In this letter, the capabilities of time-expanded phase-sensitive optical time-domain reflectometry (TE -OTDR) using binary sequences are demonstrated. We present a highly flexible and integrable TE -OTDR approach that allows a customized distributed optical fiber sensor (range, spatial resolution, and acoustic sampling) by simply changing the length of the binary sequence and the reference clock frequencies of the binary sequence generators. The here presented architecture eliminates the need for the cumbersome arbitrary signal generators used to date to create the dual-comb spectra for interrogating the fiber. In this approach, the use of large binary sequences allows us to obtain dual combs in a simple and cost-effective way. Spatial resolution of ∼1 cm is achieved, attaining ∼15,000 independent measurements points along the interrogated fiber, with a capability of sensing ∼30,000 measurements points.
In recent years, the use of highly flexible wings in aerial vehicles (e.g., aircraft or drones) has been attracting increasing interest, as they are lightweight, which can improve fuel-efficiency and distinct flight performances. Continuous wing monitoring can provide valuable information to prevent fatal failures and optimize aircraft control. In this paper, we demonstrate the capabilities of a distributed optical fiber sensor based on time-expanded phase-sensitive optical time-domain reflectometry (TE-ΦOTDR) technology for structural health monitoring of highly flexible wings, including static (i.e., bend and torsion), and dynamic (e.g., vibration) structural deformation. This distributed sensing technology provides a remarkable spatial resolution of 2 cm, with detection and processing bandwidths well under the MHz, arising as a novel, highly efficient monitoring methodology for this kind of structure. Conventional optical fibers were embedded in two highly flexible specimens that represented an aircraft wing, and different bending and twisting movements were detected and quantified with high sensitivity and minimal intrusiveness.
Distributed acoustic sensors (DASs) based on direct-detection Φ-OTDR use the light–matter interaction between light pulses and optical fiber to detect mechanical events in the fiber environment. The signals received in Φ-OTDR come from the coherent interference of the portion of the fiber illuminated by the light pulse. Its high sensitivity to minute phase changes in the fiber results in a severe reduction in the signal to noise ratio in the intensity trace that demands processing techniques be able to isolate events. For this purpose, this paper proposes a method based on Unsupervised Anomaly Detection techniques which make use of concepts from the field of deep learning and allow the removal of much of the noise from the Φ-OTDR signals. The fact that this method is unsupervised means that no human-labeled data are needed for training and only event-free data are used for this purpose. Moreover, this method has been implemented and its performance has been tested with real data showing promising results.
<p>The analyzed active sinkhole is located on the outer bank of a meander of the Ebro River, affecting the Alcal&#225; de Ebro village and a flood-control embankment dike. Between 1927 and 1957 the Ebro River channel experienced a major shift of 500 m until its present position abutting the village. The development of the sinkhole, around 100 m long, is related to dissolution of cavernous salt-bearing evaporites (i.e., halite and glauberite) underlying the unconsolidated alluvium. It experiences progressive sagging subsidence punctuated by the catastrophic occurrence of nested collapses in the village and the dike since 2007, leading to the demolition of a building and recurrent high-risk and uncertainty situations during floods. The sinkhole has been partially treated with a number of costly remediation measures, including shallow injection of polyurethane foam, compaction grouting and the installation of geogrids. Subsurface information on the spatial extent of the dissolution and subsidence phenomena has been obtained by boreholes, electrical resistivity tomography and ground-penetrating radar. Surface deformation at the site has been monitored utilizing multiple techniques, including: (1) several lines of high-precision leveling since 2015; (2) terrestrial laser scanner since 2014; (3) distributed strain and temperature sensing in optical fiber based on BOTDA since 2019; and (4) Structure from Motion Photogrammetry with drone images since 2020. The available surface displacement data provide an opportunity to: (1) compare the performance of the different techniques and identify their strengths, weaknesses and complementarity for sinkhole monitoring; (2) analyze the impact of floods and the associated water-table rises and drops on the activity of the sinkhole; and (3) assess the performance of remediation measures by comparing subsidence data before, during and after their application. Regarding the latter point, surface displacement data indicate that some measures significantly reduced subsidence activity (compaction grouting reaching the karstification zone), whereas other measures (shallow injection of polyurethane foam) aggravated the situation (subsidence acceleration and expansion). Moreover, the location of some treatments shows a significant offset with respect to the active subsidence area (i.e., inadequately sited without displacement data). The rapid sagging subsidence occurring at the present time on a paved area 11 m across is ascribed to a large cavity spanned by a geogrid, which eventually might produce a damaging catastrophic collapse. Precisely identifying the area affected by sinkhole subsidence and characterizing the spatial and temporal patterns of the surface displacement is essential for assessing the associated hazard and designing effective remediation measures. The most suitable monitoring techniques largely depend on the subsidence mechanisms (sagging vs. collapse), displacement regime (progressive vs. episodic), subsidence rates, and characteristics of the area.</p>
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