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.
Structural health monitoring (SHM) is a necessary measure to maintain bridge infrastructure safe. To this purpose, remote sensing has proven effective in acquiring data with high accuracy in relatively short time. Amongst the available methods, the ground-based synthetic aperture radar (GB-SAR) can detect sub-zero deflections up to 0.01 mm generated by moving vehicles or the environmental excitation of the bridges [1]. Interferometric radars are also capable of data collection regardless of weather, day, and night conditions (Alba et al., 2008). However, from the available literature - there is lack of studies and methods focusing on the actual capabilities of the GB-SAR to target specific structural elements and components of the bridge - which makes it difficult to associate the measured deflection with the actual bridge section. According to the antenna type, the footprint of the radar signal gets wider in distance which encompasses more elements and the presence of multiple targets in the same resolution cell adds uncertainty to the acquired data (Michel & Keller, 2021). To this effect, the purpose of the present research is to introduce a methodology for pinpointing targets using GB-SAR and aid the data interpretation. An experimental procedure is devised to control acquisition parameters and targets, and being able to analyse the returned outputs in a more clinical condition. The outcome of this research will add to the existing literature in terms of collecting data with enhanced precision and certainty. KeywordsStructural Health Monitoring (SHM), GB-SAR, Remote Sensing, Interferometric Radar AcknowledgementsThis research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London. References[1] Benedettini, F., & Gentile, C. (2011). Operational modal testing and FE model tuning of a cable-stayed bridge. Engineering Structures, 33(6), 2063-2073.[2] Alba, M., Bernardini, G., Giussani, A., Ricci, P. P., Roncoroni, F., Scaioni, M., Valgoi, P., & Zhang, K. (2008). Measurement of dam deformations by terrestrial interferometric techniques. Int.Arch.Photogramm.Remote Sens.Spat.Inf.Sci, 37(B1), 133-139.[3] Michel, C., & Keller, S. (2021). Advancing ground-based radar processing for bridge infrastructure monitoring. Sensors, 21(6), 2172.
Masonry bridges are heritage assets still mostly under service. Ensuring safety and continuous operations of these structures requires robust structural health monitoring (SHM) programs and methods for continuous and fast data collection. Remote sensing, specifically the ground-based interferometric radar (GBIR) has proven viable in monitoring bridge structures. The GBIR system is a highly regarded electromagnetic method in SHM due to its non-invasiveness and the possibility to provide collection of high accurate data in static and dynamic mode. However, system measurement control to understand signal propagation patterns against target position is needed to increase accuracy and significance. Piers are the supporting system of bridge structures, and they define their boundary conditions. They directly affect the structures vibration and natural frequency. In this paper, the dynamic behaviour of a double track railway masonry bridge pier was investigated through the integration between GBIR and augmented reality (AR). The viability of an AR-based interactive user interface for GBIR measurements developed in previous scoping studies was here tested for system measurement control on a real-life structure. Data were analysed using signal processing techniques for feature extraction. Results have proven the viability of integrating AR into GBIR monitoring of bridge structures for real-time monitoring.
Monitoring and protection of natural resources have grown increasingly important in recent years, since the effect of emerging illnesses has caused serious concerns among environmentalists and communities. In this regard, tree roots are one of the most crucial and fragile plant organs, as well as one of the most difficult to assess [1].Within this context, ground penetrating radar (GPR) applications have shown to be precise and effective for investigating and mapping tree roots [2]. Furthermore, in order to overcome limitations arising from natural soil heterogeneity, a recent study has proven the feasibility of deep learning image-based detection and classification methods applied to the GPR investigation of tree roots [3].The present research proposes an analysis of the effect of root orientation on the GPR detection of tree root systems. To this end, a dedicated survey methodology was developed for compilation of a database of isolated roots. A set of GPR data was collected with different incidence angles with respect to each investigated root. The GPR signal is then processed in both temporal and frequency domains to filter out existing noise-related information and obtain spectrograms (i.e. a visual representation of a signal's frequency spectrum relative to time). Subsequently, an image-based deep learning framework is implemented, and its performance in recognising outputs with different incidence angles is compared to traditional machine learning classifiers. The preliminary results of this research demonstrate the potential of the proposed approach and pave the way for the use of novel ways to enhance the interpretation of tree root systems. AcknowledgementsThe Authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust. The Authors would also like to thank the Ealing Council and the Walpole Park for facilitating this research. References[1] Innes, J. L., 1993. Forest health: its assessment and status. CAB International.[2] Lantini, L., Tosti, F., Giannakis, I., Zou, L., Benedetto, A. and Alani, A. M., 2020. "An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar," Remote Sensing 12(20), 3417.[3] Lantini, L., Massimi, F., Tosti, F., Alani, A. M. and Benedetto, F. "A Deep Learning Approach for Tree Root Detection using GPR Spectrogram Imagery," 2022 45th International Conference on Telecommunications and Signal Processing (TSP), 2022, pp. 391-394.
Continual monitoring of civil structures (e.g., bridges) is essential to maintenance and ensuring safety and integrity. Non-destructive techniques, for instance, laser scanning, acoustics, and Ground Penetrating Radar (GPR) have been used in the past to study both the external and internal physical dimensions of objects and structures [1]. Light Detection and Ranging (LiDAR) technology has also been used in infrastructure monitoring to capture structural 3D information and to detect displacements in surfaces with millimeter accuracy [2]. Some other technologies, such as the Ground-Based Interferometric Radar (GBIR), suffer from precise target detection when monitoring objects and require installation of dedicated reflectors. Scanning structures using these existing state-of-the-art technologies can be expensive and time-consuming. Recently, visualization technologies such as Augmented Reality (AR) have been utilized with GBIR to solve target location uncertainties by making the radar’s beam of radiation interact with the investigated structure [3]. This work proposes the use of head-mounted Augmented Reality (AR) to visualize and support the monitoring of bridge structures. First, to overcome limitations of the HoloLens depth sensing technology, we used smartphone-based LiDAR (Apple iPhone 14 Pro) to capture and export a 3D model of the shape of the structure of interest. We then imported this model into the HoloLens application so that it could be overlaid and adjusted to match the physical bridge structure. Second, a digital component model was aligned with the position and orientation of the antenna. The beam of radiation is estimated in the visualization application using the method described in our previous work [3]; then, it is displayed as a frustrum determined by an equation according to this method. Since this method does not rely on real-time LiDAR or depth mapping, we are able to visualize the projected beam of radiation beyond the usual range limitations of up to 7 meters. Furthermore, this method can be used effectively in outdoor locations, which can be challenging for infrared-based depth mapping technology. The system can provide a relatively low-cost structural monitoring and assessment solution, which can allow researchers and surveyors to accurately visualize survey areas of interest and inform the decision-making process regarding maintenance of crucial civil structures.   Acknowledgments: Sincere thanks to the following for their support: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.   References [1] Alani A. et al., Non-destructive assessment of a historic masonry arch bridge using ground penetrating radar and 3D laser scanner. IMEKO International Conference on Metrology for Archaeology and Cultural Heritage Lecce, Italy, October 23-25, 2017. [2] Lee, J et al., Long-term displacement measurement of bridges using a LiDAR system. Struct Control Health Monit. 2019; 26:e2428. [3] Sotoudeh, S. et al. "A study into the integration of AR-based data collection and multi-dimensional signal processing methods for GB-SAR target detection." Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023). Vol. 12797. SPIE, 2023.
Masonry bridges are among the main structures built along the road and railway routes. These structures are generally old and have historical value. Considering the increased axial load and passing speed from these bridges, an in-depth study of these structures and their potential is of paramount importance. In the present study, an old masonry arch bridge located in 475 km of Western Iranian railway is investigated. For the detailed modeling of this structure, a three-dimensional finite element method (3DFEM) was implemented to take into account the details of the bridge and the train passing over it. The developed model was calibrated and validated using the dynamic field test results. The obtained results showed that the increase in the axial load and train speed over the bridge must be done carefully because exceeding the travel speed of 90 km/h and increasing the axial load from 20 to 30 ton makes serious problems in the bridge and interrupts its performance. Furthermore, it was found that the adequacy factor of the bridge under the standard load of LM71 is over 2.
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.
Ensuring safety of civil infrastructure is a crucial goal in structural health monitoring (SHM). Amongst the variety of monitoring sensors, the Ground-Based Interferometric Radar (GBIR) systems have recently gained attention for their advantages such as the very high resolution and fast data collection, as opposed to other conventional methods [1]. However, this technology suffers from precise target location when the acquisition is carried out in dynamic conditions. For this purpose, external reflectors need to be installed in the portion of the structure under investigation, to which then the signal response is assumed to be related. Considering this, the present research focuses on the investigation of the dynamic response of structures using GBIR aided with augmented reality (AR) [2]. AR assisted in controlling the position of the targets inside the radar’s beam of radiation and creating different acquisition scenarios in the same range based on a combination of their number and position. Dynamic excitations were applied in the field using light weight deflectometer (LWD) [3], and their effects on the collected signal were investigated using empirical mode decomposition (EMD) signal processing technique across the different scenarios. This allowed to have a better understanding of the signal response for multiple targets or at the boundaries of the signal footprint. Results show that for data capturing using GBIR systems, AR can enhance the data quality by better controlling the collection phase. In addition, the use of multi-dimensional signal processing techniques, such as the EMD, facilitated a more comprehensive understanding of the signal response in complex scenarios.   Keywords: Structural health monitoring (SHM), Ground-based interferometric radar (GBIR), Augmented reality (AR), dynamic excitation, Empirical mode decomposition (EMD).   Acknowledgements This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London.   References [1] M. Pieraccini, M. Fratini, F. Parrini, C. Atzeni, and G. Bartoli, “Interferometric radar vs. accelerometer for dynamic monitoring of large structures: An experimental comparison,” NDT and E International, vol. 41, no. 4, pp. 258–264, Jun. 2008, doi: 10.1016/j.ndteint.2007.11.002. [2] S. Sotoudeh, F. Benedetto, S. Uzor, L. Lantini, K. Munisami, and F. Tosti, “A study into the integration of AR-based data collection and multi-dimensional signal processing methods for GB-SAR target detection,” in Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), M. Bilal and F. Tosti, Eds., SPIE, Aug. 2023, p. 49. doi: 10.1117/12.3007430. [3] F. Tosti, S. Adabi, L. Pajewski, G. Schettini, and A. Benedetto, “Large-scale analysis of dielectric and mechanical properties of pavement using GPR and LFWD,” in Proceedings of the 15th International Conference on Ground Penetrating Radar, IEEE, Jun. 2014, pp. 868–873. doi: 10.1109/ICGPR.2014.6970551.
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