7.2 Ingenieurbau
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Erscheinungsjahr
- 2021 (42) (entfernen)
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Organisationseinheit der BAM
- 7.2 Ingenieurbau (42) (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.
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.