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This article presents a comprehensive study on the impact of irradiation optical fiber cores with a femtosecond-pulsed laser, operating at a wavelength of 1030 nm, on the signal amplitude in Rayleigh scattering-based optical frequency domain reflectometry (OFDR). The experimental study involves two fibers with significantly different levels of germanium doping: the standard single-mode fiber (SMF-28) and the ultra-high numerical aperture fiber (UHNA7). The research findings reveal distinct characteristics of reflected and scattered light amplitudes as a function of pulse energy. Although different amplitude changes are observed for the examined fibers, both can yield an enhancement of amplitude. The paper further investigates the effect of fiber Bragg grating inscription on the overall amplitude of reflected light. The insights gained from this study could be beneficial for controlling the enhancement of light scattering amplitude in fibers with low or high levels of germanium doping. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive study on the impact of irradiation optical fiber cores with a femtosecond-pulsed laser, operating at a wavelength of 1030 nm, on the signal amplitude in Rayleigh scattering-based optical frequency domain reflectometry (OFDR). The experimental study involves two fibers with significantly different levels of germanium doping: the standard single-mode fiber (SMF-28) and the ultra-high numerical aperture fiber (UHNA7). The research findings reveal distinct characteristics of reflected and scattered light amplitudes as a function of pulse energy. Although different amplitude changes are observed for the examined fibers, both can yield an enhancement of amplitude. The paper further investigates the effect of fiber Bragg grating inscription on the overall amplitude of reflected light. The insights gained from this study could be beneficial for controlling the enhancement of light scattering amplitude in fibers with low or high levels of germanium doping.

To improve the understanding of failure mechanisms and behaviour of hard rock tunnel linings, local load conditions were experimentally simulated and monitored using a comprehensive set of sensors and imaging techniques. The data includes measurements from distributed optical fiber sensors (DOFS), high-resolution cameras, load cells, pressure cells and LVDTs. Two types of loads were examined: rock block load and bond loss combined with a distributed load over the area of lost bond. The experiments replicated these conditions and were conducted in a laboratory setting where the shotcrete and substrate rock were substituted by cast fiber reinforced concrete (FRC) and cast concrete, respectively. To facilitate the loads, concrete cones were cast into the substrate concrete and pushed through the FRC top layer with a hydraulic jack to mimic rock block loads. To simulate the bond loss and the associated distributed load, lifting bags were installed and inflated between the FRC layer and substrate cast concrete. All specimens were monitored using DOFS embedded in two perpendicular directions and in two layers in the top FRC layer. In addition, the hydraulic jack was instrumented with LVDTs and load cells to measure displacement and load, and the pressure in the lifting bags was monitored using a pressure cell. Two cameras continuously photographed the top surface of the FRC layer, which had been painted with a speckle pattern, during the testing and the pictures can be used for digital image correlation (DIC). Lastly, each specimen was scanned with a 3D scanner prior to and after testing of the specimen., (© 2024 The Author(s).)

Pitting corrosion of steel bars can greatly impair mechanical properties of steel bars, so detecting pitting corrosion timely and accurately is crucial for assessing the health of structures. However, most current corrosion detection methods have some difficulties measuring pitting corrosion level of steel bars effectively. Considering that corrosion pits can cause strain redistribution of steel bar in the tensile state, this study proposes a method to quantitatively determine the pitting corrosion level through monitoring the steel strain distribution. Distributed optical fiber sensors (DOFS) based on Rayleigh scattering and capable of achieving the spatial resolution in millimeter scale were adopted to monitor the strain distributions of notched HRB400E steel bars subjected to tensile stress, and finite element (FE) method was employed to analyze the strain distributions of steel bars with different pit geometries. Experimental results showed that the bare fiber sensors (PI-FS) measured steel strain more accurately than jacketed fiber sensors. From strain distributions monitored by PI-FS, the pit location and pit length can be directly determined. Further, FE results revealed that the strain distribution along the fiber sensor position was significantly affected by the fiber sensor position relative to the pit mouth. Finally, the procedures to determine the pitting corrosion level from DOFS data and FE results were presented by mathematically comparing the similarity of strain curves given by the fiber sensor on a pitting-corroded bar and FE analyses of steel bars with different corrosion levels. In the end, the issues related to the practical applicability of the proposed method were discussed and limitations of this study were addressed. Although with limitations, this study provided a novel and promising way to quantitatively monitor the pitting corrosion level based on the strain distribution of steel bars. • New method to monitor pitting corrosion level by DOFS was proposed. • Position of fiber sensors relative to the pit mouth governed the strain distribution according to FEM. • Pitting corrosion quantification was achieved by matching strain distributions from DOFS and FEM. [ABSTRACT FROM AUTHOR]

A pattern recognition method for intrusion signals is introduced, leveraging distributed optical fiber sensors and convolutional neural networks. To enhance the model's generalization capabilities, two strategies are employed. Firstly, the sensor structure is refined, incorporating the strengths of both the Sagnac and Michelson interferometers to create a hybrid design with a wide frequency response. Comparative experiments confirm that this exceptional frequency response significantly boosts the model's generalization power. Secondly, to address the limitations of distributed fiber optic sensors, a multimodal approach is proposed, integrating video frame difference and fiber optic pattern recognition. The trained convolutional neural network model exhibits low computational overhead, robust generalization, and practical applicability. When applied to a dataset collected in diverse scenarios not included in the model training, the model achieved a CNR of 0% and a FPR of only 0.26% for the intrusion behavior of climbing fences. [ABSTRACT FROM AUTHOR]