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.

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 implementation of structural health monitoring systems in civil engineering structures already in the construction phase could contribute to safer and more resilient infrastructure. Due to their lightweight, small size and high resistance to the environment, distributed optical fibre sensors stand out as a very promising technology for damage detection and quantification in reinforced concrete structures. In this article, the suitability of embedding robust distributed optical fibre sensors featuring a protective sheath to accurately assess the performance indicators, in terms of vertical deflection and crack width, of three reinforced concrete beams subjected to four-point bending is investigated. The results revealed that a certain strain attenuation occurs in embedded robust distributed optical fibre sensors compared to commonly used thin polyimide-coated distributed optical fibre sensors bonded to steel reinforcement bars. However, the presence of the protective sheath prevented the appearance of strain reading anomalies which has been a frequently reported issue. Performance wise, the robust distributed optical fibre sensors were able to provide a good estimate of the beam deflections with errors of between 12.3% and 6.5%. Similarly, crack widths computed based on distributed optical fibre sensor strain measurements differed by as little as ±20 µm with results from digital image correlation, provided individual cracks could be successfully detected in the strain profiles. Finally, a post-processing procedure is presented to generate intuitive contour plots that can help delivering critical information about the element’s structural condition in a clear and straightforward manner.

Distributed optical fiber sensors are measuring tools whose potential related to the civil engineering field has been discovered in the latest years only (reduced dimensions, easy installation process, lower installation costs, elevated reading accuracy, and distributed monitoring). Yet, what appears clear from numerous in situ distributed optical fiber sensors monitoring campaigns (bridges and historical structures among others) and laboratory confined experiments is that optical fiber sensors monitorings have a tendency of including in their outputs a certain amount of anomalistic readings (out of scale and unreliable measurements). These can be both punctual in nature and spread over all the monitoring duration. Their presence strongly affects the results both altering the data in its affected sections and distorting the overall trend of the strain evolution profiles, thus the importance of detecting, eliminating, and substituting them with correct values. Being this issue intrinsic in the raw output data of the monitoring tool itself, its only solution is computer-aided post-processing of the strain data. This article discusses different simple algorithms for getting rid of such disruptive anomalies using two methods previously used in the literature and a novel polynomial-based one with different levels of sophistication and accuracy. The viability and performance of each are tested on two study case scenarios: an experimental laboratory test on two reinforced concrete tensile elements and an in situ tunnel monitoring campaign. The outcome of such analysis will provide the reader with both clear indications on how to purge a distributed optical fiber sensors-extracted data set of all anomalies and on which is the best-suited method according to their needs. This marriage of computer technology and cutting edge structural health monitoring tool not only elevates the distributed optical fiber sensors viability but also provides civil and infrastructures engineers a reliable tool to perform previously unreachable levels of accuracy and extension monitoring coverage.

Distributed Optical Fiber Sensors (DOFSs), thanks to their multiple sensing points, are ideal tools for the detection of deformations and cracking in reinforced concrete (RC) structures, crucial as a means to ensure the safety of infrastructures. Yet, beyond a certain point of most DOFS-monitored experimental tests, researchers have come across unrealistic readings of strain which prevent the extraction of further reliable data. The present paper outlines the results obtained through an experimental test aimed at inducing such anomalies to isolate and identify the physical cause of their origin. The understanding of such a phenomenon would enable DOFS to become a truly performant strain sensing technique. The test consists of gradually bending seven steel reinforcement bars with a bonded DOFS under different conditions such as different load types, bonding adhesives, bar sections and more. The results show the bonding adhesives having an influence on the DOFS performance but not on the rise of anomalies while the reasons triggering the latter are narrowed down from six to two, reaching a strain threshold and a change in structure’s deformative behavior. Further planned research will allow identification of the cause behind the rise of strain-reading anomalies.