TY - CHAP A1 - Unger, Jörg F. A1 - Könke, Carsten ED - Gürlebeck, Klaus ED - Könke, Carsten T1 - DISCRETE CRACK SIMULATION OF CONCRETE USING THE EXTENDED FINITE ELEMENTMETHOD N2 - The extended finite element method (XFEM) offers an elegant tool to model material discontinuities and cracks within a regular mesh, so that the element edges do not necessarily coincide with the discontinuities. This allows the modeling of propagating cracks without the requirement to adapt the mesh incrementally. Using a regular mesh offers the advantage, that simple refinement strategies based on the quadtree data structure can be used to refine the mesh in regions, that require a high mesh density. An additional benefit of the XFEM is, that the transmission of cohesive forces through a crack can be modeled in a straightforward way without introducing additional interface elements. Finally different criteria for the determination of the crack propagation angle are investigated and applied to numerical tests of cracked concrete specimens, which are compared with experimental results. KW - Architektur KW - CAD KW - Computerunterstütztes Verfahren Y1 - 2006 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170327-30303 UR - http://euklid.bauing.uni-weimar.de/ikm2006/index.php_lang=de&what=papers.html ER - TY - JOUR A1 - Unger, Jörg F. A1 - Eckardt, Stefan A1 - Könke, Carsten T1 - Modelling of cohesive crack growth in concrete structures with the extended finite element method JF - Computer Methods in Applied Mechanics and Engineering N2 - Modelling of cohesive crack growth in concrete structures with the extended finite element method KW - Angewandte Mathematik KW - Strukturmechanik Y1 - 2007 SP - 4087 EP - 4100 ER - TY - JOUR A1 - Unger, Jörg F. A1 - Könke, Carsten T1 - Coupling of scales in a multiscale simulation using neural networks JF - Computers & Structures N2 - Coupling of scales in a multiscale simulation using neural networks KW - Angewandte Mathematik KW - Strukturmechanik Y1 - 2008 ER - TY - CHAP A1 - Unger, Jörg F. A1 - Könke, Carsten ED - Gürlebeck, Klaus ED - Könke, Carsten T1 - PARAMETER IDENTIFICATION OF MESOSCALE MODELS FROM MACROSCOPIC TESTS USING BAYESIAN NEURAL NETWORKS N2 - In this paper, a parameter identification procedure using Bayesian neural networks is proposed. Based on a training set of numerical simulations, where the material parameters are simulated in a predefined range using Latin Hypercube sampling, a Bayesian neural network, which has been extended to describe the noise of multiple outputs using a full covariance matrix, is trained to approximate the inverse relation from the experiment (displacements, forces etc.) to the material parameters. The method offers not only the possibility to determine the parameters itself, but also the accuracy of the estimate and the correlation between these parameters. As a result, a set of experiments can be designed to calibrate a numerical model. KW - Angewandte Informatik KW - Angewandte Mathematik KW - Architektur KW - Computerunterstütztes Verfahren KW - Computer Science Models in Engineering; Multiscale and Multiphysical Models; Scientific Computing Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20170314-28984 UR - http://euklid.bauing.uni-weimar.de/ikm2009/paper.html SN - 1611-4086 ER - TY - JOUR A1 - Könke, Carsten A1 - Eckardt, Stefan A1 - Häfner, Stefan A1 - Luther, Torsten A1 - Unger, Jörg F. T1 - Multiscale simulation methods in damage prediction of brittle and ductile materials JF - International Journal for Multiscale Computational Engineering N2 - Multiscale simulation methods in damage prediction of brittle and ductile materials KW - Angewandte Mathematik KW - Strukturmechanik Y1 - 2010 SP - 17 EP - 36 ER -