Structural health monitoring (SHM) is crucial in preserving the civil infrastructure asset and ensuring safety of the operations. Amongst the available SHM techniques, the ground-based synthetic aperture radar (GB-SAR) is one of the most reliable. However, a gap in knowledge with the use of this system exists when multiple targets are in the same acquisition range. The present study investigates into this aspect and proposes a two-stage procedure based on i) controlling the signal propagation characteristics during the data collection and ii) implementing advanced signal processing techniques to aid the interpretation of the measured signal. To this effect, three scenarios of interest are implemented in the laboratory environment, i.e., i) absence of targets, ii) presence of one target, and iii) presence of two targets in the centerline of the radar. The data collection is aided by augmented reality (AR), which allows to visualise the radar footprint and precisely control the acquisition according to the set scenarios. The collected data are processed using the empirical mode decomposition (EMD) and the Hilbert-Huang transform (HHT) techniques. The proposed methodology is shown to be effective in both the data control and processing stages. Results have proven that the signal response from multiple targets differs from that observed in the other investigated scenarios, hence showing potential for enhancing multi-target detection in structures with GB-SAR.
Structural Health Monitoring (SHM) is critical to ensuring the safety of structures such as bridges, tunnels, and dams. Despite some sensors being highly accurate, it is not always feasible to interrupt the serviceability of the structure for data collection. Within this framework, remote sensing methods such as the Ground-Based Interferometric Synthetic Aperture Radar (GB-SAR) have shown their capability in remotely collecting data of multiple targets simultaneously with a high sampling rate. However, detecting targets in their exact position is currently an area that requires further investigation for this technology. The present research focuses on developing a field investigation methodology to limit uncertainties and raise awareness about the significance of data collected by GB-SAR aided by augmented reality (AR). To this effect, head-mounted and mobile AR can support the use of techniques, such as GB-SAR, which work on fundamental geometrical principles, by providing guidance markers in real time for positional and reference information. The integration of both technologies can allow to pre-visualise the optimal position for data collection by aiding to match the structural targets under investigation within the area of interest. The proposed methodology is here implemented in clinical laboratory conditions to investigate the sensor’ sensitivity against testing parameters, such as the radar position and the distance to targets. The proposed methodology will contribute to collecting data with a higher accuracy and a lower uncertainty compared to other non-destructive methods utilised in the field. This study demonstrates the potential of using AR to enhance remote sensing methods for SHM and it builds up the foundation for future development into a more comprehensive SHM approach.
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