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Die Brücken im Netz der Bundesverkehrswege sind überwiegend in einem ausreichenden bis guten Zustand. Allerdings steigt der Unterhalts- und Sanierungsaufwand aufgrund des inzwischen hohen Alters vieler Brücken sowie des ständig wachsenden Schwerlastverkehrs. Techniken zur Einschätzung der verbleibenden Lebensdauer von Brücken sowie zur dauerhaften Beobachtung des Tragverhaltens bzw. des Erfolges von Sanierungsmaßnahmen werden daher für den sicheren und wirtschaftlichen Betrieb dringend benötigt. Zur Evaluierung dafür geeigneter holistischer Ansätze wurde in der BAM das Projekt BLEIB - Bewertung, Lebensdauerprognose und Instandsetzung von Brückenbauwerken - ins Leben gerufen.
Ein zentrales Ergebnis des Projektes ist eine extern vorgespannte Stahlbetonbrücke als Zweifeldträger mit einer Gesamtlänge von 24 m, die für den Test verschiedenster Sensorsysteme, zur Validierung numerischer Modelle und zur Erprobung von Sanierungs- und Verstärkungsmaßnahmen entwickelt wurde. Für die Simulation unterschiedlicher Schädigungsgrade kann die Vorspannung der Brücke variiert werden. Die Brücke wird mit beweglichen Gewichten belastet und über einen Shaker zum Schwingen angeregt.
Das Brückenmodell wurde bewusst geschädigt, indem die Vorspannung der Struktur erstmalig schrittweise bis auf null reduziert wurde. Unter der Eigenlast verformte sich die Brücke, wodurch eine Rissbildung im Beton einsetzte. Die Zugspannung, die zuvor durch die Vorspannung aufgenommen wurde, übernahm Schritt für Schritt der Beton. Als die Zugspannungen die relativ geringe Zugfestigkeit des Betons überstiegen, begann dieser zu reißen und die schlaffe Bewehrung der Struktur nahm die Spannungen auf. Dieser Versuch wurde unter anderem von Schallemissionsmessungen begleitet. Der Rissbildungsprozess konnte damit, bei gleichzeitiger Aufzeichnung der Vorspannung, früh detektiert und die Risse geortet werden. Die Ergebnisse korrelieren gut mit den Ergebnissen der stereophotogrammetrischen Verformungsmessungen der Struktur.
Die sprunghaft zunehmende Wichtigkeit von FAIR- und Open-Data für die Qualitätssicherung, aber auch für die Nachnutzbarkeit von Daten und den Erkenntnisfortschritt führt zu enormem Flandlungsbedarf in Forschung und Entwicklung. Damit verbunden laufen derzeit vielfältige, ambitionierte Aktionen, z. B. bezüglich der Erstellung von Ontologien und Wissensgraphen. Das Knowhow entwickelt sich rasant, die Ansätze zur Implementation entstehen in verschiedenen Fachwelten bzw. mit
unterschiedlichen Zielsetzungen parallel, so dass recht heterogene Herangehensweisen resultieren.
Diese Veröffentlichung fokussiert auf Arbeiten, die derzeit als möglichst ganzheitlicher Ansatz für Materialdaten im Rahmen der Digitalisierungsinitiative „Plattform MaterialDigital" vorangetrieben werden. Die Autoren bearbeiten baustoffbezogene Aspekte im Verbundprojekt „LeBeDigital - Lebenszyklus von Beton". Zielsetzung ist die digitale Beschreibung des Materialverhaltens von Beton über den kompletten Herstellungsprozess eines Fertigteils mit einer Integration von Daten und Modellen innerhalb eines Workflows zur probabilistischen Material- und Prozessoptimierung.
Es wird über die Vorgehensweise und die dabei gewonnenen Erfahrungen berichtet, nicht ohne den Blick auf die oft unterschätzte Komplexität der Thematik zu lenken.
Numerical simulators, such as finite element models, have become increasingly capable of predicting the behaviour of structures and components owing to more sophisticated underlying mathematical models and advanced computing power. A common challenge lies, however, in calibrating these models in terms of their unknown/uncertain parameters. When measurements exist, this can be achieved by comparing the model response against measured data. Besides uncertain model parameters, phenomena like damage can give rise to further uncertainties; in particular, quasi-brittle materials, like concrete, experience damage in a heterogeneous manner due to various imperfections, e.g. in geometry and boundary conditions. This hardens an accurate prediction of the damaged behaviour of real structures that comprise such materials.
In this study, which draws from a data-driven approach, we use the force-version of the finite element model updating method (FEMU-F) to incorporate measured displacements into the identification of the damage parameters, in order to cope with heterogeneity. In this method, instead of conducting a forward evaluation of the model and comparing the model response (displacements) against the data, we impose displacements to the model and compare the resulting force residuals with measured reaction forces. To account for uncertainties in the measurement of displacements, we endow this approach with a penalty term, which reflects the discrepancy between measured and imposed displacements, where the latter is assumed as unknown random variables to be identified as well. A Variational Bayesian approach is used as an approximating tool for computing posterior parameters. The underlying damage model considered in this work is a gradient-enhanced damage model.
We first establish the identification procedure through two virtual examples, where synthetic data (displacements) are generated over a certain spatially-dense set of points over the domain. The procedure is then validated on an experimental case-study; namely a 3-point bending experiment with displacement measurements resulting from a digital image correlation (DIC) analysis.
Appropriate monitoring of transportation infrastructures (e.g. bridges) is of utmost importance to ensure safe operation conditions. Accurate and reliable assessment of such structures can be achieved through the integration of data from non-destructive testing, advanced modeling and model updating techniques. The Bayesian framework has been widely used for updating engineering and mechanical models, due to its probabilistic description of information, in which the posterior probability distribution reflects the knowledge, over the model parameters of interest, inferred from the data. For most real-life applications, the computation of the true posterior involves integrals that are analytically intractable, therefore the implementation of Bayesian inference requires in practice some approximation methods.
This paper investigates the application of Variational Bayesian Inference for structural model parameter identification and update, based on measurements from a real experimental setup. The Variational Bayesian method circumvents the issue of evaluating intractable integrals by using a factorized approximation of the true posterior (mean field approximation) and by choosing a family of conjugate distributions that facilitates the calculations. Inference in the Variational Bayesian framework is seen as solving an optimization problem with the aim of finding the parameters of the factorized posterior which would minimize its Kullback-Leibler divergence in relation to the exact posterior. The Variational Approach is an efficient alternative to sampling methods, such as Markov Chain Monte Carlo, since the latter’s accuracy depends on sampling from the posterior distribution a sufficient amount of times (and therefore requiring an equivalent number of computations of the forward problem, which can be quite expensive).
Appropriate monitoring of transportation infrastructures (e.g. bridges) is of utmost importance to ensure safe operation conditions. Accurate and reliable assessment of such structures can be achieved through the integration of data from non-destructive testing, advanced modeling and model updating techniques. The Bayesian framework has been widely used for updating engineering and mechanical models, due to its probabilistic description of information, in which the posterior probability distribution reflects the knowledge, over the model parameters of interest, inferred from the data. For most real-life applications, the computation of the true posterior involves integrals that are analytically intractable, therefore the implementation of Bayesian inference requires in practice some approximation methods.
This paper investigates the application of Variational Bayesian Inference for structural model parameter identification and update, based on measurements from a real experimental setup. The Variational Bayesian method circumvents the issue of evaluating intractable integrals by using a factorized approximation of the true posterior (mean field approximation) and by choosing a family of conjugate distributions that facilitates the calculations. Inference in the Variational Bayesian framework is seen as solving an optimization problem with the aim of finding the parameters of the factorized posterior which would minimize its Kullback-Leibler divergence in relation to the exact posterior. The Variational Approach is an efficient alternative to sampling methods, such as Markov Chain Monte Carlo, since the latter’s accuracy depends on sampling from the posterior distribution a sufficient amount of times (and therefore requiring an equivalent number of computations of the forward problem, which can be quite expensive).
In this work an approach for smoothing the oscillations of normal impact is presented. In addition, a higher order time discretization scheme in association with a higher order spatial discretization, like the spectral element method, is investigated regarding its convergence rates.
Concrete is a complex material. Its properties evolve over time, especially at early age, and are dependent on environmental conditions, i.e. temperature and moisture conditions, as well as the composition of the material.
This leads to a variety of macroscopic phenomena such as hydration/solidification/hardening, creep and shrinkage, thermal strains, damage and inelastic deformations. Most of these phenomena are characterized by specific set of model assumptions and often an additive decomposition of strains into elastic, plastic, shrinkage and creep components is performed. Each of these phenomena are investigated separately and a number of respective independent models have been designed. The interactions are then accounted for by adding appropriate correction factors or additional models for the particular interaction. This paper discusses the importance of reconsider even in the experimental phase the model assumptions required to generalize the experimental data into models used in design codes. It is especially underlined that the complex macroscopic behaviour of concrete is strongly influenced by its multiscale and multiphyscis nature and two examples (shrinkage and fatigue) of interacting phenomena are discussed.
Concrete is a complex material. Its properties evolve over time, especially at early age, and are dependent on environmental conditions, i.e. temperature and moisture conditions, as well as the composition of the material.
This leads to a variety of macroscopic phenomena such as hydration/solidification/hardening, creep and shrinkage, thermal strains, damage and inelastic deformations. Most of these phenomena are characterized by specific set of model assumptions and often an additive decomposition of strains into elastic, plastic, shrinkage and creep components is performed. Each of these phenomena are investigated separately and a number of respective independent models have been designed. The interactions are then accounted for by adding appropriate correction factors or additional models for the particular interaction. This paper discusses the importance of reconsider even in the experimental phase the model assumptions required to generalize the experimental data into models used in design codes. It is especially underlined that the complex macroscopic behaviour of concrete is strongly influenced by its multiscale and multiphyscis nature and two examples (shrinkage and fatigue) of interacting phenomena are discussed.
Concrete is one of the most important building materials world wide. The safety of constructions build from concrete is of utmost importance in daily life. As a consequence, accurate predictions of the structural behavior over the entire lifetime of concrete structures are required to ensure a prescribed safety level. A lack of exact models and/or stochastically varying constitutive parameters are compensated by large safety factors.
The nonlinear structural performance is strongly related to the constitutive behavior of concrete. Arbitrary complex models can be used to describe the macroscopic constitutive behavior of concrete. The parameters in these models often lack any physical meaning. Consequently, the fitting can only be performed by an inverse analysis. In contrast, models on finer scales are able to simulate the physical phenomena more accurately and are thus better suited to understand the failure mechanisms. In addition, the macroscopically observed strong nonlinearities can at least partially be explained by the direct modeling of the material heterogeneities on finer scales.
The presentation discusses several phenomena that are strongly related to the internal microstructure of concrete. This includes the discrepancy between the unique results of a numerical model and the stochastic scatter observed in real experiments. A short discussion on the generation of random mesoscale geometries to model aggregates and mortar matrix explicitly and random fields are given. The strong nonlinearities especially for stresses close to the peak strength are usually the result of failure in the mortar matrix or the interfacial transition zone, whereas the aggregates are inert and often can accurately be modeled by a linear elastic model. The different constitutive properties lead to eigenstresses that strongly in uence the macroscopic behavior. In addition, this effect is even more pronounced when dealing with multiphysics phenomena such as drying, creep and shrinkage, fatigue or thermal problems. It will be demonstrated for several examples that simple models on the fine scale can be superimposed and coupled to obtain a macroscopically nonlinear behavior, where the superposition principle does not hold any longer. Finally, a short discussion on upscaling techniques to couple mesoscale models with large scale structural problems is given.
Structural build-up describes the stability and early-age strength development of fresh mortar used in 3D printing. lt is influenced by several factors, i.e. the composition of the print able material, the printing regime, and the ambient conditions. The existing modelling approaches for structural build-up usually define the model parameters for a specific material composition with out considering the influence of the ambient conditions. The goal of this contribution is to explicitly include the temperature dependency in the modelling approach. Temperature changes have signifi cant impact on the structural build-up process: an increase of the temperature leads to a faster dissol ution of cement phases and accelerates hydration. The proposed extended model includes temperature dependency using the Arrhenius theory. The new model parameters are successfully calibrated based on Viskomat measurement data using Bayesian inference. Furthermore, a higher impact of the temperature in the re-flocculation as in the structuration stage is observed.