KEYWORDS: Bridges, Augmented reality, Autoregressive models, Radar, Data acquisition, Visualization, Interferometry, Structural health monitoring, Signal to noise ratio, Vibration
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.
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.
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