@inproceedings{KoenigMueller,
  author    = {K{\"o}nig, Reinhard and M{\"u}ller, Daniela},
  title     = {Simulating the development of residential areas of the city of Vienna from 1888 to 2001},
  series = {Compendium of Abstracts of the 8th International Conference on Urban Planning and Environment (UPE8)},
  booktitle = {Compendium of Abstracts of the 8th International Conference on Urban Planning and Environment (UPE8)},
  address   = {Kaiserslautern, Germany},
  doi       = {10.25643/bauhaus-universitaet.2606},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20160622-26066},
  pages     = {23},
  abstract  = {The structure and development of cities can be seen and evaluated from different points of view. By replicating the growth or shrinkage of a city using historical maps depicting different time states, we can obtain momentary snapshots of the dynamic mechanisms of the city. An examination of how these snapshots change over the course of time and a comparison of the different static time states reveals the various interdependencies of population density, technical infrastructure and the availability of public transport facilities. Urban infrastructure and facilities are not distributed evenly across the city - rather they are subject to different patterns and speeds of spread over the course of time and follow different spatial and temporal regularities. The reasons and underlying processes that cause the transition from one state to another result from the same recurring but varyingly pronounced hidden forces and their complex interactions. Such forces encompass a variety of economic, social, cultural and ecological conditions whose respective weighting defines the development of a city in general. Urban development is, however, not solely a product of the different spatial distribution of economic, legal or social indicators but also of the distribution of infrastructure. But to what extent is the development of a city affected by the changing provision of infrastructure? As},
  subject      = {Simulation},
  language  = {en}
}
@phdthesis{Unger2009,
  author    = {Unger, J{\"o}rg F.},
  title     = {Neural networks in a multiscale approach for concrete},
  doi       = {10.25643/bauhaus-universitaet.1392},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20090626-14763},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  year      = {2009},
  abstract  = {From a macroscopic point of view, failure within concrete structures is characterized by the initiation and propagation of cracks. In the first part of the thesis, a methodology for macroscopic crack growth simulations for concrete structures using a cohesive discrete crack approach based on the extended finite element method is introduced. Particular attention is turned to the investigation of criteria for crack initiation and crack growth. A drawback of the macroscopic simulation is that the real physical phenomena leading to the nonlinear behavior are only modeled phenomenologically. For concrete, the nonlinear behavior is characterized by the initiation of microcracks which coalesce into macroscopic cracks. In order to obtain a higher resolution of this failure zones, a mesoscale model for concrete is developed that models particles, mortar matrix and the interfacial transition zone (ITZ) explicitly. The essential features are a representation of particles using a prescribed grading curve, a material formulation based on a cohesive approach for the ITZ and a combined model with damage and plasticity for the mortar matrix. Compared to numerical simulations, the response of real structures exhibits a stochastic scatter. This is e.g. due to the intrinsic heterogeneities of the structure. For mesoscale models, these intrinsic heterogeneities are simulated by using a random distribution of particles and by a simulation of spatially variable material parameters using random fields. There are two major problems related to numerical simulations on the mesoscale. First of all, the material parameters for the constitutive description of the materials are often difficult to measure directly. In order to estimate material parameters from macroscopic experiments, a parameter identification procedure based on Bayesian neural networks is developed which is universally applicable to any parameter identification problem in numerical simulations based on experimental results. This approach offers information about the most probable set of material parameters based on experimental data and information about the accuracy of the estimate. Consequently, this approach can be used a priori to determine a set of experiments to be carried out in order to fit the parameters of a numerical model to experimental data. The second problem is the computational effort required for mesoscale simulations of a full macroscopic structure. For this purpose, a coupling between mesoscale and macroscale model is developed. Representative mesoscale simulations are used to train a metamodel that is finally used as a constitutive model in a macroscopic simulation. Special focus is placed on the ability of appropriately simulating unloading.},
  subject      = {Beton},
  language  = {en}
}