Search Results (198)

Interferometric fiber optic hydrophones (IFOHs) are highly sensitive for underwater acoustic detection but face challenges owing to the trade-off between laser monochromaticity and coherence length. In this study, we propose a pseudo-random binary sequence (PRBS) phase modulation method for laser coherence length control, establishing the first theoretical model that quantitatively links PRBS parameter to coherence length, elucidating the mechanism underlying its suppression of parasitic interference noise. Furthermore, our research findings demonstrate that while reducing the laser coherence length effectively mitigates parasitic interference noise in IFOHs, this reduction also leads to elevated background noise caused by diminished interference visibility. Consequently, the modulation of coherence length requires a balanced optimization approach that not only suppresses parasitic noise but also minimizes visibility-introduced background noise, thereby determining the system-specific optimal coherence length. Through theoretical modeling and experimental validation, we determined that for IFOH systems with a 500 ns delay, the optimal coherence lengths for link fibers of 3.3 km and 10 km are 0.93 m and 0.78 m, respectively. At the optimal coherence length, the background noise level in the 3.3 km system reaches −84.5 dB (re: rad/√Hz @1 kHz), representing an additional noise suppression of 4.5 dB beyond the original suppression. This study provides a comprehensive theoretical and experimental solution to the long-standing contradiction between high laser monochromaticity, stability and appropriate coherence length, establishing a coherence modulation noise suppression framework for hydrophones, gyroscopes, distributed acoustic sensing (DAS), and other fields. Full article

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article

Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks. Full article

Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article

Free space optics (FSO) aims to perform as one of the best optical wireless channels to design a reliable, flexible, and cost-effective communication system. In FSO systems, mode-division multiplexing (MDM) transmission is a proven technique to expand transmission capacity per communication link. Thus, a 16 × 10 Gbps MDM-FSO system using fiber Bragg grating (FBG) sensors for the coexistence of communication and sensing, exploiting FSO links to transmit distinct Laguerre-Gaussian (LG) beams at a 1000–1900 m range, is proposed. The results illustrate that the system can transmit higher-order LG beams with sensor temperatures of 20–120 °C over a 1500 m range under clear air, drizzle, and moderate haze weather. Also, an improved performance is achieved in gamma–gamma compared to the log-normal distribution model for 10⁻⁶–10^−2.5 index modulation under weak-to-strong turbulence. The proposed system is capable of offering a high optical signal-to-noise ratio (OSNR) and gain of 113.39 and 15.43 dB, respectively, at an aggregate data rate of 160 Gbps under different atmospheric scenarios. Moreover, the proposed system achieves better system performance compared to existing works. Full article

Fiber optic distributed acoustic sensing (DAS) technology can monitor vibrational strain of vast areas with fine spatial resolution at high sampling rates. The fiber optic cable portion of DAS may directly monitor, measure, and map potentially unsafe areas such as thin ice sheets. Once the fiber optic cable is emplaced, DAS can provide “rapid-response” information along the cable’s length through remote sampling. A field campaign was performed to test the sensitivity of DAS to spatial variations within thin ice sheets. A pilot field study was conducted in the northeastern United States in which fiber-optic cable was deployed on the surface of a freshwater pond. Phase velocity transformations were used to analyze the DAS response to strike testing on the thin ice sheet. The study results indicated that the ice sheet was about 5 cm thick generally, tapering to about 3.5 cm within 2 m of the pond’s edge and then disappearing at the margins. After validation of the pilot study’s methodology, a follow-up experiment using DAS to collect on a rapidly deployed, surface-laid cable atop a larger freshwater pond was conducted. Using phase velocity transformations, the ice thickness along the fiber optic cable was estimated to be between 25.5 and 28 cm and confirmed via ice auger measurements along the fiber optic cable. This field campaign demonstrates the feasibility of employing DAS systems to remotely assess spatially variable properties on thin freshwater ice sheets. Full article

The status of an optic–electric composite high-voltage submarine cable (referred to as submarine cable) can be monitored based on optical fiber-distributed sensing technology, and at the same time, no additional sensor is needed in the monitoring system. Currently, this technology is widely used in submarine cable monitoring systems. To estimate the temperatures of conductor and XLPE (cross-linked polyethylene) insulation of the submarine cable based on the ambient temperature and optical fiber temperature, the thermoelectric coupling field model of the 110 kV single-core submarine cable is established and validated. The thermoelectric coupling field models of the submarine cable with different values of ambient temperature and ampacity are built, and the influence of ambient temperature and ampacity on the temperatures of conductor, insulation and optical fiber is investigated. Furthermore, the relationship between the temperatures of the conductor and insulation and the ambient temperature and optical fiber temperature is obtained. Then, estimation formulas for temperatures of conductor and insulation of submarine cable according to ambient temperature and optical fiber temperature are obtained and preliminarily validated. This work lays the foundation for condition evaluation of the submarine cable insulation, life expectancy and maximum allowable ampacity estimation. Full article

Structural health monitoring (SHM) plays a vital role in ensuring the safety, durability, and performance of civil infrastructure. This review delves into the significant advancements in optical fiber sensor (OFS) technologies such as Fiber Bragg Gratings, Distributed Temperature Sensing, and Brillouin-based systems, which have emerged as powerful tools for enhancing SHM capabilities. Offering high sensitivity, resistance to electromagnetic interference, and real-time distributed monitoring, these sensors present a superior alternative to conventional methods. This paper also explores the integration of OFSs with Artificial Intelligence (AI), which enables automated damage detection, intelligent data analysis, and predictive maintenance. Through case studies across key infrastructure domains, including bridges, tunnels, high-rise buildings, pipelines, and offshore structures, the review demonstrates the adaptability and scalability of these sensor systems. Moreover, the role of SHM is examined within the broader context of civil and urban infrastructure, where IoT connectivity, AI-driven analytics, and big data platforms converge to create intelligent and responsive infrastructure. While challenges remain, such as installation complexity, calibration issues, and cost, ongoing innovation in hybrid sensor networks, low-power systems, and edge computing points to a promising future. This paper offers a comprehensive amalgamation of current progress and future directions, outlining a strategic path for next-generation SHM in resilient urban environments. Full article

Distributed optical fiber sensing (DOFS) is an advanced tool for structural health monitoring (SHM), offering high precision, wide measurement range, and real-time as well as long-term monitoring capabilities. It enables real-time monitoring of both temperature and strain information along the entire optical fiber line, providing a novel approach for safety monitoring and structural health assessment in transportation engineering. This paper first introduces the fundamental principles and classifications of DOFS technology and then systematically reviews the current research progress on Brillouin scattering-based DOFS. By analyzing the monitoring requirements of various types of transportation infrastructure, this paper discusses the applications and challenges of this technology in SHM and damage detection for roads, bridges, tunnels, and other infrastructure, particularly in identifying and tracking cracks, deformations, and localized damage. This review highlights the significant potential and promising prospects of Brillouin scattering technology in transportation engineering. Nevertheless, further research is needed to optimize sensing system performance and promote its widespread application in this field. These findings provide valuable references for future research and technological development. Full article

This review examines the application of the geophysical methods for Transportation Infrastructure Slope Monitoring (TISM). In contrast to existing works, which address geophysical methods for natural landslide monitoring, this study focuses on their application to infrastructure assets. It addresses the key aspects regarding the geophysical methods most employed, the subsurface properties revealed, and the design of monitoring systems, including sensor deployment. It evaluates the benefits and challenges associated with each geophysical approach, explores the potential for integrating geophysical techniques with other methods, and identifies the emerging technologies. Geophysical techniques such as Electrical Resistivity Tomography (ERT), Multichannel Analysis of Surface Waves (MASW), and Fiber Optic Cable (FOC) have proven effective in monitoring slope stability and detecting subsurface features, including soil moisture dynamics, slip surfaces, and material heterogeneity. Both temporary and permanent monitoring setups have been used, with increasing interest in real-time monitoring solutions. The integration of advanced technologies like Distributed Acoustic Sensing (DAS), UAV-mounted sensors, and artificial intelligence (AI) promises to enhance the resolution, accessibility, and predictive capabilities of slope monitoring systems. The review concludes with recommendations for future research, emphasizing the need for integrated monitoring frameworks that combine geophysical data with real-time analysis to improve the safety and efficiency of transportation infrastructure management. Full article

Detecting partial discharges in cable joints is critical for timely defect identification and reliable transmission system operation. To improve the long-term reliability and sensitivity of the sensing system, a novel method for cable joint monitoring based on implanting optical fibers within the joint structure is proposed. The electric field distribution of the optical fiber-implanted cable joint was simulated, followed by electrical performance tests, demonstrating that optical fiber implantation had a negligible effect on the electrical properties of the cable joint. A platform utilizing Mach–Zehnder–Sagnac (MZ–Sagnac) interferometry was developed to evaluate the frequency response of the implanted optical fiber sensor, with calibration performed on a non-standard curved surface. The results show that the average sensitivity of the sensor in the 10 kHz–80 kHz range is 71.6 dB, 2.0 dB higher than that of the piezoelectric transducer, with a maximum signal-to-noise ratio of 65.2 dB. To simulate common fault conditions in the actual operation of cable joints, four types of discharge defects were introduced. Partial discharge tests conducted on an optical fiber-implanted cable joint, supplemented by measurements using a partial discharge detector, demonstrate that the optical fiber sensors can detect a minimum discharge of 16.0 pC. Full article

Acoustic sensing has many applications in engineering, one of which is fiber-optic hydrophones (FOHs). Conventional piezoelectric hydrophones face limitations related to size, electromagnetic interference, corrosion, and narrow operating bandwidth. Fiber-optic hydrophones, particularly those employing distributed feedback (DFB) lasers, offer a compelling alternative due to their mechanical flexibility, resistance to harsh conditions, and broad detection range. DFB lasers are highly sensitive to external perturbations such as temperature and strain, enabling the precise detection of underwater acoustic signals by monitoring the resultant shifts in lasing wavelength. This paper presents an enhanced interrogation mechanism that leverages Mach–Zehnder interferometers to translate wavelength shifts into measurable phase deviations, thereby providing cost-effective and high-resolution phase-based measurements. A dual interferometric setup is integrated with a standard demodulation algorithm to extend the dynamic range of these sensing systems. The experimental results demonstrate a substantial improvement in performance, with the dynamic range increasing from 125 dB to 139 dB at 1 kHz without degrading the noise floor. This enhancement significantly expands the utility of FOH-based systems in underwater environments, supporting applications such as underwater surveillance, submarine communication, and marine ecosystem monitoring. Full article

The distribution of temperature-induced cracks in mass concrete structures is extensive and random, making it difficult for existing detection methods to accurately identify the specific location and initiation time of cracking. Therefore, there is an urgent need for an intelligent, precise, and efficient monitoring approach capable of acquiring real-time information on the evolution of the internal temperature field in concrete structures during their early-age curing process. A novel temperature-sensitive concrete system was developed by synchronously integrating distributed optical fibers with three-dimensional printed concrete (3DPC) to enable both temperature monitoring and signal transmission. To validate the effectiveness of the proposed method, experimental testing and numerical simulations were conducted on cubic 3D-printed fiber-reinforced concrete to analyze the temporal evolution of their internal temperature fields. The results show that, during the system calibration process, the temperature measured by the distributed temperature sensing (DTS) system was highly consistent with the environmental temperature curve, with fluctuations controlled within ±1 °C. In addition, the numerical simulation results closely aligned with the experimental data, with discrepancies maintained within 5%, demonstrating the feasibility of utilizing 3D printing technology to impart temperature sensitivity to concrete materials. This integrated approach offers a promising pathway for advancing smart concrete technology, providing an effective solution for accurate sensing and control of internal temperatures in concrete structures. It holds substantial potential for practical applications in civil engineering projects. Full article

Thermal Integrity Profiling (TIP) effectively monitors concrete integrity. TIP detects structural defects and locates them through anomalies in hydration–temperature curves. However, TIP alone cannot accurately identify the defect types. To resolve this limitation, strain monitoring is integrated with TIP. A dual-parameter temperature–strain monitoring system using fiber-optic sensing was implemented in a diaphragm wall of Weizishan Station, Jinan. This study investigated the spatial distribution and variation patterns of concrete temperature–strain during the different stages of hydration. A thermal–mechanical–chemical multi-field coupling model was established based on the concrete mix ratio and the theoretical thermal parameters, and its feasibility was verified. This study also analyzed the impact mechanisms of four defect types—voids, mud inclusions, necking, and widening—on the surrounding concrete’s heat release and deformation during hydration. It presents specific hydration–temperature–strain characteristic curves that can accurately differentiate the defect types and established the correspondence between the defect types and these characteristic patterns. Finally, a rapid and accurate defect identification process is proposed for practical application, improving efficiency and precision in detecting anomalies. The findings provide a reference for implementing appropriate defect prevention and remediation measures on-site and hold promise for enhancing the prediction and control of defects during the hydration period of concrete structures. Full article

Addressing the critical technological needs for the real-time monitoring of stress distribution in mining areas, a new method for inverting goaf pressure using distributed optical fiber monitoring data is proposed. By coupling the key stratum fracture mechanics model with the subsidence trajectory function model, a theoretical model is established to accurately describe spatial stress evolution during coal mining. The model quantifies the relationship between goaf pressure changes and key stratum failures through a two-stage analysis of the subsidence process, based on distinct mechanical properties before and after key stratum fracture. Physical model experiments (3 m × 0.2 m × 1.1 m) using Brillouin Optical Time Domain Analysis (BOTDA) technology validated the proposed method, with comprehensive monitoring of key stratum deformations. By coupling the fracture mechanics model of the critical layer and the settlement trajectory function model, the dynamic transformation of the pre-fracture and post-fracture stages is realized, and the stress evolution can be monitored and predicted in real time. The results demonstrate spatial consistency between key stratum fracture locations and goaf peak stress positions. High-precision optical fiber sensing detected an ultimate strain threshold of 4000 με for key stratum failure, with pre-fracture strain measurements consistently below this threshold. The developed stress inversion formula successfully predicted pressure distribution patterns within the goaf, achieving real-time monitoring capabilities. Compared with the BPPS measurements, the deviation in the inverted data is less than 8.88%, the root mean square error (RMSE) is 0.98–1.20 in different propulsion stages, and the coefficient of determination (R²) is between 0.72 and 0.85. These findings provide a crucial theory for predicting peak stress evolution in mining areas, with implications for improving safety monitoring systems and optimizing mining operations. Full article