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Distributed fiber optic sensing (DFOS) technique is a promising and robust non-destructive testing tool that can seamlessly acquire environmental conditions over large scales. Therefore, it has found extensive applications in structural health monitoring. Its appeal for monitoring underground facilities lies in the intrinsic properties of the optical fiber, such as immunity to magnetic interference, small size, chemical inertia, etc. This paper provides a concise overview of DFOS applications in underground facility monitoring. Following a brief introduction to the workingbnprinciple of the DFOS technique, various examples are provided to demonstrate how distributed fiber sensors contribute to monitoring underground facilities. The paper presents unpublished field test results with an emphasis in the energy sector, including monitoring gas storage facilities, geothermal reservoir exploration, and ground movement detection. Furthermore, the paper identifies several directions for enhancing the DFOS system.
Implementation and validation of robot-enabled embedded sensors for structural health monitoring
(2024)
In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, facilitating the execution of repair and maintenance work in the early stages of structural damage. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate data relevant to SHM. A promising method for SHM is to embed sensors directly into concrete for recording SHM data from inside structural elements. In this paper, a sensor system for embedment into concrete is proposed, able to assess SHM data recorded from concrete. Power is supplied to the sensors on-demand by quadruped robots, which also collect the SHM data via radio-frequency identification (RFID), providing an automated and efficient SHM process. In laboratory experiments, the capability of the sensor system of automatically collecting the SHM data using quadruped robots is validated. In summary, the integration of RFID technology and robot-based inspection presented in this study demonstrates a vital approach to evolve current SHM practices towards more digitalized and automated SHM.
Implementation and validation of robot-enabled embedded sensors for structural health monitoring
(2024)
In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, facilitating the execution of repair and maintenance work in the early stages of structural damage. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate data relevant to SHM. A promising method for SHM is to embed sensors directly into concrete for recording SHM data from inside structural elements. In this paper, a sensor system for embedment into concrete is proposed, able to assess SHM data recorded from concrete. Power is supplied to the sensors on-demand by quadruped robots, which also collect the SHM data via radio-frequency identification (RFID), providing an automated and efficient SHM process. In laboratory experiments, the capability of the sensor system of automatically collecting the SHM data using quadruped robots is validated. In summary, the integration of RFID technology and robot-based inspection presented in this study demonstrates a vital approach to evolve current SHM practices towards more digitalized and automated SHM.
We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geosynthetics which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geosynthetics-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements.
We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geosynthetics which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geosynthetics-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements.
Technological innovation and digitalization are transforming industries and organizations worldwide. Digital Maturity Models often serve as a reference point to monitor the digitalization progression through prescribed stages. Yet, little is still understood about (i) how a digital maturity model should be designed and deployed in a specific industry setting and (ii) the association between digital maturity and technology adoption. Building on extant literature and extensive interviews with industry experts, we first develop a digital maturity model in the context of the conformity assessment industry (a globally regulated industry responsible for assessing compliance of organizations with standards and regulations). Second, we implement the model and conduct a survey across 1447 organizations in 15 countries. Our findings reveal that the digital transformation journey of the industry progresses along five stages of maturity (with most organizations in the early/ intermediate stages of digital maturity) with distinctive patterns of technical and managerial practices across all stages. Adoption of technologies is (unsurprisingly) associated with higher digital maturity levels - albeit that association does not hold for complex technologies (such as Blockchain) and is impacted by country specifics (measured by the Digital Skills Gap Index). Our study provides insights into digital maturity and the adoption of technologies in an institutional environment that is highly regulated and conservative, in which high integrity of service provision is primary, and digitalization is a secondary focus of organizations.
Distributed fiber optic sensing (DFOS) technique is a promising and robust non-destructive testing tool that can seamlessly acquire environmental conditions over large scales. Therefore, it has found extensive applications in structural health monitoring. Its appeal for monitoring underground facilities lies in the intrinsic properties of the optical fiber, such as immunity to magnetic interference, small size, chemical inertia, etc. This paper provides a concise overview of DFOS applications in underground facility monitoring. Following a brief introduction to the working principle of the DFOS technique, various examples are provided to demonstrate how distributed fiber sensors contribute to monitoring underground facilities. The paper presents unpublished field test results with an emphasis in the energy sector, including monitoring gas storage facilities, geothermal reservoir exploration, and ground movement detection. Furthermore, the paper identifies several directions for enhancing the DFOS system.
Im Rahmen des Forschungsprojekts "Artificial Intelligence for Rail Inspection" (AIFRI) wird ein KI-Algorithmus entwickelt, um die Fehlererkennung und Bewertung bei der Auswertung von Schienenprüfungen mittels Ultraschall- und Wirbelstromprüfverfahren zu verbessern. Die Bandbreite möglicher Defekte und die Menge an Einflussgrößen auf die Schienenprüfung ist sehr groß, aber die Prüfdaten aus dem Feld bilden diese Bandbreite nicht balanciert ab und sind unzureichend gelabelt. Durch Simulationen werden große Mengen detailliert gelabelter Daten für relevante Schienenschädigungen und Artefakte bereitgestellt. Aus diesen Daten werden wiederum virtuellen Prüffahrten erstellt, die für das Training und die Validierung der KI genutzt werden können.
In unserem Vortrag stellen wir den Erstellungsprozess der Datensätze vor. Jeder Schritt von der Geometriedarstellung bis zur Datenverarbeitung wird erfasst. Der Fokus wird auf Simulationsergebnisse und deren Verarbeitung gelegt. Die Signalverarbeitung spielt in den Datensätzen eine große Rolle, dabei werden die Datensätze an die realen Daten angepasst. Für Wirbelstrom und Ultraschall werden diese Prozesse im Hinblick auf die Fehlertypen Head Checks und Bohrungsanrisse veranschaulicht. Ferner diskutieren wir die Simulation von Schweißnähten, die von beiden Prüfsystem detektiert werden können; und somit besondere Abstimmung bedürfen aber auch Synergieeffekte zwischen den Prüfsystem ermöglichen.
Für die Ablage der Datensätze verweisen wir auf das ebenfalls eingereichte Poster „Verwendung von DICONDE bei der Eisenbahn-Schienenprüfung“.
Das Projekt AIFRI wird im Rahmen der Innovationsinitiative mFUND unter dem Förderkennzeichen 19FS2014 durch das Bundesministerium für Digitales und Verkehr gefördert.
This working paper summarises the current state of knowledge and research on the application of sensors in road pavements, designed to record input quantities and characteristics for structural assessment. In this working paper, sensors generally refer to technical equipment that can used to record the values of physical quantities and display them for interpretation.
The working paper deals with every type of sensor that is permanently connected to the road, i.e. sensors that are installed – in the bound and unbound courses of the superstructure as well as in the sub-base/sub-structure, – next to or above the road (for example on masts) and can contribute to structural recording and its subsequent assessment. This can also include sensors that are primarily used for a different purpose (e.g. traffic control). However, this working paper only describes the application of sensors that are installed primarily for other purposes for the sake of completeness; special publications (e.g. Notes on detection technologies in road traffic) are available for the respective installation situations.
We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.