7.2 Ingenieurbau
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Organisationseinheit der BAM
- 7.2 Ingenieurbau (37) (entfernen)
Eingeladener Vortrag
- nein (24)
In this presentation, a framework for integrating vibration-based structural health monitoring data into the optimization of inspection and maintenance of deteriorating structural systems is presented. The framework is demonstrated in an illustrative example considering a steel frame subject to fatigue.
Der Vortrag stellt die Durchführung und die ersten Ergebnisse der Belastungsfahrten der BAM im Rahmen des AISTEC Projekts an der Maintalbrücke am 19.05. und 20.05.2021 vor. Es wurden Tragwerksreaktionen des Bauwerks mit dem installierten Dauermonitoringsystem und insbesondere dem Betongelenk mit zusätzlich installierter Sensorik aufgezeichnet. Für die Ermittlung der Lastposition wurden mehrere Verfahren eingesetzt und verglichen.
Erschütterungen, die durch Eisenbahnverkehr erzeugt werden, breiten sich durch den Erdboden aus und regen benachbarte Gebäude zu Schwingungen an. Es wurde ein Prognoseverfahren für Bahnerschütterungen ent-wickelt, das mit einfachen Modellen schnell Ergebnisse liefert. Dies sind physikalische Modelle, zum Beispiel 1-dimensionale Modelle mit Übertragungsmatrizen für die Fahrzeug-Fahrweg-Boden-Wechselwirkung und für die Gebäude-Boden Wechselwirkung. Damit werden die komplexen Verhältnisse bei der Emission (dem Zusammen¬wirken von Fahrzeug und Fahrweg), bei der Transmission durch den homogenen oder geschichteten Boden und bei der Immission in Gebäuden näherungsweise erfasst. In (Auersch, 2020) wird gezeigt, dass diese einfachen Prognosemodelle die Ergebnisse von detaillierten Modellen wie der Finite-Element-Randelement-Methode für das Gleis, die Wellenzahlintegrale für den Boden und 3-dimensionale Finite-Element-Modelle für das Gebäude gut wiedergeben können. Dies entspricht auch der Absicht, die Prognosemodelle aus den Ergebnissen der Detailmodelle abzuleiten. Die drei Prognoseteile werden getrennt berechnet, aber es werden physikalisch sinn¬volle Schnittstellen verwendet. Für die Emission und Transmission sind es die auf den Untergrund wirkenden Erregerkräfte von Fahrzeug und Fahrweg, für die Transmission und Immission sind es die Freifeldamplituden des Bodens.
Erschütterungen, die durch Eisenbahnverkehr erzeugt werden, breiten sich durch den Erdboden aus und regen benachbarte Gebäude zu Schwingungen an. Es wurde ein Prognoseverfahren für Bahnerschütterungen ent-wickelt, das mit einfachen Modellen schnell Ergebnisse liefert. Dies sind physikalische Modelle, zum Beispiel 1-dimensionale Modelle mit Übertragungsmatrizen für die Fahrzeug-Fahrweg-Boden-Wechselwirkung und für die Gebäude-Boden Wechselwirkung. Damit werden die komplexen Verhältnisse bei der Emission (dem Zusammen¬wirken von Fahrzeug und Fahrweg), bei der Transmission durch den homogenen oder geschichteten Boden und bei der Immission in Gebäuden näherungsweise erfasst. In (Auersch, 2020) wird gezeigt, dass diese einfachen Prognosemodelle die Ergebnisse von detaillierten Modellen wie der Finite-Element-Randelement-Methode für das Gleis, die Wellenzahlintegrale für den Boden und 3-dimensionale Finite-Element-Modelle für das Gebäude gut wiedergeben können. Dies entspricht auch der Absicht, die Prognosemodelle aus den Ergebnissen der Detailmodelle abzuleiten. Die drei Prognoseteile werden getrennt berechnet, aber es werden physikalisch sinn¬volle Schnittstellen verwendet. Für die Emission und Transmission sind es die auf den Untergrund wirkenden Erregerkräfte von Fahrzeug und Fahrweg, für die Transmission und Immission sind es die Freifeldamplituden des Bodens.
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.
The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic Engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the
probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability.
Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model.
The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability.
Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model.
This presentation deals with the phenomenology and design of pile foundations for offshore wind turbines, and is divided into two lectures.
The first lecture presents a brief introduction to the context and peculiarities of such foundations, and then focuses on the particular case of axially loaded piles. This part is most relevant for the relatively slender piles of the multi-pile substructures (i.e. jackets and tripods). A clear distinction between physical phenomenology and practical design is drawn here.
his presentation deals with the phenomenology and design of pile foundations for offshore wind turbines, and is divided into two lectures.
The second lecture continues with the case of laterally loaded offshore piles, which bears most relevance for the case of the monopile foundations. Here again, a clear separation between physical reality and design methods is intended.
Finally, the last part of the second lecture introduces several advanced topics which lie outside the classical design approaches, namely the hydromechanical coupling effects (i.e. the excess pore-pressure generation around the monopiles), the cyclic pile fatigue and the so-called pile setup (i.e. the time effects on the axial pile capacity). The relevance of the latter two topics is illustrated with experimental results from a field testing campaign on real large-scale piles.
Design challenges for Offshore wind-farms. From foundation mechanics to wind-farm aerodynamics
(2021)
This talk provides a brief introduction on general engineering design challenges for the offshore wind energy production. Some general features of the offshore wind-energy field from a civil engineering perspective are firstly presented, followed by a brief discussion of some of the main geomechanical issues for the foundation of the offshore turbines into the seabed.
In the following part, an overview of relevant fluid-structure interactions and some options for an efficient numerical analysis are provided, where the hydromechanical Wave–Tower interaction is firstly discussed. Then, some general aspects of the windfarm aerodynamics are introduced. On the one hand, some modelling possibilities for the wake analysis of single turbines and turbine groups are discussed. And on the other hand the relevance of such analyses for a proper windfarm layout optimization is pointed out.
In particular, this talk shows that: i) The bearing capacity of turbine multi-pile foundations can degrade under cyclic loading (waves, wind, …), while for monopile foundations cyclic hydromechanical coupling effects may take place, which may lead to a foundation softening; ii) Numerical analysis of a turbine’s interaction with wind/waves is useful and affordable, while simplified models can already provide a useful insight into the windfarm aerodynamics. iii) Turbulent wake analysis is very relevant for the windfarm layout.
Die Linienlastgesetzmäßigkeit gilt nicht für Zuganregung. Die Punktlastgesetzmäßigkeit wird bei kurzen Zügen in größeren Entfernungen erreicht. Bei langen Zügen reduziert sich die Abnahme um r-0,3 für die theoretische exponentielle Dämpfungsabnahme, um r-0,5 für die vereinfachte potentielle Dämpfungsabnahme. Die gemessenen Abnahmereduktionen liegen in diesem Bereich.
Measurements at the foundation, the surrounding soil and nearby buildings have been done during several drop tests of different containers on different foundations. The first measurements have been done on a big foundation where it should be guaranteed that the foundation is rigid and the container is tested properly. It was controlled that the foundation does not absorb more than 2 percent of the energy of the container. Most of the drop energy is lost in shock absorbers. Later on, a smaller drop test facility has been built on the ground but inside an existing building. It had to be controlled by prediction and measurements that the drop test will not damage the building. Tests from different heights on soft, medium, and stiff targets have been done to find out rules which allow to identify acceptable and unacceptable drop tests. Later on, the biggest drop test facility has been built for masses up to 200 t. It was necessary for the design of the foundation to estimate the forces which occur during the drop tests. On the other hand, the acceptable tests should be selected and controlled by measurements where the impact duration is important. Different sensors, accelerometers, accelerometers with mechanical filters, geophones (velocity transducers), strain gauges, and pressure cells have been applied for these tasks. The signals have been transformed to displacements which proved to be best suited for the interpretation of the impact mechanism. Modell calculations have been used to check and understand the dynamic measurements. The simplest law is the conservation of the momentum which is a good approximation if the impact is short. If the soil under the foundation has an influence on the deceleration of the container, the maximum foundation velocity is lower than the simple estimation. The amplitudes of the foundation could also be estimated from the ground vibrations and their amplitude-distance law.
Measurements at the foundation, the surrounding soil and nearby buildings have been done during several drop tests of different containers on different foundations. The first measurements have been done on a big foundation where it should be guaranteed that the foundation is rigid and the container is tested properly. It was controlled that the foundation does not absorb more than 2 percent of the energy of the container. Most of the drop energy is lost in shock absorbers. Later on, a smaller drop test facility has been built on the ground but inside an existing building. It had to be controlled by prediction and measurements that the drop test will not damage the building. Tests from different heights on soft, medium, and stiff targets have been done to find out rules which allow to identify acceptable and unacceptable drop tests. Later on, the biggest drop test facility has been built for masses up to 200 t. It was necessary for the design of the foundation to estimate the forces which occur during the drop tests. On the other hand, the acceptable tests should be selected and controlled by measurements where the impact duration is important. Different sensors, accelerometers, accelerometers with mechanical filters, geophones (velocity transducers), strain gauges, and pressure cells have been applied for these tasks. The signals have been transformed to displacements which proved to be best suited for the interpretation of the impact mechanism. Modell calculations have been used to check and understand the dynamic measurements. The simplest law is the conservation of the momentum which is a good approximation if the impact is short. If the soil under the foundation has an influence on the deceleration of the container, the maximum foundation velocity is lower than the simple estimation. The amplitudes of the foundation could also be estimated from the ground vibrations and their amplitude-distance law.
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.
This article presents a solid cohesion model for the simulation of bonded granular assemblies in the frame of 3D discrete element approaches (DEM). A simple viscoplastic cohesion model for 2D geometries is extended to 3D conditions, while its yield criterion is generalized as a hyper-surface in the space of bond solicitations to include torsional moments. The model is then calibrated using experimental results of uniaxial traction at both the microscopic and macroscopic scales with an artificial granular cohesive soil. The paper finally presents some simulated results on the macromechanical sample traction application and briefly discusses the model's current limitations and promising prospects for subsequent works.
This article presents a solid cohesion model for the simulation of bonded granular assemblies in the frame of 3D discrete element approaches (DEM). A simple viscoplastic cohesion model for 2D geometries is extended to 3D conditions, while its yield criterion is generalized as a hyper-surface in the space of bond solicitations to include torsional moments. The model is then calibrated using experimental results of uniaxial traction at both the microscopic and macroscopic scales with an artificial granular cohesive soil. The paper finally presents some simulated results on the macromechanical sample traction application and briefly discusses the model's current limitations and promising prospects for subsequent works.