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Brücken sind Teil der alternden Verkehrsinfrastruktur. Um die Nutzungsdauer zu verlängern und plötzliche Schäden zu detektieren kann Bauwerksmonitoring ("Structural Health Monitoring") eingesetzt werden. Der Einfluss von Umgebungsbedingungen, beispielsweise der Temperatur, auf das Bauwerksverhalten ist meist größer als der Einfluss von Schäden. Diese Einflüsse bestmöglich voneinander zu trennen und Veränderungen im Tragverhalten korrekt Schäden oder Umgebungsbedingungen zuzuordnen ist eine offene Forschungsfrage. Diese Arbeit zeigt eine mögliche Lösung, bei der gekoppelte Modelle von Umwelteinflüssen, Schäden und Tragverhalten des Bauwerks auf Grundlage von Monitoringdaten aktualisiert werden. Das Framework dazu wird vorgestellt und an einem Laborexperiment eines Stahlbetonbalkens in der Klimakammer angewandt. Die Ergebnisse bestätigen die Vorgehensweise. In nächsten Schritten müssen komplexere Tragwerke und die Sensitivität des Ansatzes untersucht werden.
Guided wave ultrasonic features in composite overwrapped pressure vessels towards digital twin
(2023)
The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.
Wavelength scanning coherent optical time domain reflectometer (WS-COTDR) is a good candidate to spatially resolve the environmental information at comparatively low frequency. This paper reveals it can also work as a traditional optical time domain reflectometer (OTDR) to identify Fresnel reflection by averaging the obtained signal over the wavelength scanning range. Simultaneous distributed vibration sensing and a traditional OTDR measurement are experimentally demonstrated using the WS-COTDR system.
We report on the development of a field-proven distributed fiber optic sensing system for structural health monitoring in road construction and civil engineering. The system is based on a cost-efficient digital incoherent optical frequency domain reflectometry (I-OFDR) for distributed strain detection along a polymer optical fiber. In this method, the strain-induced backscatter increase in a graded-index multimode perfluorinated polymer optical fiber (PF-POF) is determined by measuring the complex transfer function of the sensing fiber using a compact digital data acquisition unit.
Wavelength scanning coherent optical time domain reflectometer (WS-COTDR) is a good candidate to spatially resolve the environmental information at comparatively low frequency. This paper reveals it can also work as a traditional optical time domain reflectometer (OTDR) to identify Fresnel reflection by averaging the obtained signal over the wavelength scanning range. Simultaneous distributed vibration sensing and a traditional OTDR measurement are experimentally demonstrated using the WS-COTDR system.
Introduction to group 8.6
(2023)
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
In non-destructive testing and structural health monitoring with ultrasonic waves, the quantification of damage in components is one of the main tasks. In many shell-like structures, such as plates, pipes, or laminate components, ultrasonic waves propagate as guided waves. Although guided waves enable the testing of large areas, their multimodal and dispersive properties make it challenging to analyze signals. So, there is a need for more advanced algorithms to handle these properties, especially when reconstructing damage position and geometry.
The reconstruction can be formulated as an inverse problem where the measured signals are fitted with a simulative forward model. Due to the small wavelength of ultrasonic waves, classic forward models based on, e.g., the Finite Element Method are computationally intensive. In contrast, the authors use the semi-analytical Scaled Boundary Finite Element Method (SBFEM) to reduce the computational effort. The SBFEM approximates arbitrary long, undamaged parts of the structure with only a few degrees of freedom.
This contribution summarizes a general inverse procedure based on algorithmic differentiation in combination with the SBFEM. Results are presented for damaged 2D cross-sectional models of waveguides. These results include an analysis of the robustness of the proposed algorithms against noise.
In this paper, a cost-efficient distributed fiber optic measurement system based on Rayleigh scattering is presented. The distributed digital incoherent optical frequency domain reflectometry (I-OFDR) method is developed for detection of significantly large strain in the range from 3 % up to 10 % as required by end users. For this purpose, a vector network analyzer used in the I-OFDR is replaced by a compact and cost-effective digital data acquisition system. This digital emitting/receiving unit enables the recording of the complex transfer function carrying information about the local deformations along the entire sensing fiber.
This study aims to develop a real time structural health monitoring method by ultrasonic tests combined with advanced six component (6C) translation and rotation measurements. Conventionally, the investigation of the velocity and acceleration response in the translation direction is used to obtain the eigenfrequencies of structures. Recently the measurement of rotation has been considered to fully characterize the dynamic behavior of structures. This research undertakes the evaluation of a novel 6C sensor (IMU50-iXblue) with components originally developed for navigation for the purpose of bridge monitoring. However, as for all vibration recordings, there is a certain influence of environmental conditions (mainly temperature) which may affect evaluation and the results of structural assessment. We propose applying the cross-correlation function to the 6C ambient vibration signals to reconstruct wave propagation and using coda wave interferometry (CWI) to obtain internal velocity variation from waveforms. A field experiment on a large-scale prestressed concrete bridge model is presented. To verify that we are able to identify the pre-stress loss even in presence of temperature effects, we perform measurements in two different scales: the ultrasonic and output-only, vibration measurements. The change in the structural properties due to the pre-stress loss should be detected by the pulse velocity change. The results reveal both the performance and advantages of ultrasonic techniques and the capabilities of 6C sensors. To conclude, the application of CWI to wave signals contributes to a comprehensive assessment for bridge monitoring.
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.
This study aims to develop a real-time monitoring method for the structural performance by ultrasonic testing in combination with six-component (6-C) translation and rotation measurements. So far, the recordings of the velocity and acceleration response in the translation direction have been used to obtain the eigenfrequencies of structures. Recently, measurement of rotation has been used to fully characterize the dynamic behavior of structures. In this research, 6-C sensors consisting of a three-component rotational seismometer and a three-component broadband seismometer are evaluated for their suitability for bridge monitoring. Since changes in environmental conditions such as temperature during vibration recordings affect the structural evaluation results, cross-correlation function is applied here to the 6-C ambient vibration signals to reconstruct the wave propagation on the one hand, and coda wave interferometry (CWI) is applied to obtain the internal velocity variation from waveforms on the other hand. In field tests on a large-scale prestressed concrete bridge model, it was verified whether the prestress loss can also be determined in presence of temperature effects. For this purpose, measurements were performed in two scales, ultrasonic and output-only vibration measurements. The change in structural properties due to the prestress loss should be detected by the pulse velocity change. The results demonstrate both the performance and advantages of ultrasonic techniques and the capabilities of 6-C sensors. Thus, the application of CWI to wave signals can contribute comprehensively to bridge monitoring.
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.
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.
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.
Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring
(2022)
Although distributed fiber sensing techniques have been widely used in structural health monitoring, the measurement results of bridge monitoring, particularly under destructive testing, have rarely been reported. To the best of our knowledge, this paper is the first report of distributed vibration measurement results, which we obtained during a three-day destructive test on an abolished bridge. A coherent optical time domain reflectometry (COTDR) was used to acquire the vibration information while the bridge was being sawed. The obtained signal was analyzed in time and frequency domain. Some characteristics of the sawing-induced vibration were retrieved by the short-time Fourier transform; the vibration exhibited several high frequency components within the measured range up to 20 kHz and all the components appeared in the same time slot. Some unexpected signals were also detected. Thorough analysis showed that they are quite different from the sawing-induced vibration and are believed to originate from internal damage to the bridge (probably the occurrence of cracks).
Monitoring of repositories for radioactive waste requires techniques which can be applied long-term under harsh conditions. In this work, the reliability and suitability of materials and a capacitive sensor for measuring relative humidity are investigated, which are to be embedded in the special concrete components for the closure structures of underground repository. Preliminary tests with accelerated aging of the materials used, validation of the sensors under difficult conditions, investigations of the surfaces after aging by pH 14 solution, defined exposure of the sensors in a climatic chamber and the microscope images are discussed. The results will be used for further development and optimization of the RFID based sensor systems which can be applied to monitor the condition of different building structures without cabling.
The fourth dataset dedicated to the Open Guided Waves platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. Two types of signals were used for guided wave excitation, namely chirp signal and tone-burst signal. The chirp signal had a frequency range of 20-500kHz. The tone-burst signals had a form of sine modulated by Hann window with 5 cycles and carrier frequencies 16.5kHz, 50kHz, 100kHz, 200kHz, 300kHz. The piezoceramic actuator used for this purpose was attached to the center of the stringer side surface of the core plate. Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.
Damage detection of reinforced concrete (RC) structures is becoming a more attractive domain due to the safety issues arising in the last few decades. The damage in concrete can be caused by excessive exploitation of the structure or environmental effects. The cracks in concrete can be detected by different nondestructive testing methods. However, the available methods used for this purpose have numerous limitations. The technologies available in the market nowadays have difficulties detecting slowly progressive, locally limited damage. In addition, some of These methods cannot be applied, especially in hard-to-reach areas in the superstructures. In order to avoid these deficiencies, an embedded ultrasonic methodology can be used to detect cracks in RC structures. In this study, the methodology of crack detection supported with the advanced Signal processing algorithm was proposed and verified on RC structures of various types, and cracks occurring between embedded sensors can be detected. Moreover, different pairs of ultrasonic sensors located in the considered structures are used for the analysis of the sensitivity of distance between them. It is shown that the ultrasonic sensors placed in the range of 1.5–2 m can detect cracks, even when the other methods failed to detect changes in the structure. The obtained results confirmed that diffuse ultrasonic sensor methodology is able to monitor real structures more effectively than traditional techniques.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.
Ultrasonic monitoring, making use of the sensitivity of the coda of repeated transmission meas-urements to changes in stress, temperature, moisture, as well as localized or distributed damage, has gotten at-tention in structural health monitoring (SHM) research recently. Analysis methods such as coda wave inter-ferometry (CWI), including its nonlinear extension, have been shown to be able to measure ultrasonic wave velocity changes with a 1∙10-5 resolution, while indicators such as cross-correlation or cross-coherence have been used to distinguish between reversible and irreversible changes. Several small- and large-scale laboratory experiments have demonstrated that stress changes in structures can be captured or damage detected in a very early stage. The use of this technique for pre-warning before failure are currently under investigation, as well as detailed research on the physical causes and the connection between ultrasonic wave properties and materi-al/structural behavior. Recently, several of large-scale laboratory and real structures have been instrumented with embedded ultrasonic transducers to gather experience and evidence on how to use this technology in re-al-world applications. Preliminary results from installations on a new bridge, an existing bridge, a tunnel, a la-boratory earthquake test as well as a historic stadium in Germany, Poland, and the United States, respectively, are presented. Environmental influences (mainly temperature) and validation by load tests are discussed.
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.
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.
Digitaler Zwilling: Verwaltungsschale BBox als Datenablage über den Lebenszyklus einer Brücke
(2021)
Auf dem Weg zur digitalen Brücke existieren bereits erste Lösungen, die den Lebenszyklus einer Brücke abbilden können. Für die Planung, den Bau und den Unterhalt stehen unterschiedlichste Werkzeuge, z. B. BIM, DIN 1076, SIB-Bauwerke, Monitoring etc. zur Verfügung, die jeweils mit unterschiedlichen Datenformaten arbeiten. Für ein intelligentes Erhaltungsmanagement müssen aber alle Daten mit den verschiedenen Datenformaten zusammengeführt, abgelegt und so verwaltet werden können, dass über den gesamten Lebenszyklus einer Brücke die Abbildung eines ganzheitlichen digitalen Zwillings eines Bauwerks möglich ist. Die Autoren haben dafür mit BBox den Prototyp einer Verwaltungsschale (VWS) auf Grundlage von Industrie 4.0 entwickelt. Damit wird das physikalisch-ingenieurtechnische Modell zur Zustandsbewertung der Brücke in den Mittelpunkt gestellt und der gesamte Lebenszyklus einer Brücke kann unabhängig von Datenformaten digital erfasst werden. Da der Aufbau der VWS durch die Granularität optimal strukturiert ist, bietet die Ablage und Einspeisung von Messdaten sowohl die Grundlage eines Live-Monitorings als auch den Grundstein für maschinelles Lernen (ML). Der Datenzugriff via S3-Schnittstelle erleichtert die Entwicklung von eigenen Prognosemodellen mit Informationsmustern (SHIP – Structural Health Information Pattern). Am Beispiel der Heinrichsbrücke Bamberg wird die praktische intelligente Umsetzung des Bauwerksmonitorings inkl. VWS mit Informationsmustern und ML gezeigt.
The aim of this work is to improve the current structural health monitoring (SHM) methods for civil structures. A field experiment was carried out on a two-span bridge with a built-in un-bonded prestressing system. The bridge is a 24-metre long concrete beam resting on three bear-ings. Cracks were formed subsequently when a prestressing force of 350 kN was changed to 200 kN, so that different structural states could be demonstrated. The structural assessment of this reference bridge was accomplished by the non-destructive testing using ultrasonic devices and vibration measurements. The ultrasonic velocity variations were investigated by using the coda wave interferometry method. The seismic interferometry technique was applied to the vi-bration recordings to reconstruct the wave propagation field in the bridge. This investigation shows that the wave velocity is sensitive to the current structural state and can be considered as the damage indicator. Overall, the implementation of coda cave interferometry and seismic interferometry technique facilitates structural health monitoring (SHM) in civil engineering.
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.
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.
Monitoring of reinforced concrete structures to ensure their stability and increase their service-life is a crucial element of a modern infrastructural concept. With classical methods of non-destructive testing and inspection, repeated measurements under comparable conditions are difficult to conduct. Therefore, DFG research unit FOR 2825 CoDA researches the assessment of concrete damage using ultrasound coda wave interferometry and embedded sensors. Embedding the sensors into the monitoring target reduces human and non-human factors influencing repeatability. Using Coda Wave Interferometry (CWI), small velocity changes in the material can be detected by comparison of repeated measurements. The technique is sensitive to damaging changes like cracking as well as to reversible influences like material temperature. The understanding of these different influences on the signal is crucial for the analysis of long-term monitoring data to make an educated assessment of the structure and its integrity. With several laboratory experiments in a climate chamber and a long-term experiment recording an annual cycle in a large model on an outdoor test site in Horstwalde close to Berlin, we try to understand the influence of temperature on the CWI results. The results show that the velocity change calculated by CWI does closely follow the trend of concrete temperature. After one year of data recording with the large model being exposed to environmental variations only, the calculated velocity change resembles the annual temperature curve. The data shows a linear dependency between velocity and temperature change in a range of -0.03 percent per °K to -0.06 percent per °K - regardless of specimen size. An approach to remove temperature influence from the yearly cycle recorded in the large-scale experiment using this linear relation is unable to remove high-frequency variations - especially daily influences. Low-pass filtering the data can eliminate these variations while preserving permanent shifts caused by damages. Although we have shown that the influence of temperature on long term monitoring can be removed to a significant extent, there is still an influence of environmental changes remaining in the data. Possible nonlinear effects and influences not related to temperature need to be investigated in the future.
Optimization of the duration of Structural Health Monitoring (SHM) campaigns is rarely performed.
This article provides a utility-based solution to posteriorly determine: i) optimal monitoring Durations and ii) the extension of the service life of the welds on a steel bridge deck. The approach is Illustrated with a case study focusing on remaining fatigue life estimation of the welds on the orthotropic steel deck of the Great Belt Bridge, in Denmark. The identification of the optimal monitoring duration and the decision about extending the service life of the welds are modelled by maximizing the expected benefits and minimizing the structural risks. The results are a parametric analysis, mainly on the effect of the target probability, benefit, cost of failure, cost of rehabilitation, cost of monitoring and Discount rate on the posterior utilities of monitoring strategies and the choice of service life considering the risk variability and the costs and benefits models. The results show that the decision on short-term monitoring, i.e., 1 week every six months, is overall the most valued SHM strategy. In addition, it is found that the target probability is the most sensitive parameter affecting the optimal SHM Durations and service life extension of the welds.
Due to the importance of reinforced concrete structures for modern society, damage assessment during the entire life-cycle of such structures has become a special interest in non-destructive testing. Using embedded ultrasound sensors in combination with other measurement methods, numerical modeling and self-made data collectors, tailored specifically for monitoring tasks, the German research group DFG FOR CoDA aims to investigate and develop novel methods for damage detection and rapid model updating in reinforced concrete structures. In the first stage of the project, besides the development of custom-built, low-cost data collectors, ultrasonic transducers are embedded in a large, reinforced concrete specimen on a BAM test site near Berlin. In this experiment, the influence of changing environmental conditions (mainly temperature) on the ultrasound signal is investigated using coda-wave interferometry. The results show a correlation between changes in temperature and ultrasonic velocity. Such changes must be taken into consideration in a long-term monitoring setup to distinguish between reversible and permanent changes. By correcting the data using a linear relation between concrete temperature and velocity change to remove the seasonal trends and by low-pass filtering the data to remove daily variations can remove most of the temperature influence on the ultrasound measurements.
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.
We present the results of distributed fiber optic strain sensing for condition monitoring of a hybrid type IV composite fully wrapped pressure vessel using multilayer integrated optical fibers. Distributed strain sensing was performed for a total number of 252,000 load cycles until burst of the vessel. During this ageing test material fatigue could be monitored and spatially localized. Critical material changes were detected 17,000 cycles before material failure. Results have been validated by acoustic emission analysis.
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.
The use of load and structural performance measurement information is vital for efficient structural integrity management and for the cost of energy production with Offshore Wind Turbines (OWTs). OWTs are dynamically sensitive structures subject to an interaction with a control unit exposed to repeated cyclic wind and wave loads causing deterioration and fatigue. This study focuses on the quantification of the value of structural and environmental information on the integrity management of OWT structures, with the focus on fatigue of welded joints. By utilizing decision analysis, structural reliability methods, measurement data, as well as the cost-benefit models, a Value of Information (VoI) analysis can be performed to quantify the most beneficial measurement strategy. The VoI assessment is demonstrated for the integrity management of a butt welded joint of a monopile support structure for a 3 MW OWT with a hub height of approximately 71m. The conditional value of three-year measured oceanographic information and one-year strain monitoring information is quantified posteriori in conjunction with an inspection and repair planning. This paper provides insights on how much benefits can be achieved through structural and environmental information, with practical relevance on reliability-based maintenance of OWT structures.
Monitoring of concrete structures is of utmost importance in maintenance and preservation of infrastructure. As a part of the DFG research group CoDA (Concrete Damage Assessment by Coda Waves), the works within this PhD project aim for identification of damage sensitive parameters extracted from ultrasonic measurements obtained with embedded sensors, the determination of environmental (reversible) influences on the signal and the localisation of damaged areas. The main technique used for the detection of changes in the monitored material is the so-called Coda Wave Interferometry (CWI). It uses the later part of the ultrasonic recording for the detection of small changes in the sensed area and the calculation of a relative velocity change. Using this technique we can show that we are able to detect changes in concrete temperature with ultrasound measurements. This enables temperature correction for ultrasound monitoring with embedded sensors. The crucial point for imaging and localisation with coda waves is the calculation of the so-called sensitivity kernels. We propose the application of numerical wave simulations for the kernel calculations instead of the commonly used diffusivity equation. Using finite-difference wave modelling code from our DFG project partners we are able to calculate those Kernels and first results are shown in this presentation. The major task for the second year will be the development and implementation of the inversion algorithm as well as the preparation of validation experiments.
Due to their high strength-to-weight ratio and excellent fatigue resistance, glass fiber reinforced polymers (GFRP) are used as a construction material in a variety of applications including composite high-pressure gas storage vessels. Thus, an early damage detection of the composite material is of great importance. Material degradation can be determined via measuring the distributed strain profile of the GFRP structures. In this article, swept wavelength interferometry based distributed strain sensing (DSS) was applied for structural health monitoring of internal pressure loaded GFRP tube specimens. Measured strain profiles were compared to theoretical calculation considering Classical Lamination Theory. Reliable strain measurements with millimeter resolution were executed even at elongations of up to 3% in the radial direction caused by high internal pressure load. Material fatigue was localized by damaged-induced strain changes during operation, and detected already at 40% of burst pressure.
Structural Health Monitoring (SHM) is an important part of buildings surveillance and maintenance to detect material failure as early as possible and to contribute in protection of structures and their users.
The implementation of Radio Frequency Identification (RFID) sensor systems without cable connection and battery into building components offers innovative possibilities to enable long-term in-situ SHM of addressed structures, bridges. The objectives of the presented study are complete embedding of RFID sensors systems in concrete, full passive communication with the systems, at best for the whole life span of structures. One challenge for this task is the highly alkaline environment in concrete, which requires non-degrading and robust encapsulation. Further Requirements are passive communication and energy supply, appropriate antenna design, placement and fixation in concrete, and the selection and implementation of sensors and connections. The concept is to develop and optimize a simple and robust system, which meets the requirements, as well as comprehensive validation in concrete specimen and real world applications. Two different systems were developed (HF and UHF RFID, respectively).
First tasks were the implementation of analog sensors using the superposition principle for the signal adaption. Investigation of suitable materials for robust encapsulation and sensor protection against basic environments.
Four materials were investigated in pH 13 solution for 14 days
- 3D-Printer-Polymer was completely resolved
- PVC has no noticeable decrease in weight
- (VitaPro) glass filter for the sensor protector, has weight loss 2.7 %
- The epoxy resin has increased by 1.8 % due to moisture expansion
Different concrete samples were prepared for the validation of the systems.
RFID sensors were embedded in different integration depths. Investigate the energy- and data transfer through concrete, also with varying moisture content. Additionally, signal strength data was used to optimize and validate the antenna characteristics in concrete. Next steps are to guarantee a sufficient energy supply for UHF RFID systems embedded in different concrete mixtures and further embedding the HF and UHF RFID systems in real bridges and buildings to validate the long term monitoring.
Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications
(2019)
We introduce wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) for dynamic vibration sensing along optical fibers. The method is based on spectral shift computation from Rayleigh backscatter spectra. Artificial neural networks (ANNs) are used for fast and high-resolution strain computation from raw measurement data. The applicability of the method is demonstrated for vibration monitoring of a reinforced concrete bridge. We demonstrate another application example for quasi-static and dynamic measurement of ground deformation and surface wave propagation along a dark fiber in a telecommunication cable.
Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications
(2019)
We introduce wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) for dynamic vibration sensing along optical fibers. The method is based on spectral shift computation from Rayleigh backscatter spectra. Artificial neural networks (ANNs) are used for fast and high-resolution strain computation from raw measurement data. The applicability of the method is demonstrated for vibration monitoring of a reinforced concrete bridge. We demonstrate another application example for quasi-static and dynamic measurement of ground deformation and surface wave propagation along a dark fiber in a telecommunication cable.
The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure.
The subject of damage localization is an important issue for Structural Health Monitoring (SHM) particularly in mechanical or civil structures under ambient excitation. In this paper, the statistical subspacebased damage localization method has been applied on a benchmark application, namely a 1/200 scale model of the Saint-Nazaire Bridge, which is a cable-stayed bridge located on the Loire River near the river’s mouth. The employed damage localization method combines data-driven features with physical parameter information from a finite element model in statistical tests, avoiding typical ill-conditioning problems of FE model updating. Damage is introduced in the mockup for cable failures on some of the 72 cables. The purpose of the experiment is to assess the capability of damage assessment methods to find a cable failure.
We present the results of distributed fibre optic strain sensing for condition monitoring of a hybrid type IV composite fully wrapped pressure vessel using multilayer integrated optical fibres. During load cycle tests material fatigue could be localised and monitored 17,000 load cycles before burst. Results have been validated by acoustic emission analysis.
The assessment of Coda Wave Interferometry (CWI) and Distributed Fiber Optics Sensing (DFOS) techniques for the detection of damages in a laboratory size reinforced concrete beam is presented in this paper. The sensitivity of these two novel techniques to micro cracks is discussed and compared to standard traditional sensors. Moreover, the capacity of a DFOS technique to localize cracks and quantify crack openings is also assessed. The results show that the implementation of CWI and DFOS techniques allow the detection of early subtle changes in reinforced concrete structures until crack formation. With their ability to quantify the crack opening, following early detection and localization, DFOS techniques can achieve more effective monitoring of reinforced concrete structures. Contrary to discrete sensors, CWI and DFOS techniques cover larger areas and thus provide more efficient infrastructures asset management and maintenance operations throughout the lifetime of the structure.
This work describes a vibration-based structural health monitoring of a prestressed-concrete box girder bridge on the A100 Highway in Berlin by applying statistical pattern recognition technique to a huge amount of data continuously collected by an integrated monitoring system during the period from 2000 to 2013. Firstly, the general condition and potential damage of the bridge is described. Then, the dynamic properties are extracted from 20 velocity sensors. Environmental variability captured by five thermal transducers and traffic intensity approximately estimated by strain measurements are also reported. Nonlinear influences of temperature on natural frequencies are observed. Subsequently, the measurements during the first year are used to build a baseline health index. The multiple linear regression (MLR) method is used to characterize the nonlinear relationship between natural frequencies and temperatures. The Euclidean distance of the residual errors is calculated to build a statistical health index. Finally, the indices extracted from the following years gradually deviate; which may indicate structural deterioration due to loss of prestress in the prestressed tendons.
The Westend Bridge is located on the A100 Highway in Berlin. An integrated continuous dynamic monitoring system, composed of 20 velocity sensors, 5 temperature sensors, 3 strain gauges, 1 crack sensor and 2 inclination sensors, was implemented by the Federal Institute for Materials Research and Testing in 2000. The system runs continuously with occasional intermittence and led to a huge amount of data over a 14-year span. In this article, variations of the strain, crack and inclination measurements during the last 14 years are presented. It is noted that the observed crack and inclination of the bridge are strongly influenced by seasonal temperature variation. It further induces change in the relationship between the strains measured in both concrete and prestressed tendon. Application of k-means cluster Analysis technique in both the crack and strain measurements can partition them into different seasonal phases by identifying ‘turning points’ that indicate annual periodical bridge change. In the period of these two ‘turning points’, a strong linear relation of the strains in two materials is observed. In the rest of the year, a nonlinear relationship between the strains recorded in both the concrete and the prestressed tendon is noted. The possible reason is the additional thermal load due to the change in temperature difference between the bridge’s surface and soffit. Finally, a health index in a Framework of regression model and process control theory is proposed by investigating the linear relationship between the strains in concrete and prestressed tendon. The tendency of the health index in the 14 years may suggest the long-term bridge change during that time frame.