We report on the development of a complete system for spatially resolved detection of critical soil displacement in river embankments. The system uses Brillouin frequency domain analysis (BOFDA) for distributed measurement of strain in silica optical fibers. Our development consists of the measurement unit, an adequate coating for the optical fibers and a technique to integrate the coated optical fibers into geotextiles as they are commonly used in dike construction. We present several laboratory and field tests that prove the capability of the system to detect areas of soil displacement as small as 2 meters. These are the first tests of truly distributed strain measurements on optical fibers embedded into geosynthetics.
We studied Brillouin gain spectrum characteristics in dispersion-shifted fibers having compound GeO2-doping compositions in the fiber core to realize simultaneous measurement of distributed strain and temperature. Due to various dopant concentration alongside the radius of tested nonzero dispersion-shifted fibers several multiple Brillouin scattering resonances were observed in the stimulated Brillouin spectra arose through backscattering on higher acoustic modes which propagated along the fiber axis. As a result of the varying acoustic velocities, the Brillouin resonance peaks featured different temperature coefficients which can be used to accomplish the simultaneous measurement of fiber strain and temperature. We presented our first measurement results for NZDS Fujikura and LEAF Corning fiber and discussed the superior sensory suitability of the former fiber types.
In comparison with traditional measuring Systems (pressure cells, piezometers and inclinometers which produce only point measurements) fibre optic sensors are well suited for spatiallydistributed monitoring purposes. The very tiny optical fibres of a diameter of less then 1 mm can operate as sensors in strong electromagnetic fields, under “harsh” explosive and Chemical environments and under ionizing radiation.
We present our research on the development of a Brillouin optical frequency-domain analysis (BOFDA) using a perfluorinated graded-index polymer optical fiber (PFGI-POF) as a sensing fiber. The described works include investigations both on the selection of the setup components with respect to mode coupling effects and on the impact of the humidity cross-sensitivity. The suitability of the developed POF-based BOFDA is proved by a distributed measurement on an 86 m long PFGI-POF recorded with the spatial resolution of 6 m.
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.