@unpublished{SteinerBourinetLahmer,
  author    = {Steiner, Maria and Bourinet, Jean-Marc and Lahmer, Tom},
  title     = {An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression},
  doi       = {10.25643/BAUHAUS-UNIVERSITAET.3832},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181218-38320},
  pages     = {1 -- 33},
  abstract  = {In the field of engineering, surrogate models are commonly used for approximating the behavior of a physical phenomenon in order to reduce the computational costs. Generally, a surrogate model is created based on a set of training data, where a typical method for the statistical design is the Latin hypercube sampling (LHS). Even though a space filling distribution of the training data is reached, the sampling process takes no information on the underlying behavior of the physical phenomenon into account and new data cannot be sampled in the same distribution if the approximation quality is not sufficient. Therefore, in this study we present a novel adaptive sampling method based on a specific surrogate model, the least-squares support vector regresson. The adaptive sampling method generates training data based on the uncertainty in local prognosis capabilities of the surrogate model - areas of higher uncertainty require more sample data. The approach offers a cost efficient calculation due to the properties of the least-squares support vector regression. The opportunities of the adaptive sampling method are proven in comparison with the LHS on different analytical examples. Furthermore, the adaptive sampling method is applied to the calculation of global sensitivity values according to Sobol, where it shows faster convergence than the LHS method. With the applications in this paper it is shown that the presented adaptive sampling method improves the estimation of global sensitivity values, hence reducing the overall computational costs visibly.},
  subject      = {Approximation},
  language  = {en}
}
@article{AchenbachLahmerMorgenthal,
  author    = {Achenbach, Marcus and Lahmer, Tom and Morgenthal, Guido},
  title     = {Identification of the thermal properties of concrete for the temperature calculation of concrete slabs and columns subjected to a standard fire—Methodology and proposal for simplified formulations},
  series = {Fire Safety Journal 87},
  journal   = {Fire Safety Journal 87},
  doi       = {10.1016/j.firesaf.2016.12.003},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170331-30929},
  pages     = {80 -- 86},
  abstract  = {The fire resistance of concrete members is controlled by the temperature distribution of the considered cross section. The thermal analysis can be performed with the advanced temperature dependent physical properties provided by 5EN6 1992-1-2. But the recalculation of laboratory tests on columns from 5TU6 Braunschweig shows, that there are deviations between the calculated and measured temperatures. Therefore it can be assumed, that the mathematical formulation of these thermal properties could be improved. A sensitivity analysis is performed to identify the governing parameters of the temperature calculation and a nonlinear optimization method is used to enhance the formulation of the thermal properties. The proposed simplified properties are partly validated by the recalculation of measured temperatures of concrete columns. These first results show, that the scatter of the differences from the calculated to the measured temperatures can be reduced by the proposed simple model for the thermal analysis of concrete.},
  subject      = {Sensitivit{\"a}tsanalyse},
  language  = {en}
}