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Public, hidden, and forensic features either encoded or directly legible are used for authentication. Fabry-Perot layer stacks as information carriers in combination with imaging ellipsometry as optical read-out system provide all-in-one anti-counterfeiting capability that may establish the new security level 'encoded forensic'. Different layer designs are described with regard to all three security levels: public features (e.g. color and tilt effect) perceptible by the human eye, hidden features (e.g. spectroscopic response in the UV or IR), and forensic features (ellipsometric quantities Ψ and Δ as a function of wavelength λ and angle of incidence AOI). Physically uncloneable functions (PUF) could be realized as a result of a multi-material and a multi-parameter deposition approach as well as by means of specific design features of the Fabry-Perot layer stack. Hence, they are not subject to any reverse engineering strategies. Examples of stratified, micro-structured, and laser-modified Fabry-Perot layer systems are considered that may be used at all perception levels (e.g. human eye, bar code reader, and imaging ellipsometry) for authentication against product counterfeiting and related areas. --------------------------------------------------------------------------------------------------------------------
Zur Authentifizierung werden offene, versteckte und forensische Merkmale entweder codiert oder direkt auslesbar verwendet. Mit Fabry-Perot Schichtsystemen als Informationsträger in Kombination mit der abbildenden Ellipsometrie als optischem Auslesesystem steht ein 'all-in-one' Fälschungsschutz-System zur Verfügung, das ein neues Sicherheitsniveau 'kodiert-forensisch' darstellt. Es werden unterschiedliche Schichtdesigns beschrieben, mit denen alle drei Sicherheitsniveaus realisiert werden können: offene Merkmale (z.B. Farbe und Kippeffekt), die mit dem menschlichen Auge wahrnehmbar sind, versteckte Merkmale (z.B. die spektrale Antwort im Sichtbaren oder im IR) sowie forensische Merkmale (ellipsometrische Größen Ψ und Δ als Funktion der Wellenlänge λ und des Einfallswinkels AOI).
Physikalisch nicht klonbare Funktionen (PUF) können im Ergebnis einer Multi-Material- und Multi-Parameter Beschichtungsstrategie sowie durch spezifische Design-Merkmale des Fabry-Perot Schichtsystems realisiert werden. Folglich sind solche Schichtsysteme dem 'reverse engineering' nicht zugänglich. Es werden Beispiele für lateral ausgedehnte, mikrostrukturierte und Laser modifizierte Schichtsysteme auf verschiedenen Ausleseniveaus (menschliches Auge, Barcode-Lesegerät, abbildendes Ellipsometer) vorgestellt, die zur Authentifizierung von Produkten und verwandte Gebiete geeignet sind.
Entwicklung einer praxisnahen Prüfmethode zur Bestimmung der Radon-Exhalation aus Bauprodukten
(2012)
Embedding of information on surfaces is state of the art for identification testing in which public, hidden and forensic features are used. In many instances, the legal authentication of a product, a material or a document is required. Among the surface-based encoded labels, bar codes and data matrices are most frequently applied. They are publicly visible. The material itself is irrelevant, only a sufficient optical contrast is required.
However, a strong material dependence of the label can be achieved by means of Fabry-Perot layer stacks. Stack designs are described with regard to all three security levels: public features (e.g. color and tilt effect) perceptible by the human eye, hidden features (e.g. optical response in a given spectral range) detectable by commonly available instruments and forensic features (ellipsometric quantities Ψ and Δ as a function of wavelength λ and angle of incidence AOI) only detectable by sophisticated instruments.
Regarding material-correlated authentication, ellipsometric quantities Ψ and Δ are used as encoded forensic features for the first time. Hence, Fabry-Perot layer stacks as information carriers in combination with imaging ellipsometry as optical read-out system provide all-in-one anti-counterfeiting capabilities.
Determination of radon exhalation from construction materials using VOC emission test chambers
(2013)
The inhalation of 222Rn (radon) decay products is one of the most important reasons for lung cancer after smoking. Stony building materials are an important source of indoor radon. This article describes the determination of the exhalation rate of stony construction materials by the use of commercially available measuring devices in combination with VOC emission test chambers. Five materials – two types of clay brick, clinker brick, light-weight concrete brick, and honeycomb brick – generally used for wall constructions were used for the experiments. Their contribution to real room concentrations was estimated by applying room model parameters given in ISO 16000-9, RP 112, and AgBB. This knowledge can be relevant, if for instance indoor radon concentration is limited by law. The test set-up used here is well suited for application in test laboratories dealing with VOC emission testing.
The inhalation of 222Rn (radon) is one of the most important reasons for lung cancer, after smoking. Usually, the geological subsoil and the building ground are the dominant sources for enhanced indoor radon levels. Additionally, building materials can increase indoor radon concentrations when these materials contain higher contents of 226Ra (radium), especially in combination with low air exchange rates. For a realistic estimation of indoor radon
concentrations, it is helpful to carry out measurements of radon exhalation rates from relevant materials using emission test chambers. In Germany, it is aspired to limit the total indoor radon concentration to 100 Bq/m3, whereby building materials should contribute at most 20 Bq/m3. Within a project financed by the German Institute for Construction Technology (DIBt), a practical oriented measurement procedure of the radon exhalation of building materials in accordance to ISO 16000-9 was developed to have a means for the assessment of these materials with respect to their indoor use. Test chambers with different volumes were used. The tested materials were mainly used for wall constructions (e.g., bricks, light-weight concrete) and have known specific radium activities and radon exhalation rates.
The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.
Structural changes of highly active Pd/MeOx (Me = Fe, Co, Ni) during catalytic methane combustion
(2018)
Fe2O3, Co3O4 and NiO nanoparticles were prepared via a citrate method and further functionalized with Pd by impregnation. The pure oxides as well as Pd/Fe2O3, Pd/Co3O4, and Pd/NiO (1, 5 and 10 wt % Pd) were employed for catalytic methane combustion under methane lean (1 vol %)/oxygen rich (18 vol %, balanced with nitrogen) conditions. Already, the pure metal oxides showed a high catalytic activity leading to complete conversion temperature of T100 ≤ 500 °C. H2-TPR (Temperature-programmed reduction) experiments revealed that Pd-functionalized metal oxides exhibited enhanced redox activity compared to the pure oxides leading to improved catalytic combustion activity at lower temperatures. At a loading of 1 wt % Pd, 1Pd/Co3O4 (T100 = 360 °C) outperforms 1Pd/Fe2O3 (T100 = 410 °C) as well as 1Pd/NiO (T100 = 380 °C). At a loading of 10 wt % Pd, T100 could only be slightly reduced in all cases. 1Pd/Co3O4 and 1Pd/NiO show reasonable stability over 70 h on stream at T100. XPS (X-ray photoelectron spectroscopy) and STEM (Scanning transmission electron microscopy) investigations revealed strong interactions between Pd and NiO as well as Co3O4, respectively, leading to dynamic transformations and reoxidation of Pd due to solid state reactions, which leads to the high long-term stability.
Managing aging engineering structures requires damage identification, capacity reassessment, and prediction of remaining service life. Data from structural health monitoring (SHM) systems can be utilized to detect and characterize potential damage. However, environmental and operational variations impair the identification of damages from SHM data. Motivated by this, we introduce a Bayesian probabilistic framework for building models and identifying damage in monitored structures subject to environmental variability. The novelty of our work lies (a) in explicitly considering the effect of environmental influences and potential structural damages in the modeling to enable more accurate damage identification and (b) in proposing a methodological workflow for model‐based structural health monitoring that leverages model class selection for model building and damage identification. The framework is applied to a progressively damaged reinforced concrete beam subject to temperature variations in a climate chamber. Based on deflections and inclinations measured during diagnostic load tests of the undamaged structure, the most appropriate modeling approach for describing the temperature‐dependent behavior of the undamaged beam is identified. In the damaged state, damage is characterized based on the identified model parameters. The location and extent of the identified damage are consistent with the cracks observed in the laboratory. A numerical study with synthetic data is used to validate the parameter identification. The known true parameters lie within the 90% highest density intervals of the posterior distributions of the model parameters, suggesting that this approach is reliable for parameter identification. Our results indicate that the proposed framework can answer the question of damage identification under environmental variations. These findings show a way forward in integrating SHM data into the management of infrastructures.
Die Infrastruktursysteme der Industriestaaten erfordern heute und in Zukunft ein effizientes Management bei alternder Bausubstanz, steigenden Lasten und gleichbleibend hohem Sicherheitsniveau. Digitale Technologien bieten ein großes Potenzial zur Bewältigung der aktuellen und künftigen Herausforderungen im Infrastrukturmanagement. Im BMBF-geförderten Projekt Bewertung alternder Infrastrukturbauwerke mit digitalen Technologien (AISTEC) wird untersucht, wie unterschiedliche Technologien und deren Verknüpfung gewinnbringend eingesetzt werden können. Am Beispiel der Maintalbrücke Gemünden werden ein sensorbasiertes Bauwerksmonitoring, bildbasierte Inspektion mit durch Kameras ausgestatteten Drohnen (UAS) und die Verknüpfung digitaler Bauwerksmodelle umgesetzt. Die aufgenommenen Bilder dienen u. a. als Grundlage für spätere visuelle Anomaliedetektionen und eine 3D-Rekonstruktion, welche wiederum für die Kalibrierung und Aktualisierung digitaler Tragwerksmodelle genutzt werden. Kontinuierlich erfasste Sensordaten werden ebenfalls zur Kalibrierung und Aktualisierung der Tragwerksmodelle herangezogen. Diese Modelle werden als Grundlage für Anomaliedetektionen und perspektivisch zur Umsetzung von Konzepten der prädiktiven Instandhaltung verwendet. Belastungsfahrten und historische Daten dienen in diesem Beitrag der Validierung von kalibrierten Tragwerksmodellen.
The Bundesanstalt für Materialforschung und -prüfung (BAM) collaborates with TNO to develop a software framework for automated calibration of structural models based on monitoring data. The ultimate goal is to include such models in the asset management process of engineering structures. As a basis for developing the framework, a multi-span road bridge consisting of ten simply supported steel box girders was selected as a test case. Our group measured output-only vibration data from one box girder under ambient conditions. From the data, we determined eigenfrequencies and mode shapes. In parallel, we developed a preliminary structural model of the box girder for the purpose of predicting its modal properties. In this contribution, we provide an overview of the measurement campaign, the operational modal analysis, the structural modeling and qualitatively compare the identified with the predicted modes. As an outlook, we discuss the further steps in the calibration process and future applications of the calibrated model.
Information on the condition and reliability of an offshore jacket structure provided by a vibration-based structural health monitoring system can guide decisions on inspection and maintenance. When selecting the sensor setup, the designer of the monitoring system must assess its overall benefit compared to its costs before installation. The potential benefit of continuously monitoring the dynamic response of a jacket structure can be formally quantified through a value of information analysis from Bayesian decision theory. In this contribution, we present a framework for optimizing the placement of vibration sensors on offshore jacket structures by maximizing the value of information of the monitoring system. To solve the resulting discrete optimization problem, we adapt a genetic algorithm. The framework is demonstrated in a numerical example considering a redundant jacket-type steel frame. The numerical study shows that monitoring the vibration response of the frame is beneficial. Good sensor setups consist of relatively few sensors located towards the upper part of the
frame. The adapted genetic algorithm performs similarly well as established sequential sensor placement algorithms and holds substantial promise for application to real jacket structures.
Changes in the measured response of structural systems can be an indication of structural damages. However, such changes can also be caused by the effect of varying environmental conditions. To detect, localize and quantify changes or damages in structural systems subject to varying environmental conditions, physics-based models of the structural systems have to be applied which explicitly account for the influence of ambient conditions on the structural behavior. Data obtained from the structural systems should be used to calibrate the models and update predictions. Bayesian system identification is an effective framework for this task. In this paper, we apply this framework to learn the parameters of two competing structural models of a reinforced concrete beam subject to varying temperatures based on static response data. The models describe the behavior of the beam in the uncracked and cracked condition. The data is collected in a series of load tests in a climate chamber. Bayesian model class selection is then applied to infer the most plausible condition of the beam conditional on the available data.
Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system.
Inspection and maintenance contribute significantly to the lifetime cost of bridges. There is significant potential in using information obtained through structural health monitoring to update predictive models of the condition and performance of such structures, and thus ena-ble an improved decision-making regarding inspection and maintenance activities. Within the AISTEC project funded by the German Federal Ministry of Education and Research, we develop vibration-based structural health monitoring systems aimed at continuously provid-ing information on the structural condition of bridges. Environmental variations such as changing ambient temperatures can significantly influence the dynamic characteristics of bridges and thus mask the effect of structural changes and damages. It remains a challenge to account for such influences in structural health monitoring. To study the effect of ambient temperatures on the dynamic characteristics of beam structures, we monitor the vibration response of a reinforced concrete beam in the uncracked and cracked state at varying tem-peratures in a climate chamber. We postulate a set of competing parameterized probabilistic structural models, which explicitly account for the effect of varying ambient temperatures on the mechanical properties of the system. We then combine the information provided by the structural models with the information contained in the recorded vibration data to learn the parameters of the temperature-dependent structural models and infer the plausible state of the beam using Bayesian system identification and model class selection.
Nach den bisherigen Regeln werden Bauwerksprüfungen von Brücken in starr definierten Intervallen durchgeführt. Diese starre, periodische, zustandsbasierte Instandhaltungsstrategie soll zukünftig durch eine flexible und prädiktive Instandhaltung ersetzt werden. Hierbei sollen Inspektionen und Instandhaltungsmaßnahmen unterstützt durch autonome Systeme auf der Grundlage von Monitoringdaten geplant und zusätzlich bei unvorhergesehenen Ereignissen ausgelöst werden. Im Rahmen des Verbundforschungsvorhabens AISTEC wird ein Vorgehen für Großbauwerke und kleinere Regelbauwerke zur Umsetzung eines kombinierten globalen, schwingungsbasierten und quasistatischen Monitorings entworfen. An der Maintalbrücke Gemünden – einer semiintegralen Rahmenbrücke aus Spannbeton auf der Schnellfahrstrecke Hannover–Würzburg – wurde als Demonstrator ein Dauermonitoring realisiert.
In this contribution, we consider two applications in which probabilistic approaches can potentially complement or enhance the design and assessment of offshore wind turbine foundations. First, we illustrate in a numerical example that probabilistic modelling can be helpful in dealing with chang-es in turbine locations during the planning phase of an offshore wind farm. In this case, spatial probabilistic modelling of the ground conditions enables (a) an inference of the soil properties at the modified turbine location from field data collected at different locations across an offshore wind farm site and (b) an optimisation of further site investigations. Second, we discuss the uncer-tainties and risks associated with the installation of large diameter monopiles in soils with hetero-geneities such as strong layers and/or embedded boulders. Subsequently, we present a concept for modelling, understanding, and managing these risks based on a probabilistic model of the subgrade conditions, monopile, and subgrade-pile-interaction.
Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data.
Support structures of wind turbines in German offshore wind farms are regularly inspected. Currently, inspection outcomes are generally not systematically recorded. This prevents their consistent handling and processing, which is a key requirement to enable an efficient structural integrity management. As part of the DiMoWind-Inspect project, a data model and reference designation system for such inspection results is developed to facilitate their identification, localization, quantification, tracing and linking throughout the lifetime of a wind farm and beyond. The inspection results together with structural health monitoring results and information on repairs form the basis for assessing and predicting the structural condition, estimating the remaining lifetime, and planning of future inspections, structural health monitoring, and repairs. As a basis for developing a digital structural integrity management, a generic framework is proposed in this paper, which describes the stakeholders, data, models, processes and workflows of the integrity management of support structures in offshore wind farms and their interrelations. The framework adopts a building information modelling approach to describe the information relevant to the structural integrity management. The central digital space of the framework is a common data environment. An implementation of the framework will enable a digital structural integrity management in which inspection, structural health monitoring, repair, assessment and planning outcomes generated throughout the life cycle of a wind farm can be consistently collected, managed, shared and processed by the relevant shareholders.
A digital structural integrity management of offshore structures enables an optimized planning of inspections and repairs with risk-based methods. In a risk-based approach, the inspection and repair strategy that minimizes the expected lifetime costs consisting of the expected inspection, repair and failure costs is determined. In addition to inspections, information on the structural condition can be continuously obtained by monitoring the vibration response of the structural system. Changes in the vibration characteristics quantified in terms of modal properties can be an indication of structural damage. In risk-based inspection and repair planning, the effect of monitoring results is determined via Bayesian updating of the structural condition and reliability. This information should be applied to inform decisions on inspections and may result in a reduced inspection effort. The benefit of continuously monitoring the structural health can be quantified in terms of the value of information, which corresponds to the difference between the expected lifetime costs with and without monitoring. In this work, we demonstrate in a numerical example how an optimized sensor placement for a vibration-based structural health monitoring system can be determined by maximizing the value of information.