7.2 Ingenieurbau
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- 7.2 Ingenieurbau (169) (entfernen)
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.
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.
The attenuation of wave amplitudes is ruled by the planar, cylindrical or spher-ical geometry of the wave front (the geometric or power-law attenuation) but also by the damping of the soil (an exponential attenuation). Several low- and high-frequency filter effects are derived for the layering and the damping of the soil, for the moving static and the distributed train loads and for a homoge-neous or randomly heterogeneous soil. Measurements of hammer- and train-induced vibrations at five sites have been analysed for these attenuation and filter effects. The measured attenuation with distance can be discribed by gen-eralised power laws and some reasons will be discussed. The theoretical filter effects can well be found in the measurements.
Vibration measurements have many causes and many technical and natural sources. Problems can sometimes be solved by short-term measurements, but in many cases, a long-term measurement is necessary. In long-term measurements of days, weeks, months and even years, it is easy to collect a huge quantity of raw data, but at the end, the post-processing of these data can be exhausting (for example one-year vibration data of a wind energy tower). A software has been developed which con-sists of measuring and evaluation routines where the measuring routines can operate different meas-uring systems and different measuring cards. The main advantage of this software is the fact that the interesting evaluations can be integrated in the measuring process so that the characteristics of the vibration can be extracted without storing all the raw data. Only important time segments are stored, for example train passages. The overall concept of the software and the main evaluation routines will be described in some details. Examples of our measurement experience will illustrate the capabilities of the software. 1) Surveying construction work in nearby sensitive buildings (for example an old wind tunnel), including a stable alarm system and meaningful vibration limits. 2) Prediction of train-induced vibration for a planned building to prevent annoyance and to improve the building design. 3) Modal analysis and long term measurements of several single- or multi-span, concrete or steel bridges 4) Modal and wave analysis of coupled floors in a historical building (“Neues Palais” at Potsdam). 5) Soil properties of various measurement sites (different routines to evaluate the dispersion). Moreover, from many projects, amplitudes, frequencies, and attenuation laws have been collected and analysed for the different sources such as vibratory or impact pile driving and ground compaction, demolition work with different machines, blasting in quarries and in tunnel works, bomb and mine clearing.
Usually, geometric irregularities are considered as the main cause of ground vibrations from trains. A varying stiffness of the track, the track support and the soil can also generate ground vibrations. The regular stiffness variation of the track on and between the sleepers results in a deterministic dynamic axle load. The random stiffness variation of the track support yields also dynamic axle loads. The dynamic axle loads are generated by the varying wheel displacements under the static axle load by the acceleration of the unsprung mass of the rail vehicle. The random stiffness variation has a second effect. The pulses from the passage of the static axle loads are superposed regularly to the quasi-static response, but also irregularly to yield a “scattered” part of the axle pulses. The same holds for a random variation of the soil stiffness. All these effects of stiffness variations have been calculated by wavenumber-domain multi-beam track models, a random finite-element soil model and the superposition of axle impulses in a stochastic simulation. The results are confronted with many measurements at different sites. It is concluded that the stiffness variation of the track and the soil generate an important ground vibration component near railway lines.
A simple and fast prediction scheme is presented for train-induced ground and building vibrations. For the emission, finite-element boundary-element or multiple-beam-on-continuous-soil models of the track have been analysed and approximated by faster track-on-Winkler-soil models. The vehicle-track interaction due to irregularities yields the excitation forces. For the transmission of waves in the soil, the wavenumber integral of the compliance of layered soils has been evaluated. The calculation time is reduced for the prediction by using the solution of a homogeneous half-space with a frequency-dependent wave velocity (the dispersion) of the soil. For the immision, many 2 and 3-dimenisonal finite-element building models have been investigated, and a good approximation has been established by a 1-dimensional soil-wall-floor model. In addition, the axle sequence of the train, the quasi-static and the “scattered” response of the soil, and the wave propagation from a tunnel to a pile foundation of a building have been included.
This talk provides a brief overview on some geomechanical phenomena and problematic issues in the field of offshore wind-energy geotechnics, with reference to their associated challenges for a numerical analysis/simulation. These may include large deformations, fluid coupling and grain-scale phenomena, all of which are generally difficult to be addressed with conventional FE techniques.
These challenges are illustrated here with a practical example for the retrofit of axially loaded piles using compaction grouting techniques. In this case, the mechanical effects of the retrofit may be analysed with standard FE techniques, but require the adoption of strong assumptions, while the injection process itself can only be analysed with special techniques such as the MPM.
Finally, an LBM-DEM framework for the micromechanical analysis of such problems is introduced and a practical application for the estimation of the soil resistance to driving (SRD) in layered profiles is discussed.
Es wird eine gekoppelte Finite-Element-Randelementmethode zur Berechnung von Pfahlgrün-dungen in inhomogenen (geschichteten) Böden vorgestellt. Sie beruht auf den Greenschen Funktionen (Punktlastlösungen) für inhomogene Böden. Diese Lösungen können auch für die Wellenausbreitung in der Tiefe, zum Beispiel von einem Bahntunnel zu einem eingebetteten Gebäude, dem Kellergeschoss benutzt werden. Die Punktlastlösungen in der Tiefe werden mit der Halbraumlösung an der Bodenoberfläche und mit der Vollraumlösung verglichen und Gesetzmäßigkeiten für geschichtete Böden abgeleitet. Zu den Pfahlgründungen werden die Horizontalnachgiebigkeiten von Pfählen in geschichteten Böden dargestellt. Für den homoge-nen und den kontinuierlich steifer werdenden Boden werden Potenzgesetze für den Boden- und Pfahleinfluss aufgestellt. Der Vergleich mit dem Winkler-Modell der rein lokalen Boden-reaktion zeigt, dass die Winkler-Bettung in allen Fällen einen zu kleinen Bodeneinfluss ergibt.
Die Definition und Beschreibung der Einfügedämmung im Normentwurf DIN 45673-4 ist noch nicht richtig. Es wird die Beschreibung aus DIN 45673-3 herangezogen, die für Messungen gilt. Für die drei Rechenverfahren gibt es jeweils eine passende Beschreibung. Mit diesen Vorlagen ist eine vernünftige Definition der Einfügungsdämmung zu finden. Es bedarf einer Abgrenzung gegenüber anderen (falschen) Möglichkeiten. Des Weiteren ist der Anhang 2 erweitert und der Parametersatz im Anhang 1 auf das Wesentliche reduziert worden.