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Die dauerhafte messtechnische Überwachung von Brückenbauwerken ist nach wie vor eine neuartige Verfahrensweise zur Bestimmung des Ist-Zustandes von Tragwerken. Der Fachbereich 7.2 Ingenieurbau der BAM befasst sich seit mittlerweise mehr als 25 Jahren mit der Entwicklung von Methoden und Verfahren dieser Form der Bauwerksüberwachung. Die damit verbundene Tätigkeit umfasst die Beschreibung theoretischer Grundlagen, die Entwicklung von Algorithmen zur Erfassung, Bearbeitung und Auswertung von Messsignalen, die Bewertung der Ergebnisse bezüglich Tragsicherheit, Gebrauchstauglichkeit und Restnutzungsdauer und nicht zuletzt die Untersuchung und Verifizierung der praktischen Anwendbarkeit.
Dabei standen insbesondere die sogenannten dynamischen Verfahren im Blickpunkt der Aktivitäten. Ursprünglich zur Überwachung von rotierenden Maschinenkomponenten entwickelt und dann auch erfolgreich zur Überwachung von Bauteilen der Flugzeug-, Raumfahrt- und Automobilindustrie weiterentwickelt, weckten diese Verfahren weltweit das Interesse der sich mit der Sicherheit bestehender Baukonstruktionen befassenden Wissenschaftler und Ingenieure.
Ein beachtenswertes Projekt der BAM in diesem Zusammenhang ist die Dauerüberwachung der Westendbrücke in Berlin, das Mitte der 90er Jahre von der damaligen Senatsverwaltung für Stadtentwicklung von Berlin (ehemals Bauen, Wohnen und Verkehr) in Auftrag gegeben und über viele Jahre gefördert wurde. Im Rahmen dieses Aufsatzes werden am Beispiel dieses Projektes, das sich wie ein roter Faden durch die Entwicklungsgeschichte des Monitorings an der BAM zieht, die Motivationen und Zielstellungen dargelegt, das Überwachungssystem vorgestellt und die gemachten Erfahrungen anhand einiger Ergebnisse erläutert. In einem weiteren Kapitel wird ein Ausblick auf die Zukunft des Monitorings mit angepassten Zielvorstellungen, neuen Methodiken und Strategien geworfen.
For the last decades vibration based identification of damage on civil Engineering structures has become an important issue for maintenance operations on transport infrastructure.
Research in that field has been rapidly expanding from classic modal Parameter estimation using measured excitation to modern operational monitoring. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. In this paper, two methods accounting for statistical and/or operational uncertainties are applied to measurement data of a progressive damage test on a prestressed concrete bridge. On the base of covariance driven Stochastic Subspace Identification (SSI) an algorithm is developed to monitor and automatically compute confidence intervals of the obtained modal parameters. Furthermore, a null space based non-parametric damage detection method, utilizing a statistical χ2 type test is applied to the measurement data. It can be shown that for concrete bridges the proposed methodology is able to clearly indicate the presence of structural damage, if the damage leads to a change of the structural system.
For the last decades vibration based damage detection of engineering structures has become an important issue for maintenance operations on transport infrastructure. Research in vibration based structural damage detection has been rapidly expanding from classic modal parameter estimation to modern operational monitoring. Methodologies from control Engineering especially of aerospace applications have been adopted and converted for the application on civil structures. Here the difficulty is to regard to the specific environmental and operational influence to the structure under observation. A null space based damage detection algorithm is tested for its sensitivity to structural damage of a prestressed concrete road bridge. Specific techniques and extensions of the algorithm are used to overcome difficulties from the size of the structure which is associated with the number of recorded sensor channels as well as from the operational disturbances by a nearby construction site. It can be shown that for concrete bridges the proposed damage detection methodology is able to clearly indicate the presence of structural damage, if the damage leads to a significant change of the structural system. Small damage which do not result in a System change when not activated by loading, do not lead to a modification of the dynamic response behavior and for that cannot be detected with the proposed global monitoring method.
In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios and mode shapes) obtained from Stochastic Subspace Identification (SSI) of a structure, are afflicted with statistical uncertainty. For evaluating the quality of the obtained results it is essential to know the respective confidence intervals of these figures. In this paper we present algorithms that automatically compute the confidence intervals of modal parameters obtained from covarianceand data-driven SSI of a structure based on vibration measurements. They are applied to the monitoring of the modal parameters of a prestressed concrete highway bridge during a progressive damage test that was accomplished within the European research project IRIS. Results of the covariance- and data-driven SSI are compared.
Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably.
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.
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.
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.
Falltürme sind Bauwerke, die als Versuchsanlagen sehr speziellen, impulsartigen Belastungen ausgesetzt sind. Am Fallturm auf dem Testgelände Technische Sicherheit (TTS) der BAM wurde bei Routineinspektionen eine Abnahme der strukturellen Integrität in Form von Vorspannungsverlusten in den Bolzen der Stahlverbindungen festgestellt. Um ein Verständnis für die zugrundeliegenden Trag- und Schädigungsmechanismen zu erlangen, wurde ein umfassendes Structural Health Monitoring (SHM) System geplant und am Bauwerk installiert unter Nutzung von digitalen Bauwerksmodellen. Für die Auslegung des Überwachungssystems, insbesondere aber zur Unterstützung der Untersuchung des Schädigungsprozesses, wurden Finite-Elemente-Modelle erstellt. Um sicherzustellen, dass die FE-Modelle das reale Tragwerksverhalten mit ausreichender Genauigkeit abbilden, mussten sie jedoch in Bezug auf die gemessenen Antworten des Tragwerks kalibriert werden. Der vorliegende Beitrag beschreibt experimentelle und numerische Untersuchungen zur Identifizierung des strukturellen Systems des Stahlrohrgitterturms in Vorbereitung einer Überwachungskampagne. Die Auswertung von gemessenen Schwingungen unter ambienter Anregung ermöglichte die Identifizierung der Eigenfrequenzen mehrerer globaler Schwingungsmoden des Fallturms. Zur Modellvalidierung wurde zunächst eine Sensitivitätsanalyse durchgeführt, um die Parameter mit dem größten Einfluss zu ermitteln. Anschließend wurde ein evolutionärer Algorithmus (EA) zur Optimierung nach dem Prinzip der Minimierung der Differenzen zwischen gemessenen und simulierten charakteristischen Antworten eingesetzt. Das aktualisierte Modell wurde schließlich an der dynamischen Reaktion der Turmstruktur infolge einer realen Falltest-induzierten Stoßbelastung validiert. Die Ergebnisse zeigten eine gute Übereinstimmung zwischen numerischen und experimentellen Ergebnissen.
The contribution discusses the processing and analysis of data generated on two different ways of investigations for impact damage in reinforced concrete structures. Damage investigations are essential to determine type and characteristics of damage and thus the residual capacity. Damage describing data is generated using two different types of investigation, a non-destructive tomographic as well as numerical examination. Subsequently, data of both sources was merged and analysed. Within the research project “Behaviour of structural components during impact load conditions caused by aircraft fuel tank collision” reinforces concrete plates were damaged by impact loading, see Hering (2020). Afterwards the damaged specimens were investigated tomographically as well as numerically using several methods and models. Aim of the presented research work was to specify an objective comparability of numerical data with experimentally determined damage patterns and based on this, to establish a quantitative damage evaluation.
Altematively to common modal analysis as tool for detectmg changes between a reference and an actual (possibly damaged) structural state, the subspace-based damage detection method has been developed in recent years and successfully adopted to test application data sets. Characteristic for that method is that instead of analyzing modal parameters, a Statistical test with respect to changes of a dynamic signature of structural response is introduced. Therefor, a Gaussian residual vector is extracted from the subspace of an output only Vibration data covariance matrix within the reference state. The paper describes the application of this damage detection method within a laboratory fatigue test on a Steel frame structure. Aim of the investigation was to analyze the usability and efficiency of the detection method for realistic damage on carrying structures of wind energy turbines. In a second Step, a numerical model of the lab test structure is developed and validated. Thus, a comparable numerical Simulation of the fatigue damage detection was feasible and the accuracy of the Simulation procedure could be verified. The present study describes the first Step in a two-step approach for quantifying and optimizing fundamental characteristics of SHM Systems for offshore wind turbine structures concerning a required number of sensors and their optimal location.
Subspace-based detection of fatigue damage on jacket support structures of offshore wind turbines
(2014)
The paper describes the application of the Stochastic Subspace-based Damage Detection (SSDD) method on model structures for an utilization of this approach on offshore wind turbine structures. Aim of the study was therefore to analyze the usability and efficiency of the detection method as well as to determine an optimized set of parameter for realistic damage on support structures of wind energy turbines. Based on results of an experimental fatigue test on a Steel frame laboratory structure a strategy for a numerical verification of the experimentally evolved damage detection was developed, utilizing a time integration approach to simulate the dynamic response. In a second Step the identified modeling and computing methodology is used to numerically investigate the ability to detect damage in real size structural components of offshore wind turbines.
Fault detection and isolation can be handled by many different approaches. This paper builds upon a hypothesis test that checks whether the mean of a Gaussian random vector has become non-zero in the faulty state, based on a chi2 test. For fault isolation, it has to be decided which components in the parameter set of the Gaussian vector have changed, which is done by variants of the chi2 hypothesis test using the so-called sensitivity and minmax approaches. While only the sensitivity of the tested parameter component is taken into account in the sensitivity approach, the sensitivities of all parameters are used in the minmax approach, leading to better statistical properties at the expense of an increased computational burden. The computation of the respective test variable in the minmax test is cumbersome and may be ill-conditioned especially for large parameter sets, asking hence for a careful numerical evaluation. Furthermore, the fault isolation procedure requires the repetitive calculation of the test variable for each of the parameter components that are tested for a change, which may be a significant computational burden. In this paper, dealing with the minmax problem, we propose a new efficient computation for the test variables, which is based on a simultaneous QR decomposition for all parameters. Based on this scheme, we propose an efficient test computation for a large parameter set, leading to a decrease in the numerical complexity by one order of magnitude in the total number of parameters. Finally, we show how the minmax test is useful for structural damage localization, where an asymptotically Gaussian residual vector is computed from output-only vibration data of a mechanical or a civil structure.
Bridge retrofitting of a section of the Berlin subway which is designed as railway on steel viaduct is presented. Fatigue damage in the superstructure of the over 70-years-old viaduct made an investigation of the damage causes necessary prior to the planning of retrofitting measures. The damage specifically occurred at the inverted arched steel plates of the ballast support elements. Those plates were provided for carrying the track ballast as well as the traffic load. For the retrofitting, the inverted arched plates were unloaded. The superstructure was redesigned into a fixed track system, which is able to transfer the traffic load directly into the substructure. The new rail fastening system made it necessary to verify the structural integrity of single elements as well as of the main system of the existing viaduct. The verification was based on several experimental investigations at BAM laboratory and on-site. Based on the evaluation of all test results the operation license could be issued.
Mit dem generellen Ziel, die Anwendbarkeit und Funktionalität des Verfahrens der stochastischen subspace-basierten Schadensdetektion für Strukturen von Offshore-Windenergieanlagen nachzuweisen, wurden experimentelle und numerische Untersuchungen an einer Laborstruktur sowie anschließend numerische Untersuchungen an einer OWEA-Gründungsstruktur durchgeführt.
Das zugrundeliegende Verfahren verwendet einen Ansatz, in weder modale Kenngrößen noch andere benutzerdefinierte Eingaben erfordert, nachdem einmalig der Referenzzustand aufgenommen wurde. Dabei werden ausschließlich dem Belastungsprozess inhärente stochastische Anregungen der betrachteten Struktur genutzt.
Auf der Basis experimentell erzielter Detektionsergebnisse aus einem Ermüdungsversuch an einer Laborstruktur aus Stahl wurden numerische Modelle und Berechnungsalgorithmen entwickelt und getestet. In einem zweiten Schritt wird diese Analysemethodik auf eine fiktive OWEA-Jacketstruktur realer Größe angewendet. An den simulierten Strukturantworten im Ungeschädigten und geschädigten Zustand wird der Detektionsalgorithmus angewendet, um den Einfluss von Sensorposition und Abtastrate auf die Schadensdetektion festzustellen. Unter Berücksichtigung einzelner vereinfachender Annahmen zeigten die Ergebnisse der Anwendung der vorgestellten Detektionsmethodik an den numerisch erzeugte Antwortdatensätzen eine beachtliche Empfindlichkeit des x2-Testwert basierten Schadensindikators. Obwohl nicht unter allen Umständen in einem frühen Stadium nachweisbar, typische Ermüdungsrisse in Schweißverbindungen von aufgelösten Tragwerken haben ab einer Restbiegesteifigkeit von ca. 80% einen signifikanten und detektierbaren Einfluss auf den Schadensindikator.
Künftige Forschungsaktivitäten in diesem Bereich sollten neben der Erkennung von Schäden auch die Lokalisierung und ggf. auch die Quantifizierung zum Ziel haben. Erforderlich dazu ist die Berücksichtigung und Verarbeitung der strukturellen Parameter und ihrer Sensitivitäten im zugrunde liegenden mathematischen Modell.
The modeling and prediction of the behavior of reinforced concrete under impact load is still an engineering challenge. The scientific community has put a lot of effort into the development of this knowledge, especially after the unfortunate events of 09/11 in Manhattan. The main concern is with nuclear power plants and how to design structures that can withstand such extreme situations. An experimental investigation has been carried out to collect fundamental data and to develop a deeper understanding of the effect of impact damage on the load capacity of concrete plates. The paper presents the process on the definition of procedures and the first results of an experimental investigation on the damage and residual load capacity of reinforced concrete plates after impact load. Two types of reinforced concrete plates measuring 1.5 x 1.5 x 0.3 m were subjected to the impact of a flat-nose hard projectile. The two types were casted with the same reinforcement and 80 or 40 MPa concrete. After the impact, the plates go through planar tomography, visual inspection and an ultimate load capacity test. The results showed that the planar tomography can be used for the assessment of internal damage on concrete plates, as long as the number of scans in each direction is more than four. The visual inspection gave a good. The formation of cone cracking after the impact load showed the highest influence on the remaining load capacity of the concrete plates. More plates will be tested to confirm the indications at different damage conditions.
The Stochastic Dynamic Damage Locating Vector (SDDLV) approach is a vibration-based damage localization method based on both a finite element model of a structure and modal parameters estimated
from output-only measurements in the damage and reference states. A statistical version of the Approach takes into account the inherent uncertainty due to noisy measurement data. In this paper, the effect of temperature fluctuations on the performance of the method is analyzed in a model-based approach using a finite element model with temperature dependent parameters. Robust damage localization is carried out by rejecting the temperature influence on the identified modal parameters in the damaged state. The algorithm is illustrated on a simulated structure.