Search Results (112)

Cable shields may develop holes when the sheath is damaged. Time–frequency domain reflectometry (TFDR) is an effective method for detecting cable defects, but it cannot directly evaluate hole sizes. To address this issue, we analyze the impact of shield hole sizes on TFDR signals. Building on this analysis, we propose an improved dual-frequency TFDR method to measure shield holes and evaluate their sizes. This method directly measures the characteristic impedances and damage ratios using dual-frequency TFDR, followed by a two-step evaluation process to determine the hole center angles and lengths based on these measurements. Simulations and experiments validate the proposed method. In laboratory-scale experiments using a scaled cable model, and considering measurement noise, the maximum relative errors for shield hole length and center angle are 11% and 5%, respectively. Full article

Deep-well radar telemetry over ultra-long cables suffers from strong frequency-selective attenuation and impedance drift under high temperature and pressure. We have proposed a channel-adaptive “communication + acquisition” architecture for a 7500 m borehole radar system. The scheme integrates spread-spectrum time domain reflectometry (SSTDR; m-sequence with BPSK) to monitor the cable in situ, identify termination/cable impedance, and adaptively match the load, thereby reducing reflection-induced loss. On the receiving side, we combine time domain adaptive equalization—implemented as an LMS-driven FIR filter—with frequency domain OFDM equalization based on least-squares (LS) channel estimation, enabling constellation recovery and robust demodulation over the distorted channel. The full processing chain is realized in real time on a Xilinx Artix-7 (XC7A100T) FPGA with module-level reuse and pre-stored training sequences for efficient hardware scheduling. In a field deployment in the Shunbei area at 7500 m depth, radar results show high agreement with third-party geological logs: the GR-curve correlation reaches 0.92, the casing reflector at ~7250 m is clearly reproduced, and the key bottom depth error is 0.013%. These results verify that the proposed system maintains stable communication and accurate imaging in harsh deep-well environments while remaining compact and implementable on cost-effective hardware. Full article

This paper presents a compact dynamic optical frequency domain reflectometry (D-OFDR) platform enabling millimeter-scale, distributed strain sensing for real-time structural health monitoring (SHM) of reinforced concrete subjected to seismic loading. The proposed D-OFDR interrogator employs a dual-interferometer architecture: a main interferometer for strain sensing and an auxiliary interferometer for nonlinear frequency sweep compensation. Both signals are detected by photodetectors and digitized via a dual-channel FPGA-based DAQ board, enabling high-speed embedded signal processing. A dual-edge triggering scheme exploits both the up-chirp and down-chirp of a 50 Hz bidirectional sweep to achieve a 100 Hz interrogation rate without increasing the sweep speed. Laboratory validation tests on stainless steel cantilever beams showed sub-hertz frequency fidelity (an error of 0.09 Hz) relative to conventional strain gauges. Shake-table tests on a 2 m RC column under incremental seismic excitations (scaled 10–130%, peak acceleration 0.864 g) revealed distinct damage regimes. Distributed strain data and frequency-domain analysis revealed a clear frequency reduction from approximately 3.82 Hz to 1.48 Hz, signifying progressive stiffness degradation and structural yielding prior to visible cracking. These findings demonstrate that the bidirectional sweep-triggered D-OFDR method offers enhanced real-time monitoring capabilities, substantially outperforming traditional point sensors in the early and precise detection of seismic-induced structural damage. Full article

Optical frequency domain reflectometry (OFDR) is one of the key diagnostic tools for fiber optic components and circuits built on them. A low signal-to-noise ratio, resulting from the low intensity of backscattered signals, prevents the correct quantitative description of the medium parameters. Known methods of signal denoising, such as empirical mode decomposition, frequency filtering, and activation function dynamic averaging, make the signal smoother but introduce errors into its dynamic characteristics, changing the intensity of reflection peaks and distorting the backscattering level. We propose a method to reduce OFDR trace noise using elliptical arc fitting (EAF). The obtained results indicate that this algorithm efficiently processes both areas with and without contrasting back reflections, with zero distortion of Fresnel reflection peaks, and with zero attenuation error in regions without Fresnel reflections. At the same time, other methods distort reflection peaks by 14.2–42.6% and shift the correct level of Rayleigh scattering by 27.2–67.3%. Further work will be aimed at increasing the accuracy of the method and testing it with other types of data. Full article

Biochar has garnered considerable attention for its potential to improve soil properties due to its unique characteristics. However, the precise measurement of soil water content using electromagnetic sensors becomes challenging after biochar is incorporated. This study investigated the impact of biochar on soil water content measurement by adding biochar of varying dosages and particle sizes to a typical loess, under both room and subzero temperature conditions by using time domain reflectometry (TDR) and frequency domain reflectometry (FDR) techniques. The results demonstrate that biochar amendment significantly influenced the measurement accuracy of both TDR and FDR. A clear dosage-dependent relationship was observed, with measurement errors exhibiting progressive escalation as biochar addition rates increased. At room temperature, the root mean square error (RMSE) values for loess were remarkably low (TDR: 0.029; FDR: 0.093). In contrast, the 9% coarse-grained biochar-amended soil (BAS-9%C) showed substantially elevated RMSE values (TDR: 0.2006; FDR: 0.1468). Furthermore, comparative analysis revealed that particle size significantly affected measurement precision, with coarse-grained biochar demonstrating more pronounced interference effects than fine-grained biochar at equivalent application rates. At subzero temperatures, BAS-6%C exhibited significantly higher RMSE values (TDR: 0.1753; FDR: 0.2022) compared to BAS-6%F (TDR: 0.079; FDR: 0.1872). A dielectric mixing model was established for calculating the dielectric constant of BAS. In addition, calibration equations for accurately determining the water content of biochar-amended loess under both room and subzero temperature conditions were established. Furthermore, the mechanisms by which biochar influenced the performance of the TDR and FDR sensors are comprehensively discussed. These findings can provide valuable theoretical foundation and practical guidance for future soil improvement with biochar and accurate water content measurement in BAS. Full article

The degradation of subarctic peatland ecosystems under climate change impacts surrounding landscapes, carbon balance, and biogeochemical cycles. To assess these ecosystems’ responses to climate change, it is essential to consider not only the active-layer thickness but also its thermo-hydraulic conditions. Ground-penetrating radar is one of the leading methods for studying the active layer, and this paper proposes systematically investigating its potential to determine the thermal properties of the active layer. Collected experimental data confirm temperature hysteresis in peat linked to changes in water and ice content, which GPR may detect. Using palsa mires of the Kola Peninsula (NW Russia) as a case study, we analyze relationships between peat parameters in the active layer and search for thermal gradient responses in GPR signal attributes. The results reveal that frequency-dependent GPR attributes can delineate thermal intervals of ±1 °C through disperse waveguides. However, further verification is needed to clarify the conditions under which GPR can reliably detect temperature variations in peat, considering factors such as moisture content and peat structure. In conclusion, our study discusses the potential of GPR for remotely monitoring freeze–thaw processes and moisture distribution in frozen peatlands and its role as a valuable tool for studying peat thermal properties in terms of permafrost stability prediction. Full article

The phase-sensitive optical time domain reflectometry (Φ-OTDR) has been widely applied in various fields. However, due to fading noise, false alarms often occur; this limits its engineering applications. In this paper, a fading suppression method for Φ-OTDR systems based on multi-domain multiplexing (MDM) is proposed. The principles and limitations of existing suppression methods such as spatial-domain multiplexing (SDM), polarization-domain multiplexing (PDM), and frequency-domain multiplexing (FDM) are analyzed. The principle of MDM is explained in detail, and an experimental system is established to test the fading noise suppression capabilities of different parameter combinations of the PDM, FDM, and SDM methods. Experimental results show that it is difficult to comprehensively suppress fading noise with single-domain multiplexing. Through optimizations of different parameter combinations, MDM can comprehensively suppress fading noise by appropriately selecting the number of FDM frequencies, the spatial weighting intervals, and using PDM, thus obtaining the optimal anti-fading solution between performance and hardware complexity. Through MDM, the fade-free measurement is achieved, providing a promising technical solution for the practical application of the Φ-OTDR technology. Full article

In optical frequency domain reflectometry (OFDR), the random optical noise in Rayleigh backscattering and the sliding window length in the algorithm cause a trade-off between sensing spatial resolution and accuracy. This paper proposes a novel high-spatial-resolution high-accuracy OFDR distributed sensor based on a seamless femtosecond weak fiber Bragg grating (WFBG) array. Using femtosecond laser point-by-point (PbP) inscription technology, a 5 cm long seamless weak grating array was successfully fabricated on a polyimide fiber, consisting of ten 5 mm long WFBGs. The experimental results demonstrate that a sensing spatial resolution of 533 μm and a wavelength demodulation accuracy of ±2.05 pm were achieved for the first time. Full article

Coal mining disrupts soil structure and causes water loss, thereby affecting the ecological environment of mining areas. Rapid, accurate, and non-destructive detection of surface soil moisture is crucial for advancing ecological restoration in these regions. This study focuses on the mined and unmined areas of the Yushuquan coal mine, located on the southern slope of the Tianshan Mountains in Xinjiang, China. The soil volumetric water content (SVWC) was measured using time-domain reflectometry (TDR), while the shallow subsurface soil was investigated using ground-penetrating radar (GPR). Various features were extracted from GPR signals in both the time- and frequency-domains, and their relationships with SVWC were analyzed. Multiple features were selected and optimized to determine the optimal feature combination for building a multi-feature backpropagation neural network model for soil volumetric water content prediction (Muti-BP-SVWC). The performance of this model was compared with two single-feature-based methods for SVWC prediction: the average envelope amplitude (AEA) method and the frequency shift method. The application results of the Muti-BP-SVWC model in different regions demonstrated significant improvements in accuracy and stability compared to the AEA method and the frequency shift method. In the mined area validation set, the model achieved an determination coefficient (R²) of 0.77 and the root mean square error (RMSE) of 0.0091 cm³/cm³, while in the unmined area validation set, the R² of 0.84 and an RMSE of 0.0059 cm³/cm³. These results indicate that incorporating multiple features into the BP neural network can better capture the complex relationship between GPR signals and SVWC. This approach effectively inverts the shallow subsurface soil moisture in mining areas and provides valuable guidance for ecological restoration in these regions. Full article

Slope failures caused by changes in soil moisture content have become a growing global concern, resulting in significant loss of life and economic damage. To ensure the stability of slopes, it is necessary to accurately monitor the moisture content and understand the complex interactions between soil, water, and slope behavior. This paper provides a comprehensive overview of advanced soil moisture detection techniques for unsaturated soil slopes, including point-scale measurements and geophysical methods. It first introduces the fundamental concepts of the soil–water characteristic curve (SWCC) and its influence on the shear strength and stability of unsaturated soil slopes. It then delves into the working principles and applications of various point-scale measurement techniques, such as time-domain reflectometry (TDR), frequency-domain reflectometry (FDR), and neutron probe methods. Additionally, this paper explores the use of geophysiDear Editor: The author has checked that the name and affiliation are accuratecal methods, including ground-penetrating radar (GPR), electrical resistivity tomography (ERT), and electromagnetic induction (EMI), for the non-invasive assessment of soil moisture conditions and slope stability monitoring. This review highlights the advantages of integrating multiple geophysical techniques, combined with traditional geotechnical and hydrological measurements, to obtain a more comprehensive understanding of the subsurface conditions and their influence on slope stability. Several case studies are presented to demonstrate the successful application of this integrated approach in various slope monitoring scenarios. The continued advancement in these areas will contribute to the development of more accurate, reliable, and widely adopted solutions for the assessment and management of slope stability risks. Full article

Distributed strain sensing is a powerful tool for in situ structural health monitoring for a wide range of critical engineering infrastructures. Strain information from a single sensing device can be captured from multiple locations simultaneously, offering a reduction in hardware, wiring, installation costs, and signal analysis complexity. Fiber optic distributed strain sensors have been the widely adopted approach in this field, but their use is limited to lower strain applications due to the fragile nature of silica fiber. Coaxial cable sensors offer a robust structure that can be adapted into a distributed strain sensor. They can withstand greater strain events and offer greater resilience in harsh environments. This paper presents the developments in methodology for coaxial cable distributed strain sensors. It explores the two main approaches of coaxial cable distributed strain sensing such as time domain reflectometry and frequency domain reflectometry with applications. Furthermore, this paper highlights further areas of research challenges in this field, such as the deconvolution of strain and temperature effects from coaxial cable distributed strain sensor measurements, mitigating the effect of dielectric permittivity on the accuracy of strain measurements, addressing manufacturing challenges with the partial reflectors for a robust coaxial cable sensor, and the adoption of data-driven analysis techniques for interrogating the interferogram to eliminate concomitant measurement effects with respect to temperature, dielectric permittivity, and signal-to-noise ratio, amongst others Full article

Water pipelines in water diversion projects can leak, leading to soil deformation and ground subsidence, necessitating research into soil deformation monitoring technology. This study conducted model tests to monitor soil deformation around leaking buried water pipelines using distributed fiber optic strain sensing (DFOSS) technology based on optical frequency domain reflectometry (OFDR). By arranging strain measurement fibers in a pipe–soil model, we investigated how leak location, leak size, pipe burial depth, and water flow velocity affect soil strain field monitoring results. The results showed that pipeline leakage creates a “saddle-shaped” spatial distribution of soil strain above the pipeline, effectively indicating ground subsidence locations. When only one survey line is arranged, it is preferable to place the optical fiber directly above the pipeline. Surface monitoring fibers primarily detected tensile strain, with more pronounced peak values observed under conditions of larger leak size, higher flow velocity, shallow burial depth, and top-pipe leakage location. Monitoring fibers below the pipeline showed mainly unimodal distribution, with peak strain coinciding with the leak location. The sequential timing of strain changes at different fiber positions enabled the determination of soil seepage direction. This study demonstrates that DFOSS technology can provide important support for the early warning of such geological disasters. Full article

This paper presents, for the first time to the best of our knowledge, simultaneous temperature and relative humidity (RH) measurement using a machine learning (ML) model in Rayleigh-based Optical Frequency Domain Reflectometry (OFDR). The sensor unit consists of two segments: bare and polyimide-coated fibers, each with different sensitivities to temperature. The polyimide-coated fiber is RH-sensitive, unlike the bare fiber. We propose the ML approach to avoid manual post-processing data and maintain relatively high accuracy of the sensor. The root mean square error (RMSE) values for the 3 cm length of the sensor unit were 0.36 °C and 1.73% RH for temperature and RH, respectively. Furthermore, we investigated the impact of sensor unit lengths and number of data points on RMSE values. This approach eliminates the need for manual data processing, reduces analysis time, and enables accurate, simultaneous measurement of temperature and RH in Rayleigh-based OFDR. Full article

Reliable soil moisture information is essential for accurate irrigation scheduling. A wide range of soil moisture sensors are currently available on the market, but their performance needs to be evaluated as most sensors are calibrated under limited laboratory conditions. The aim of this study was to evaluate the performance of six commercially available moisture sensors (HydraProbe, Teros 10, Teros 11, EnviroPro, CS616 and Drill & Drop) and three tensiometers (Irrometer RSU-C-34, Teros 32 and Teros 21) and to establish calibration equations for a typical sandy soil of the Doñana National Park (Huelva, Spain). The calibration process for soil moisture sensors indicated differences between factory and corrected equations. All tested sensors improved with adjustments made to the factory calibration, except for the HydraProbe sensor which had a more accurate factory equation for a sandy soil. Among the various sensors tested, the Teros 10, Teros 11, and HydraProbe were found to be the easiest to install, typically positioned with an auger to prevent preferential pathways and ensure adequate sensor-soil contact. For tensiometers, the Teros 32 sensor requires specialized labor for its correct installation, as it must be positioned at a specific angle and maintained with distilled water. All tensiometers need a stabilization period after installation. Full article

Optimizing the use of existing high-voltage transmission lines demands real-time condition monitoring to ensure structural integrity and continuous service. Operating these lines at the current capacity is limited by safety margins based on worst-case weather scenarios, as exceeding these margins risks bringing conductors dangerously close to the ground. The integration of optical fibers within modern transmission lines enables the use of Distributed Fiber Optic Sensing (DFOS) technology, with Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry (CP-$\mathrm{\Phi }$OTDR) proving especially effective for this purpose. CP-$\mathrm{\Phi }$OTDR measures wind-induced vibrations along the conductor, allowing for an analysis of frequency-domain vibration modes that correlate with conductor length and sag across spans. This monitoring system, capable of covering distances up to 40 km from a single endpoint, enables dynamic capacity adjustments for optimized line efficiency. Beyond sag monitoring, CP-$\mathrm{\Phi }$OTDR provides robust detection of external threats, including environmental interference and mechanical intrusions, which could compromise cable stability. By simultaneously monitoring the Optical Phase Conductor (OPPC) and Optical Ground Wire (OPGW), this study offers the first comprehensive, real-time evaluation of both structural integrity and potential external aggressions on overhead transmission lines. The findings demonstrate that high-frequency data offer valuable insights for classifying mechanical intrusions and environmental interferences based on spectral content, while low-frequency data reveal the diurnal temperature-induced sag evolution, with distinct amplitude responses for each cable. These results affirm CP-$\mathrm{\Phi }$OTDR’s unique capacity to enhance line safety, operational efficiency, and proactive maintenance by delivering precise, real-time assessments of both structural integrity and external threats. Full article