plates with artificial internal defects were simulated and tested to validate the feasibility of the proposed method. It was observed that the surface magnetic permeability perturbations caused by internal corrosions of different depths incur different RFS values. Finally, pipelines with artificial and natural internal corrosions were tested to validate the feasibility of the proposed method. The RFS caused by the natural corrosion was 4.26 MHz, and the RFS caused by the sound area was 1.63 MHz. The proposed magnetic permeability perturbation-based RFID method provides a promising method for pipeline internal corrosion monitoring with the application of RFID sensing technology.

Radio frequency identification (RFID) antenna sensors have become one of the most interesting areas of passive strain sensing owing to the advantages of battery-less and wireless interrogation. However, existing RFID methods could mainly detect strain magnitude with its direction along the length or width of the radiant element. To meet the practical purpose of strain detection, a magnitude and direction detection method based on an RFID sensor array is proposed. First, a single strain sensor, i.e., a short stub feed patch antenna, is designed. Specifically, taking resonant frequency shift (RFS) as the feature of strain amplitude, the sensor has different sensitivities in the electrical length direction and width direction. Then, based on this characteristic, a sensor array consisting of four sensors with a mutual angle of 45° is proposed to achieve the quantitative detection of strain magnitude and direction. Finally, the feasibility of the proposed method is validated by simulations and experiments. Experimental results show that the sensitivities of the single strain sensor in the longitude direction and transverse direction are −873.91 and 57.28 Hz/ $\mu \varepsilon $ , respectively. The strain magnitude detection accuracy of the proposed sensor array is within 10% except for individual abnormal values, and the direction detection error of the sensor array is within 3°.

Ultrasonic guided waves (UGWs) are well-suited for the nondestructive inspection (NDT) of adhesive joints as they can enable the transmission and reception of signals across the joint without requiring direct access to the damaged area. Conventional damage indices (DIs), such as the peak of the signal envelope, are limited for Carbon Fiber Reinforced Polymer (CFRP) adhesive joints due to the material's anisotropy and the complexity of UGW propagation. This is especially challenging for disbond detection and sizing. This study proposes an unsupervised approach using autoencoders for automatic disbond characterization in CFRP joints. Angle beam transducers (ABTs) were used in a pitch-catch setup to collect UGW signals under pristine conditions at two excitation frequencies (320 kHz and 650 kHz). The proposed DI is based on the reconstruction error between the original and autoencoder-reconstructed signals. Three architectures, including fully connected, Transformer-based, and CNN-LSTM autoencoders, were evaluated using 5-fold cross-validation and tested on independent scans. Performance was assessed using different metrics. Results show that the CNN-LSTM model consistently achieved the highest detection accuracy, while the Transformer model exhibited the most stable performance across folds. In a controlled temperature and humidity environment, the proposed DI provides superior localization and sizing of disbonds compared to conventional manually extracted signal features.

This paper presents a handheld guided wave testing probe, measurement procedure, and image processing algorithm to detect and locate defects in carbon fiber-reinforced (CFRP) structures. This study was performed by collecting data from a network of piezoelectric sensors controlled in a pairwise pitch-catch configuration. The probe includes sixteen piezoelectric elements spatially distributed in a square shape configuration to generate and receive ultrasonic guided waves (UGW) signals in the vicinity of a region to be inspected. Instead of collecting all the sensor combinations for defect detection, a measurement procedure is presented to record UGW signals from pairs of sensors that share identical distances and path conditions in the square shape configuration of the proposed probe. Additionally, a comparison between both baseline and baseline-free approaches is performed using the Reconstruction Algorithm for Probabilistic Inspection of Defects (RAPID) algorithm for defect detection, localization, and evaluation in a quasi-isotropic CFRP plate.