Filtern
Erscheinungsjahr
Dokumenttyp
- Beitrag zu einem Tagungsband (43) (entfernen)
Schlagworte
- Structural health monitoring (6)
- Damage detection (5)
- Damage localization (4)
- Model interpolation (4)
- Structural Health Monitoring (4)
- Fault detection (3)
- Schadensdetektion (3)
- Temperature rejection (3)
- Belastungsversuch (2)
- Cable failure (2)
Organisationseinheit der BAM
- 7 Bauwerkssicherheit (25)
- 7.2 Ingenieurbau (25)
- 8 Zerstörungsfreie Prüfung (6)
- 3 Gefahrgutumschließungen; Energiespeicher (2)
- 3.3 Sicherheit von Transportbehältern (2)
- 7.4 Baustofftechnologie (2)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (2)
- 8.5 Röntgenbildgebung (2)
- 8.0 Abteilungsleitung und andere (1)
- 8.1 Sensorik, mess- und prüftechnische Verfahren (1)
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.
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.
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.