Open Access

In this paper, we use distributed fiber optic sensing (DFOS) for traffic load monitoring on a newly constructed bridge equipped with a network of embedded optical fibers. Specifically, the optical fibers are embedded longitudinally in all bridge girders at two depths, forming a looped sensing configuration that enables the measurement of different stress states when a vehicle passes. The measurements presented here were conducted before the bridge was opened to traffic, using a mobile crane weighing approximately 36 metric tons, with the load distributed evenly across three axles. We show that our DFOS system can effectively capture the bridge structural response to the moving mobile crane and provide estimates of axle weight distribution and spacing. Finally, the results indicate that, although the fibers located in the girders beneath the loaded lane are strongly affected by the traffic, the fibers beneath the unloaded lane show only a negligible response, highlighting the ability to separate the effects of vehicles traveling in neighboring lanes.

Wavelength-scanning optical time-domain reflectometry (WS-COTDR) is a phase-sensitive distributed sensing technique in which the wavelength of the probe pulses is swept over a defined range rather than kept fixed. Recording the backscattered traces at each wavelength enables reconstruction of the local reflection spectrum at each position along the fiber. Environmental perturbations modify the fiber’s local properties and induce measurable spectral shifts, allowing quantitative retrieval of strain or temperature changes (Liehr 2018). Because the sensing mechanism relies on spectral shifting rather than optical phase of the backscattered light, WS-COTDR is inherently immune to fading and offers a comparatively simple system architecture. These advantages have driven rapid development since its initial demonstration. Recent progress has substantially enhanced the performance of WS-COTDR. Polarization-diversity detection has greatly reduced large measurement errors originating from cross-correlation of reflection spectra (Lu 2025). Hybrid configurations that combine WS-COTDR with conventional φOTDR overcome the limited frequency response range of the former technique (Lu 2024). Advanced signal-processing approaches, including machine-learning methods, have improved wavelength-scanning linearity (Liehr 2018), accelerated data processing (Liehr 2019), and suppressed detection noise, enabling sensing distances beyond 100 km (Liehr 2020). WS-COTDR has also matured into a practical tool for field applications. It has been deployed for traffic monitoring using dark fibers along roads and bridges (Liehr 2018, Lu 2025), and it has demonstrated reliable leakage localization in gas-storage boreholes (Lu 2021). Its strong sensitivity at low frequencies makes it particularly suitable for monitoring slow ground motion. Field trials have captured blast-induced vibrations and indicate the capability to detect long-term subsidence over multi-month periods.

Distributed fiber optic sensors (DFOSs) based on Brillouin scattering have progressed significantly over the past decades and have seen many applications, particularly in the field of structural health monitoring. These applications often benefit from the capability to measure multiple parameters simultaneously, such as temperature, strain, and humidity. However, accurately measuring even a single parameter becomes challenging when the fiber is subjected to simultaneous changes in multiple parameters. This issue is known in the literature as cross-sensitivity. While solutions to mitigate cross-sensitivity in Brillouin DFOSs have been reported, they often increase the overall system’s cost or complexity by requiring the use of two optical fibers or specialty fibers. This thesis reports on the development of a machine learning-assisted Brillouin optical frequency domain analysis (BOFDA) system for simultaneous measurements of two or more parameters, including temperature, strain, and humidity. First, a BOFDA system capable of obtaining high signal-to-noise ratio (SNR) multipeak Brillouin gain spectra from a Standard telecom optical fiber is presented. These spectra are used to extract features and train simple and robust machine learning models to simultaneously predict temperature and strain. Next, the ability of BOFDA to monitor humidity using a polyimide (PI)-coated optical fiber is demonstrated. This is the first application of BOFDA for humidity sensing and one of the few in general for DFOSs. The machine learning-assisted BOFDA is also shown to be effective in discriminating humidity and temperature in a single optical fiber using a similar methodology as for temperature and strain discrimination. Finally, by combining These methods, a solution for simultaneously measuring all three parameters (temperature, strain, and humidity) using standard acrylate-coated and PI-coated optical fibers is reported. All experiments were conducted in the lab under controlled temperature and relative humidity (RH) conditions, generally ranging from 20 ◦C to 60 ◦C and from 20% to 80%, respectively. However, the reported methodologies are not inherently limited to these ranges. Compared to time-domain systems, BOFDA is a cost-effective solution; however, its measurement time is significantly longer, often by up to two orders of magnitude. Although the main focus of the thesis is to address cross-sensitivity, the issue of long measurement time could hinder many applications and is therefore also considered here. It is shown that convolutional neural networks have the potential to significantly reduce the BOFDA measurement time, alleviating the major drawback of BOFDA. However, this solution has been developed only for single-parameter sensing. The development of a time-efficient multiparameter BOFDA sensor is seen as a promising direction for future research. The outcome of this research has significant potential for applications in structural Health monitoring. Simultaneous multiparameter sensing with BOFDA could improve the safety, efficiency, and service life of critical infrastructure, such as bridges, tunnels, dams, highways, and submarine power cables.

Kunststoffe sind aufgrund ihrer hervorragenden gewichtsspezifischen Eigenschaften auch künftig für zahlreiche technische Anwendungen unverzichtbar. Eine funktionierende Kreislaufwirtschaft ist jedoch entscheidend, um ihren Einsatz nachhaltig und ressourceneffizient zu gestalten. Bei der Wiederverwertung wird zwischen mechanischem, chemischem und thermischem Recycling unterschieden, wobei die beiden erstgenannten grundsätzlich vorzuziehen sind. Eine Grundvoraussetzung für jedes erfolgreiche Recycling ist die Sortierung in sortenreine Monofraktionen. In der industriellen Kunststoffsortierung kommen derzeit vor allem spektroskopische Verfahren auf Basis der Nahinfrarotspektroskopie (NIR) zum Einsatz. Dabei werden Transmissions- oder Reflexionsspektren zur Identifikation der Polymerarten genutzt. NIR-basierte Systeme stoßen jedoch an ihre Grenzen, wenn Additive oder Pigmente – insbesondere Ruß – enthalten sind. Schwarz eingefärbte Kunststoffe („black plastics“) absorbieren die NIR-Strahlung stark und können daher nicht zuverlässig detektiert werden. Sie werden folglich häufig nicht dem stofflichen Recycling zugeführt, sondern energetisch verwertet oder, sofern erlaubt, deponiert. Unter den zerstörungsfreien Verfahren bieten die laserinduzierte Plasmaspektroskopie (LIBS) sowie die im Vergleich zur NIR-Spektroskopie langwelligere Terahertz-Spektroskopie (THz) hier vielversprechende Alternativen. Beide Verfahren ermöglichen eine Identifikation von Kunststoffen anhand ihrer materialspezifischen spektralen Eigenschaften. Mithilfe geeigneter Datenverarbeitung und Klassifikationsalgorithmen ist so eine Erkennung unabhängig von Farbe oder Rußanteil möglich. In dieser Arbeit werden Mess- und Auswertemethodik von LIBS und THz vorgestellt, deren Klassifikationsergebnisse an Kunststoffen gegenübergestellt und ihr Potenzial für eine nachhaltige Kreislaufwirtschaft bewertet. Neben theoretischen Grundlagen werden aktuelle Anwendungsbeispiele präsentiert und Perspektiven für zukünftige Entwicklungen aufgezeigt.

We present our research work on the condition monitoring of hydrogen storage composite pressure vessels using distributed fibre optic sensors. The sensing fibres are integrated into the composite structure by wrapping them over the polymer liner in the helical and circumferential direction during the manufacturing process of the carbon fibre reinforced polymer. The following use of optical backscatter reflectometry allows for continuous condition monitoring and precise detection and localization of structural damages during the entire service life. To account for the time-dependent strength degradation of the composite pressure vessels, both slow burst and ambient hydraulic cycling tests, respectively, were conducted on five 70 MPa pressure vessels with integrated fibre optic sensors. The results achieved via distributed fibre optic strain sensing demonstrate a near linear strain response to pressure suitable for sensitive condition monitoring and confirm the required robustness of the selected sensor solution.