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Die Brückeninfrastruktur in Deutschland und Europa steht aufgrund steigender Verkehrslasten und alternder Bauwerke vor erheblichen Herausforderungen.
Das DFG-Schwerpunktprogramm 2388 „Hundert plus – Verlängerung der Lebensdauer komplexer Baustrukturen durch intelligente Digitalisierung“ (SPP100+) strebt an, durch digitale Innovationen und prädiktive Instandhaltungsstrategien die Nutzungsdauer bestehender Brückenbauwerke zu verlängern.
Der vorliegende Beitrag fokussiert sich auf das SPP100+ zugehörige Cluster „Monitoring und Simulation“, das sieben Teilprojekte umfasst. Die Projekte entwickeln fortschrittliche Methoden zur Überwachung und Zustandsbewertung von Brücken mittels Digitaler Zwillinge, hochauflösender Sensortechnik und numerischer Simulationen. Innovative Ansätze wie nichtlineare Modellanpassungen, stochastische Methoden und künstliche Intelligenz ermöglichen eine präzise und frühzeitige Identifizierung potenzieller Schäden. Die Kombination aus kontinuierlichem Bauwerksmonitoring und effizienter Datenauswertung ist entscheidend für die langfristige Sicherheit und Langlebigkeit bestehender Brücken und trägt darüber hinaus zur Ressourcenschonung bei.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.
Simulation-based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their tructural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation-based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up-to-date representation of bridge behavior, helping to inform decision-making for maintenance and management.
Da kohlenstofffaserverstärkte Kunststoffe (CFK) in anspruchsvollen sicherheitsrelevanten Einsatzgebieten wie im Automobilbau und in der Luftfahrt eingesetzt werden, besteht ein zunehmender Bedarf an zerstörungsfreien Prüfmethoden. Ziel ist die Gewährleistung der Sicherheit und Zuverlässigkeit der eingesetzten Bauteile. Aktive Thermografieverfahren ermöglichen die effiziente Prüfung großer Flächen mit hoher Auflösung in wenigen Arbeitsschritten. Ein wichtiges Teilgebiet der Prüfungen ist die Ortung und Charakterisierung von Delaminationen, die sowohl bereits in der Fertigung als auch während der Nutzung eines Bauteils auftreten können, und dessen strukturelle Integrität schwächen.
In diesem Beitrag werden CFK-Strukturen mit künstlichen und natürlichen Delaminationen mit Hilfe unterschiedlich zeitlich modulierter Strahlungsquellen experimentell untersucht. Verwendet werden dabei Anregungen mit Blitzlampen und mit frequenzmodulierten Halogenlampen. Mittels Filterfunktionen im Zeit- und Frequenzbereich wird das Kontrast-zu-Rausch-Verhältnis (CNR) der detektierten Fehlstellen optimiert. Verglichen werden anschließend die Nachweisempfindlichkeit, das CNR und die Ortsauflösung der zu charakterisierenden Delaminationen für die unterschiedlichen Anregungs- und Auswertungstechniken. Ergänzt werden die Experimente durch numerische Simulationen des dreidimensionalen Wärmetransportes.
Flash thermography is a fast and reliable non-destructive testing method for the investigation of defects in carbon fiber reinforced polymer (CFRP) materials. In this paper numerical simulations of transient thermography data are presented, calculated for a quasi-isotropic flat bottom hole sample. They are compared to experimental data. These simulations are one important step towards the quantitative reconstruction of a flaw by assessing thermographic data. The applied numerical model is based on the finite-element method, extended by a semi-analytical treatment of the boundary of the sample, which is heated by the flash light. A crucial part for a reliable numerical model is the prior determination of the material parameters of the specimen as well as of the experimental parameters of the set-up. The material parameters in plane and in depth diffusivity are measured using laser line excitation. In addition, the absorption and heat transfer process of the first layers is investigated using an IR microscopic lens. The performance of the two distinct components of CFRP during heating – epoxy resin and carbon fibers – is examined. Finally, the material parameters are optimized by variation and comparison of the simulation results to the experimental data. The optimized parameters are compared to the measured ones and further methods to ensure precise material parameter measurements are discussed.
Ray tracing boundary value problems: simulation and SAFT reconstruction for ultrasonic testing
(2016)
The application of advanced imaging techniques for the ultrasonic inspection of inhomogeneous anisotropic materials like austenitic and dissimilar welds requires information about acoustic wave Propagation through the material, in particular travel times between two Points in the material. Forward ray tracing is a popular approach to determine traveling paths and arrival times but is ill suited for inverse problems since a large number of rays have to be computed in order to arrive at prescribed end points.
In this contribution we discuss boundary value problems for acoustic rays, where the ray path between two given points is determined by solving the Eikonal equation. The implementation of such a two Point boundary value ray tracer for sound field simulations through an austenitic weld is described and its efficiency as well as the obtained results are compared to those of a forward ray tracer. The results are validated by comparison with experimental results and commercially available UT simulation tools.
As an application, we discuss an implementation of the method for SAFT (Synthetic Aperture Focusing Technique) reconstruction. The ray tracer calculates the required travel time through the anisotropic columnar grain structure of the austenitic weld. There, the formulation of ray tracing as a boundary value Problem allows a straightforward derivation of the ray path from a given transducer Position to any pixel in the reconstruction area and reduces the computational cost considerably.
Pulse thermography of concrete structures is used in civil engineering for detecting voids, honeycombing and delamination. Quantitatively realistic numerical 3D simulation is difficult due to the arising boundary layer at the heated surface and unreliable information about material parameters and environmental conditions. We address both issues by a semi-analytic reformulation of the heat transport problem and by parameter identification. Numerical results are compared with measurements of a test specimen.
Pulse thermography is a non-destructive testing method based on infrared imaging of transient thermal patterns. Heating the surface of the structure under test for a short period of time generates a non-stationary temperature distribution and thus a thermal contrast between the defect and the sound material. In modern NDT, a quantitative characterization of hidden imperfections in materials is desired. In particular, defect depth and shape are of interest. The reconstruction of the defect from thermography data is a nonlinear inverse problem, and ill-posed. We propose an algorithm for the identification of subsurface defects based on the travel time of the reflected thermal pulse. Our work extends results by Lugin and Netzelmann, taking lateral thermal flows directly into account while retrieving the defect depth. This requires significantly less computational work. Quantitative information about the defect shape and depth is obtained. Application of our method to both thermography data generated by a finite element simulation and experimental heating of PVC test specimens with different defects yields good reconstruction of the actual defects.
Simulation-based digital twins must provide accurate, robust, and reliable digital representations of their physical counterparts. Therefore, quantifying the uncertainty in their predictions plays a key role in making better-informed decisions that impact the actual system. The update of the simulation model based on data must then be carefully implemented. When applied to complex structures such as bridges, discrepancies between the computational model and the real system appear as model bias, which hinders the trustworthiness of the digital twin and increases its uncertainty. Classical Bayesian updating approaches aimed at inferring the model parameters often fail to compensate for such model bias, leading to overconfident and unreliable predictions. In this paper, two alternative model bias identification approaches are evaluated in the context of their applicability to digital twins of bridges. A modularized version of Kennedy and O'Hagan's approach and another one based on Orthogonal Gaussian Processes are compared with the classical Bayesian inference framework in a set of representative benchmarks. Additionally, two novel extensions are proposed for these models: the inclusion of noise-aware kernels and the introduction of additional variables not present in the computational model through the bias term. The integration of these approaches into the digital twin corrects the predictions, quantifies their uncertainty, estimates noise from unknown physical sources of error, and provides further insight into the system by including additional pre-existing information without modifying the computational model.