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Roth, B. Fiber Optic Sensors. Encyclopedia. Available online: https://encyclopedia.pub/entry/12768 (accessed on 19 April 2024).
Roth B. Fiber Optic Sensors. Encyclopedia. Available at: https://encyclopedia.pub/entry/12768. Accessed April 19, 2024.
Roth, Bernhard. "Fiber Optic Sensors" Encyclopedia, https://encyclopedia.pub/entry/12768 (accessed April 19, 2024).
Roth, B. (2021, August 04). Fiber Optic Sensors. In Encyclopedia. https://encyclopedia.pub/entry/12768
Roth, Bernhard. "Fiber Optic Sensors." Encyclopedia. Web. 04 August, 2021.
Fiber Optic Sensors
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This entry is adapted from the peer-reviewed paper 10.3390/s21154948

The last decade has seen rapid developments in the areas of carbon fiber technology, additive manufacturing technology, sensor engineering, i.e., wearables, and new structural reinforcement techniques. These developments, although from different areas, have collectively paved way for concrete structures with non-corrosive reinforcement and in-built sensors. This effort bridges the gap between civil engineering and sensor engineering communities through an overview on the up-to-date technological advances in both sectors, with a special focus on textile reinforced concrete embedded with Fiber Optic Sensors (FOSs). These have advantages over alternative concepts, such as, immunity to electromagnetic interference or light weight. The former is an important aspect for some civil infrastructure, i.e., railways where the lines are electrified. Thus, there has been in depth research on the feasibility and deployment of FOSs for SHM purposes. The characteristics desired for the ideal FOS for strain measurement in civil structures would include: adequate sensitivity and dynamic range; linear response; sensitivity to the direction of measurand field change; being single-ended, i.e., to minimize the number of leads; insensitivity to thermal fluctuations; non-perturbativity to the structure; immunity to power interruptions; ability to multiplex; ease of mass production, and durability for the entire lifetime of the structure.

textile reinforcement structural health monitoring fiber optic sensors smart sensing sensors in civil engineering reinforcement of structures

1. Introduction

The development of optical fiber saw a rapid increase in the 1990s for telecommunications purposes. Indeed, most of the optical sources, detectors, and related consumables that are still in use, i.e., connectors, couplers, etc., operate at the wavelengths that were initially focused on telecommunication windows, i.e., 1550 nm and 1300 nm. In recent years, fiber optics has also drawn considerable attention in the sensors field owing to its many advantages over the conventional electrical-based sensors, such as immunity to electromagnetic interference, multiplexing capability, high temperature capability, chemical inertness, robustness, and light weight. A FOS system consists of a transducer device, i.e., the sensing element or mechanism, a communication channel which carries the measurand data, and a subsystem that provides energy and detects/processes and conditions the received signal. Since several sensors can be embedded along a single fiber, creating a distributed sensors network, the detection of local damage such as strain, cracks, and corrosion, becomes a possibility. The conventional electrical sensors must have two wires for each sensor to form an electrical loop. In comparison, the multiplexing capability of the optical fiber network is far easier to implement and install. FOSs can be used either as point sensors or in a distributed sensor configuration, making the design of the sensor or the sensor network more flexible and application specific. Thus, depending on the transduction mechanisms used, a variety of physical and chemical sensors can be developed, which have potential applications in a wide range of industries.
The use of FOSs for the SHM of concrete was first suggested by Mendez et al. (1990) [1]. Since the first development from Mendez et al., many different FOSs have been conceived for SHM of concrete through the measurement of cracks [2], strains [3][4] as well as Relative Humidity (RH) [5] and pH [6][7], for instance. In general, the FOS concepts can be categorized into (i) integrated, (ii) point, (iii) quasi-distributed and (iv) distributed sensing, depending on the spatial distribution and sensing mechanism [8]. The different categories of FOSs and their corresponding sensing mechanisms are summarized in Figure 1, followed by an explanation on their operating mechanisms and their utilizations in exemplary applications.

Figure 1. Graphical illustration of different FOS concepts: integrated (a), point (b), quasi-distributed (c), and distributed (d). The black line illustrates the fiber optic link. The red and blue lines represent the sensing length and measurement points respectively.

2. Fiber Optic Sensors Embedded in Textile-Reinforced Concrete for Smart Structural Health Monitoring

The recent growth in the wearable sensor technology market, especially in the field of biomedical and robotics sectors, indicates a clear direction that could be predicted for civil infrastructure of the future. It can be inferred that textile reinforcement will take over its conventional counterparts, and civil infrastructure will be fully embedded with sensors and other capabilities to ensure its safety, comfort, and long-term durability while minimizing the otherwise expected carbon footprint. In order to achieve these targets, reliable and cost-effective sensors need to be in place. Much like the concrete reinforcement sector, the infrastructure monitoring sensors sector has also seen a dramatic shift towards new methods of sensing, i.e., adopting fiber optic sensors as opposed to conventional SHM schemes such as strain gauges.
In terms of functionalizing these reinforcement structures with sensors for SHM applications, FOSs are the best candidates due to their advantages, such as their small size, light weight, and remote interrogation and multiplexing capability. Optical fibers can be integrated during the manufacturing process of the TRCs in large volumes or could be tailor-made. Thus, such structures can be fabricated economically and optimized for specific target applications. When FCSs are equipped with optical fibers, Bremer et al. demonstrated in [9] that the corresponding sensor response depends linearly on the applied strain with a relatively low hysteresis and, depending on the integration direction, the load direction can be discriminated between transverse and longitudinal loads. Moreover, in terms of distributed sensing, it could also be verified that integrated optical fibers can be applied to determine the applied strain profile. However, depending on the magnitude of the applied load, the obtained load peak tends to broaden, and this reduces the spatial resolution. Therefore, in [10] it was deduced that for the FOS response, the bonding length between the optical fiber and the textile-base reinforcement structure is important, i.e., that the bonding length is long enough and thus avoids directional variations of the optical fiber at the point of measurement, so that the applied load can be sufficiently transferred on to the FOS. Moreover, it was demonstrated that when embedding the functionalized textile-based carbon reinforcement structure into highly alkaline concrete, the applied PVA coating, which is relatively inert, provides substantial resistance against deterioration of the structure [10]. In addition, functionalized TNS designed for the reinforcement and crack detection of concrete structures have been reported in [2]. The optical glass fiber was pre-strained during the fabrication process of the functionalized TNS in order to enhance the crack detection mechanism. Furthermore, when embedding the functionalized TNS into concrete, an insignificant light attenuation of 0.09 dB and a relatively high sensitivity of crack detection, i.e., of ≥1.4 mm, was observed. The performance of functionalized TNS and FCS are summarized for comparison in Table 1. Future directions of functionalized TRCs clearly point towards increased exploitation of Rayleigh, Brillouin, and/or Raman scattering for fiber-optic sensor networks, their implementation for structural health monitoring, as well as the combination with artificial intelligence algorithms for signal analysis and sensitivity enhancement, see e.g., [11][12]. In this context, several aspects are worth to be highlighted as they offer the potential to significantly enhance the performance of sensors based on laser-optical principles in comparison to state of the art technology. Firstly, the unique properties of laser radiation, as demonstrated already in many precision measurement applications from very diverse research fields, e.g. [13][14][15][16][17][18][19], are not fully exploited in civil infrastructure monitoring or embedded sensing and are expected to lead to a noticeable increase in measurement accuracy in the latter areas as well, in particular, in view of the continued rapid development in laser technology, e.g. [14]. Secondly, the trend towards miniaturized multiplexed sensing might open a route to the simultaneous determination of a multitude of physical or chemical quantities, e.g. strain, pressure, temperature, refractive index, humidity or pH value, with high spatial and temporal resolution [20][21][22][23]. Thirdly, the combination of integrated photonic concepts with ubiquitous read-out units [e.g.  [24][25] and/or artificial intelligence, i.e. from neuromorphic computing [26], could allow for real-time dynamic monitoring of large infrastructure in possibly harsh environments and ultimately lead to application-specific fiber-optic sensor networks for precision monitoring, surveillance or remote sensing as well as efficient predictive data analysis, see e.g. [11][12][27][28] and references therein. Also, these developments may help to reduce the resource and energy expenditure for large-scale distributed sensing architecture in versatile application scenarios which, in the end, could lead to transfer of such measurement systems to the mass market at affordable cost and equipment requirements.
Table 1. Comparison of functionalized TNS and FCS. Values obtained from [2][29][10].
Host Structure Textile Net Structures (TNS) Functionalized Carbon Structure (FCS)
Sensing mechanism Point-based (e.g., power meter) andDistributed (OTDR) Point-based (e.g., FBG, EFPI),Quasi-distributed (FBG network), andDistributed (e.g., DAS, OFDR, BOTDA)
Sensing parameter Cracks Strain
Sensitivity ≥1.4 mm 0.44 nm/%
Hysteresis Not specified 0.011%
Spatial resolution Not specified ≤0.5 m for distributed sensing
Fiber type Single-mode optical glass fiber Single-mode optical glass fiber
Optical attenuation ≤0.09 dB Insignificant light attenuation due to connector tolerances
Fabrication technology Reel-to-reel knitting technique Modified embroidery technique
Estimated cost Low Moderate
Highlight Pre-strained optical glass fiber Relatively inert due to PVA coating

References

  1. Mendez, A.; Morse, T.F.; Mendez, F. Applications of Embedded Optical Fiber Sensors in Reinforced Concrete Buildings and Structures. Proc. SPIE 1990, 1170, 60–70.
  2. Bremer, K.; Weigand, F.; Zheng, Y.; Alwis, L.S.; Helbig, R.; Roth, B. Structural Health Monitoring Using Textile Reinforcement Structures with Integrated Optical Fiber Sensors. Sensors 2017, 17, 345.
  3. Almubaied, O.; Chai, H.K.; Islam, R.; Lim, K.-S.; Tan, C.G. Monitoring Corrosion Process of Reinforced Concrete Structure Using FBG Strain Sensor. IEEE Trans. Instrum. Meas. 2017, 66, 2148–2155.
  4. Fan, L.; Bao, Y.; Chen, G. Feasibility of Distributed Fiber Optic Sensor for Corrosion Monitoring of Steel Bars in Reinforced Concrete. Sensors 2018, 18, 3722.
  5. Bremer, K.; Meinhardt-Wollweber, M.; Thiel, T.; Werner, G.; Sun, T.; Grattan, K.; Roth, B. Sewerage tunnel leakage detection using a fibre optic moisture-detecting sensor system. Sens. Actuators A Phys. 2014, 220, 62–68.
  6. Dantan, N.; Habel, W.R.; Wolfbeis, O.S. Fiber optic pH sensor for early detection of danger of corrosion in steel-reinforced concrete structures. In Proceedings of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, San Diego, CA, USA, 16 May 2005; pp. 274–284.
  7. Nguyen, T.H.; Venugopala, T.; Chen, S.; Sun, T.; Grattan, K.; Taylor, S.E.; Basheer, P.M.; Long, A.E. Fluorescence based fiber optic pH sensor for the pH 10–13 range suitable for corrosion monitoring in concrete structures. Sens. Actuators B Chem. 2014, 191, 498–507.
  8. Lopez-Higuera, J.M.; Cobo, L.R.; Incera, A.Q.; Cobo, A. Fiber Optic Sensors in Structural Health Monitoring. J. Lightwave Technol. 2011, 29, 587–608.
  9. Bremer, K.; Alwis, L.S.M.; Weigand, F.; Kuhne, M.; Zheng, Y.; Krüger, M.; Helbig, R.; Roth, B. Evaluating the Performance of Functionalized Carbon Structures with Integrated Optical Fiber Sensors under Practical Conditions. Sensors 2018, 18, 3923.
  10. Bremer, K.; Alwis, L.S.M.; Zheng, Y.; Weigand, F.; Kuhne, M.; Helbig, R.; Roth, B. Durability of Functionalized Carbon Structures with Optical Fiber Sensors in a Highly Alkaline Concrete Environment. Appl. Sci. 2019, 9, 2476.
  11. Bao, Y.; Chen, Z.; Wei, S.; Xu, Y.; Tang, Z.; Li, H. The State of the Art of Data Science and Engineering in Structural Health Monitor. Engineering 2019, 5, 234–242.
  12. Liehr, S.; Jäger, L.A.; Karapanagiotis, C.; Münzenberger, S.; Kowarik, S. Real-time dynamic strain sensing in optical fibers using artificial neural networks. Opt. Express 2019, 27, 7405–7425.
  13. Abbott, B.P. et al. (LIGO Scientific Collaboration and Virgo Collaboration); Observation of gravitational waves from a binary black hole merger. Physical Review Letters 2016, 116(6), 061102.
  14. Picque, N.; Hänsch, T.W.; Frequency comb spectroscopy. Nature Photonics 2016, 13, 146.
  15. Schiller, S.; Roth, B.; Lewen, F.; Ricken, O.; Wiedner, M.C.; Ultra-narrow-linewidth continuous-wave THz sources based on multiplier chains. Applied Physics B 2009, 95 (1), 55-61.
  16. Baum, G.; Pavlovic, N.; Roth, B.; Bartschat, K.; Fang, Y.; Bray, I.; Detailed experimental and theoretical study of elastic scattering at intermediate energies in the electron-cesium system. Physical Review A 2002, 66 (2), 022705.
  17. Roth, B.; Offenberg, D.; Zhang, C.B.; Schiller, S.; Chemical reactions between cold trapped Ba+ ions and neutral molecules in the gas phase. Physical Review A 2008, 78 (4), 042709.
  18. Offenberg, D.; Wellers, C.; Zhang, C.B.; Roth, B.; Schiller, S.; Measurement of small photodestruction rates of cold, charged biomolecules in an ion trap. Journal of Physics B: Atomic, Molecular and Optical Physics 2009, 42 (3), 035101.
  19. Fröhlich, U.; Roth, B.; Antonini, P.; Lämmerzahl, C.; Wicht, A.; Schiller, S.. Ultracold Trapped Molecules: Novel Systems for Tests of the Time-Independenceof the Electron-to-Proton Mass Ratio; Karshenboim, S.G.; Peik, E., Eds.; Lecture Notes in Physics; Astrophysics, Clocks and Fundamental Constants: Berlin Heidelberg (Springer), 2004; pp. 297-307.
  20. Dincer, C.; Bruch, R.; Kling, A.; Dittrich, P.S.; Urban, G.A.; Multiplexed Point-of-Care Testing – xPOCT. Trends in Biotechnology 2017, 35, 728–742.
  21. Walter, J.-G.; Eilers, A.; Alwis, L.S.M.; Roth, B.W.; Bremer, K.; SPR Biosensor Based on Polymer Multi-Mode Optical Waveguide and Nanoparticle Signal Enhancement. Sensors 2020, 20, 2889.
  22. Schlangen, S.; Bremer, K.; Zheng, Y.; Böhm, S.; Steinke, M.; Wellmann, F.; Neumann, J.; Roth, B.; Overmeyer, L.; Long-Period Gratings in Highly Germanium-Doped, Single-Mode Optical Fibers for Sensing Applications. Sensors 2018, 18, 1363.
  23. Rusling, J.F.; Kumar, C.V.; Gutkinde, J.S..; Patele, V.; Measurement of biomarker proteins for point-of-care early detection and monitoring of cancer. Analyst 2010, 135, 2496-2511.
  24. Bremer, K.; Roth, B.; Fibre optic surface plasmon resonance sensor system designed for smartphones. Optics Express 2015, 23, 17179–17184..
  25. Walter, J.-G.; Alwis, L.S.M.; Roth, B.; Bremer, K.; All-Optical Planar Polymer Waveguide-Based Biosensor Chip Designed for Smartphone-Assisted Detection of Vitamin D. Sensors 2020, 20, 6771.
  26. Zhang, Y.; Qu, P.; Ji, Y.; Zhang, W.; Gao, G.; Wang, G.; Song, S.; Li, G.; Chen, W.; Zheng, W.; et al.Chen, F.Pei, J.Zhao, R.Zhao, M., Shi, L. A system hierarchy for brain-inspired computing. Nature 2020, 586, 378–384.
  27. Hartog, A.H.. An Introduction to Distributed Optical Fibre Sensors; Mendez, A., Eds.; CRC Press: Boca Raton, 2017; pp. 1-427.
  28. Kalam Azad, A.; Wang, L.; Guo, N.; Tam, H.-Y.; Lu, C.; Signal processing using artificial neural network for BOTDA sensor system. Optics Express 2016, 24(6), 6769-82.
  29. Montanini, R.; Recupero, A.; De Domenico, F.; Freni, F. Strain Sharing Assessment in Woven Fiber Reinforced Concrete Beams Using Fiber Bragg Grating Sensors. Sensors 2016, 16, 1564.
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