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The present work is a comprehensive collection of recently published research articles on Structural Health Monitoring (SHM) campaigns performed by means of Distributed Optical Fiber Sensors (DOFS). The latter are cutting-edge strain, temperature and vibration monitoring tools with a large potential pool, namely their minimal intrusiveness, accuracy, ease of deployment and more. Its most state-of-the-art feature, though, is the ability to perform measurements with very small spatial resolutions (as small as 0.63 mm). This review article intends to introduce, inform and advise the readers on various DOFS deployment methodologies for the assessment of the residual ability of a structure to continue serving its intended purpose. By collecting in a single place these recent efforts, advancements and findings, the authors intend to contribute to the goal of collective growth towards an efficient SHM. The current work is structured in a manner that allows for the single consultation of any specific DOFS application field, i.e., laboratory experimentation, the built environment (bridges, buildings, roads, etc.), geotechnical constructions, tunnels, pipelines and wind turbines. Beforehand, a brief section was constructed around the recent progress on the study of the strain transfer mechanisms occurring in the multi-layered sensing system inherent to any DOFS deployment (different kinds of fiber claddings, coatings and bonding adhesives). Finally, a section is also dedicated to ideas and concepts for those novel DOFS applications which may very well represent the future of SHM.

Distributed optical fiber sensors (DOFS) offer unprecedented features, the most unique one of which is the ability of monitoring variations of the physical and chemical parameters with spatial continuity along the fiber. Among all these distributed sensing techniques, optical frequency domain reflectometry (OFDR) has been given tremendous attention because of its high spatial resolution and large dynamic range. In addition, DOFS based on OFDR have been used to sense many parameters. In this review, we will survey the key technologies for improving sensing range, spatial resolution and sensing performance in DOFS based on OFDR. We also introduce the sensing mechanisms and the applications of DOFS based on OFDR including strain, stress, vibration, temperature, 3D shape, flow, refractive index, magnetic field, radiation, gas and so on.

We live in an environment of ever-growing demand for transport networks, which also have ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that have surpassed their service lives. The commonly established alternative is increasing their durability by means of Structural Health Monitoring (SHM)-based maintenance and serviceability. Amongst the multitude of approaches to SHM, the Digital Twin model is gaining increasing attention. This model is a digital reconstruction (the Digital Twin) of a real-life asset (the Physical Twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter. This tool can provide infrastructure managers with functionalities to monitor and optimize their asset stock and to make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also include regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model. The technology can be even pushed as far as performing structural behavioral predictions and automatically compensating for them. The present exploratory review covers the key Digital Twin aspects—its usefulness, modus operandi, application, etc.—and proves the suitability of Distributed Sensing as its network sensor component.

The overlying rock layers in a mining area may collapse or settle, subjecting pipelines to uneven forces that can lead to deformation or even fracture. This paper proposes a pipeline safety monitoring method that combines fiberoptic vibration and strain sensing to detect vibrations and deformations caused by rock layer collapse in mining zones. First, pipeline deformation monitoring under unknown force directions was investigated using fiber Bragg grating (FBG) sensing technology. Second, we constructed a mining area pipeline model and conducted vibration/deformation monitoring tests employing FBG sensors, distributed Brillouin strain sensing, and distributed fiberoptic vibration sensing technologies. The experimental results demonstrate that FBG sensor arrays deployed at 90-degree intervals can effectively identify the pipeline’s primary force direction and maximum strain, with direction angle errors of less than 5.2%. The integrated analysis of vibration and strain data enables accurate identification and measurement of extended vibration responses and pipeline deformations in open-air zones. This study establishes a comprehensive monitoring framework for ensuring pipeline safety in mining areas.

Artificial intelligence (AI) plays a critical role in signal recognition of distributed sensor systems (DSS), boosting its applications in multiple monitoring fields. Due to the domain differences between massive sensors in signal acquisition conditions, such as manufacturing process, deployment, and environments, current AI schemes for signal recognition of DSS frequently encounter poor generalization performance. In this paper, an adaptive decentralized artificial intelligence (ADAI) method for signal recognition of DSS is proposed, to improve the entire generalization performance. By fine-tuning pre-trained model with the unlabeled data in each domain, the ADAI scheme can train a series of adaptive AI models for all target domains, significantly reducing the false alarm rate (FAR) and missing alarm rate (MAR) induced by domain differences. The field tests about intrusion signal recognition with distributed optical fiber sensors system demonstrate the efficacy of the ADAI scheme, showcasing a FAR of merely 4.3% and 0%, along with a MAR of only 1.4% and 2.7% within two specific target domains. The ADAI scheme is expected to offer a practical paradigm for signal recognition of DSS in multiple application fields.

Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines the Rayleigh, Brillouin, and Raman scattering mechanisms and integrates multiple DOFS systems in a single configuration, has attracted growing attention and has been developed rapidly. Compared to a single DOFS system, the multi-parameter measurements based on hybrid DOFS offer multidimensional valuable information to prevent misjudgments and false alarms. The highly integrated sensing structure enables more efficient and cost-effective monitoring in engineering. This review highlights the latest progress of the hybrid DOFS technology for multi-parameter measurements. The basic principles of the light-scattering-based DOFSs are initially introduced, and then the methods and sensing performances of various techniques are successively described. The challenges and prospects of the hybrid DOFS technology are discussed in the end, aiming to pave the way for a vaster range of applications.

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.

A dynamic filtering system that can choose in real time between two different noise filters depending on the dynamics of the measured environment is presented. Unlike other adaptive filters approaches, this system does not require prior knowledge of the environment beyond noise characteristics. We implemented this system into a Brillouin optical time-domain analysis (BOTDA) sensing scheme using a closed-circuit control system for dynamic tracking of the Brillouin Frequency Shift (BFS) along the sensing fiber using a Proportional-Integral-Derivative (PID) controller. Through experiments and numerical simulations, we compare this method to the filtering capabilities of P and PI controllers chosen as optimal in a previous work for closed-circuit BOTDA (CC-BOTDA). Results show that the adaptive noise filter provides a dynamic response comparable to the other controllers, while increasing noise suppression by a factor between 30% and beyond 100%, showing how an adaptive system can improve suppression with only knowledge of the measurement noise.

Distributed vibration sensing based on optical vector network analysis (OVNA) is a promising technique for measuring dynamic perturbations in optical fibers, but its practical use is limited by the high computational cost of short-time Fourier transform (STFT) and cross-correlation stages. In this work, we present a GPU-accelerated signal processing pipeline, together with an optimization strategy based on dataflow reduction, mixed-precision arithmetic, and hardware-aware tuning. The proposed implementation reduces the processing time for 200 sweeps from 64.7 s on a single-core CPU to 0.199 s on a modern GPU, while preserving the final shift results, with zero mismatches over 199,199 measurement points. Benchmarking across three GPU generations further shows that STFT benefits more from large on-chip cache resources, whereas cross-correlation scales more closely with memory bandwidth. These results suggest that modern GPUs can significantly reduce the computational burden of OVNA, as well as other distributed sensing methods with a similar processing flow, enabling kHz-rate aggregate throughput from batched processing, supporting real-time-oriented operation on modern GPUs.

Over the past three decades, extensive research activity on Brillouin scattering-based distributed optical fiber sensors has led to the availability of commercial instruments capable of measuring the static temperature/strain distribution over kilometer distances and with high spatial resolution, with applications typically covering structural and environmental monitoring. At the same time, the interest in dynamic measurements has rapidly grown due to the relevant number of applications which could benefit from this technology, including structural analysis for defect identification, vibration detection, railway traffic monitoring, shock events detection, and so on. In this paper, we present an overview of the recent advances in Brillouin-based distributed optical fiber sensors for dynamic sensing. The aspects of the Brillouin scattering process relevant in distributed dynamic measurements are analyzed, and the different techniques are compared in terms of performance and hardware complexity.