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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.
Die moderne Gesellschaft in Deutschland und Europa profitiert von der hervorragenden baulichen Infrastruktur, die für uns viele Aspekte aus den Bereichen Mobilität, Energieversorgung, Transport, Umweltschutz, (Nah-)Erholung und Gefahrenabwehr zur Alltäglichkeit werden lassen und uns ein hohes Gefühl der technischen Sicherheit vermitteln. Viele der Annehmlichkeiten, wie beispielweise ein Hochgeschwindigkeitseisenbahnverkehrsnetz, zuverlässige Stromversorgung im europäischen Verbundsystem und ein immenser Personen-, Güter- und Warenverkehr auf der Straße, zu Wasser und in der Luft ist ohne eine leistungsfähige, resiliente und zuverlässige bauliche Infrastruktur undenkbar.
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.
Today, short- and long-term structural health monitoring (SHM) of bridge infrastructures and their safe, reliable and cost-effective maintenance has received considerable attention. From a surveying or civil engineer’s point of view, vibration-based SHM can be conducted by inspecting the changes in the global dynamic behaviour of a structure, such as natural frequencies (i. e. eigenfrequencies), mode shapes (i. e. eigenforms) and modal damping, which are known as modal parameters. This research work aims to propose a robust and automatic vibration analysis procedure that is so-called robust time domain modal parameter identification (RT-MPI) technique. It is novel in the sense of automatic and reliable identification of initial eigenfrequencies even closely spaced ones as well as robustly and accurately estimating the modal parameters of a bridge structure using low numbers of cost-effective micro-electro-mechanical systems (MEMS) accelerometers. To estimate amplitude, frequency, phase shift and damping ratio coefficients, an observation model consisting of: (1) a damped harmonic oscillation model, (2) an autoregressive model of coloured measurement noise and (3) a stochastic model in the form of the heavy-tailed family of scaled t-distributions is employed and jointly adjusted by means of a generalised expectation maximisation algorithm. Multiple MEMS as part of a geo-sensor network were mounted at different positions of a bridge structure which is precalculated by means of a finite element model (FEM) analysis. At the end, the estimated eigenfrequencies and eigenforms are compared and validated by the estimated parameters obtained from acceleration measurements of high-end accelerometers of type PCB ICP quartz, velocity measurements from a geophone and the FEM analysis. Additionally, the estimated eigenfrequencies and modal damping are compared with a well-known covariance driven stochastic subspace identification approach, which reveals the superiority of our proposed approach. We performed an experiment in two case studies with simulated data and real applications of a footbridge structure and a synthetic bridge. The results show that MEMS accelerometers are suitable for detecting all occurring eigenfrequencies depending on a sampling frequency specified. Moreover, the vibration analysis procedure demonstrates that amplitudes can be estimated in submillimetre range accuracy, frequencies with an accuracy better than 0.1 Hz and damping ratio coefficients with an accuracy better than 0.1 and 0.2 % for modal and system damping, respectively.
The identification of the dynamic behavior of structures, like bridges and towers, is relevant to address multiple issues. In many cases the dynamic parameters should be acquired only once or at a frequency that doesn’t justify the installation of distinct vibration sensors for a long-term monitoring. To identify modal frequencies of a structure, a drone based mobile sensing platform has been implemented. This sensing platform measures the relative displacement be-tween the structure and the drone, which also shows a strong dynamic behavior under wind tur-bulences. By regarding the dynamic model of the drone and additional measurements at the dis-tance sensor the absolute movement of the structure can be estimated based on the measured relative distance. This time domain data is a suitable input for various operational modal analysis algorithms. The system has been used to identify the dynamic properties of test and real structure, like a 1.5 MW wind turbine tower.
The identification of the dynamic behavior of structures, like bridges and towers, is relevant to address multiple issues. In many cases the dynamic parameters should be acquired only once or at a frequency that doesn’t justify the installation of distinct vibration sensors for a long-term monitoring. To identify modal frequencies of a structure, a drone based mobile sensing platform has been implemented. This sensing platform measures the relative displacement be-tween the structure and the drone, which also shows a strong dynamic behavior under wind tur-bulences. By regarding the dynamic model of the drone and additional measurements at the dis-tance sensor the absolute movement of the structure can be estimated based on the measured relative distance. This time domain data is a suitable input for various operational modal analysis algorithms. The system has been used to identify the dynamic properties of test and real structure, like a 1.5 MW wind turbine tower.
At the Bundesanstalt für Materialforschung und -prüfung (BAM) full scale specimens for nuclear transport and storage containers (casks) are tested for their structural integrity in a series of drop tests on the Test Site Technical Safety in Horstwalde, 50 km south of Berlin. These drop tests cause a major stress not only on the casks, but also on the steel tower structure of the test facility, itself. The load pattern makes the structure very interesting for detailed investigation. The focus of the monitoring lies on the bolted joints of the flange connections that are a typical connection for cylindrical elements if welding is technical or economical unfavorable. The definition of the monitoring takes was done by investigating the existing documents and inspection results accompanied by building an initial digital representation of the structure, consisting of two finite element (FE) models and a geometrical 3D point cloud representation. As a first step the structures behavior during static and dynamic loading was analyzed using measurement data and an updated numerical FE Model. The idea behind is to use models for a digital planning and operation/evaluation of the structural health monitoring. A static FE simulation and a dynamic FE simulation are generated, to investigate how the structure behaves under the load conditions.
At the Bundesanstalt für Materialforschung und -prüfung (BAM) full scale specimens for nuclear transport and storage containers (casks) are tested for their structural integrity in a series of drop tests on the Test Site Technical Safety in Horstwalde, 50 km south of Berlin. These drop tests cause a major stress not only on the casks, but also on the steel tower structure of the test facility, itself. The load pattern makes the structure very interesting for detailed investigation. The focus of the monitoring lies on the bolted joints of the flange connections that are a typical connection for cylindrical elements if welding is technical or economical unfavorable. The definition of the monitoring takes was done by investigating the existing documents and inspection results accompanied by building an initial digital representation of the structure, consisting of two finite element (FE) models and a geometrical 3D point cloud representation. As a first step the structures behavior during static and dynamic loading was analyzed using measurement data and an updated numerical FE Model. The idea behind is to use models for a digital planning and operation/evaluation of the structural health monitoring. A static FE simulation and a dynamic FE simulation are generated, to investigate how the structure behaves under the load conditions.