@inproceedings{AlmamouGebhardtBocketal.,
  author    = {Almamou, Abd Albasset and Gebhardt, Thomas and Bock, Sebastian and Hildebrand, J{\"o}rg and Schwarz, Willfried},
  title     = {QUALITY CONTROL OF CONSTRUCTED MODELS USING 3D POINT CLOUD},
  series = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar},
  booktitle = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar},
  editor    = {G{\"u}rlebeck, Klaus and Lahmer, Tom},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2794},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-27944},
  pages     = {9},
  abstract  = {Over the last decade, the technology of constructing buildings has been dramatically developed especially with the huge growth of CAD tools that help in modeling buildings, bridges, roads and other construction objects. Often quality control and size accuracy in the factory or on construction site are based on manual measurements of discrete points. These measured points of the realized object or a part of it will be compared with the points of the corresponding CAD model to see whether and where the construction element fits into the respective CAD model. This process is very complicated and difficult even when using modern measuring technology. This is due to the complicated shape of the components, the large amount of manually detected measured data and the high cost of manual processing of measured values. However, by using a modern 3D scanner one gets information of the whole constructed object and one can make a complete comparison against the CAD model. It gives an idea about quality of objects on the whole. In this paper, we present a case study of controlling the quality of measurement during the constructing phase of a steel bridge by using 3D point cloud technology. Preliminary results show that an early detection of mismatching between real element and CAD model could save a lot of time, efforts and obviously expenses.},
  subject      = {Angewandte Informatik},
  language  = {en}
}
@inproceedings{MotraDimmigOsburgHildebrand,
  author    = {Motra, Hem Bahadur and Dimmig-Osburg, Andrea and Hildebrand, J{\"o}rg},
  title     = {UNCERTAINTY QUANTIFICATION IN CYCLIC CREEP PREDICTION OF CONCRETE},
  series = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 04 - 06 2012, Bauhaus-University Weimar},
  booktitle = {Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 04 - 06 2012, Bauhaus-University Weimar},
  editor    = {G{\"u}rlebeck, Klaus and Lahmer, Tom and Werner, Frank},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2780},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-27803},
  pages     = {18},
  abstract  = {This paper presents a methodology for uncertainty quantification in cyclic creep analysis. Several models- , namely BP model, Whaley and Neville model, modified MC90 for cyclic loading and modified Hyperbolic function for cyclic loading are used for uncertainty quantification. Three types of uncertainty are included in Uncertainty Quantification (UQ): (i) natural variability in loading and materials properties; (ii) data uncertainty due to measurement errors; and (iii) modelling uncertainty and errors during cyclic creep analysis. Due to the consideration of all type of uncertainties, a measure for the total variation of the model response is achieved. The study finds that the BP, modified Hyperbolic and modified MC90 are best performing models for cyclic creep prediction in that order. Further, global Sensitivity Analysis (SA) considering the uncorrelated and correlated parameters is used to quantify the contribution of each source of uncertainty to the overall prediction uncertainty and to identifying the important parameters. The error in determining the input quantities and model itself can produce significant changes in creep prediction values. The variability influence of input random quantities on the cyclic creep was studied by means of the stochastic uncertainty and sensitivity analysis namely the Gartner et al. method and Saltelli et al. method. All input imperfections were considered to be random quantities. The Latin Hypercube Sampling (LHS) numerical simulation method (Monte Carlo type method) was used. It has been found by the stochastic sensitivity analysis that the cyclic creep deformation variability is most sensitive to the Elastic modulus of concrete, compressive strength, mean stress, cyclic stress amplitude, number of cycle, in that order.},
  subject      = {Angewandte Informatik},
  language  = {en}
}