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- 2022 (9) (entfernen)
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This contribution introduces an investigation of a large-scale prestressed concrete bridge model (“BLEIB” structure at the BAM-TTS open air test site) by means of on-site cooperative measurements. This bridge has an external post-tensioning system and has been instrumented with the ultrasonic transducers, temperature sensors and optical fibers for Distributed Acoustic Sensing (DAS). Our experiment was designed to test the suitability of the novel 6C sensors developed within the framework of the GIOTTO project – the IMU50. The IMU50 sensor enables vibration measurements in translation along three axes and rotation around three axes. The geophone sensors were considered for complementary measurements of vertical velocity response. In the experiment, several perturbations were achieved by controlling the external influence factors such as loading and prestressing changes. The aim of the integrated measurement strategy was to fully observe the results of the condition change and to verify the effectiveness of multiple sensors for bridge monitoring.
The geometric phase in the beat signal from coherent interference of two frequency-offset light beams is measured using a novel distributed optical fiber sensing setup. In a fiber optic medium, with changing beam intensities, to the best of our knowledge, ours is the first measurement of the mentioned geometric phase. Experimental results of applying a 100-Hz sinusoidal stimulus to a polarization scrambler and a piezoelectric transducer inline to an optical fiber are presented. The results may enable novel distributed fiber sensing techniques.
In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection.
Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach
(2022)
We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber’s multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model’s uncertainties and select the features that contribute most to the model’s performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature information addressing the well-known cross-sensitivity effects.
Die verteilte faseroptische akustische Sensorik (DAS) wird vorgestellt, hinsichtlich ihrer Leistungsfähigkeit beschrieben und verschiedenste Anwendungsfelder, z.B. für das Zustandsmonitoring oder für seismische Messungen, beispielhaft gezeigt. Zudem werden mögliche Anwendungsfelder von DAS für den Kontext Kerntechnische Entsorgung (KTE) und Rückbau aufgezeigt.