@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}
}
@article{Aguinaga,
  author    = {Aguinaga, Jos{\´e} Guillermo De},
  title     = {Error in prediction due to data type availability in a coupled hydro-mechanical model},
  series = {Electronic Journal of Geotechnical Engineering},
  journal   = {Electronic Journal of Geotechnical Engineering},
  doi       = {10.25643/bauhaus-universitaet.3117},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170413-31170},
  pages     = {2459 -- 2471},
  abstract  = {Different types of data provide different type of information. The present research analyzes the error on prediction obtained under different data type availability for calibration. The contribution of different measurement types to model calibration and prognosis are evaluated. A coupled 2D hydro-mechanical model of a water retaining dam is taken as an example. Here, the mean effective stress in the porous skeleton is reduced due to an increase in pore water pressure under drawdown conditions. Relevant model parameters are identified by scaled sensitivities. Then, Particle Swarm Optimization is applied to determine the optimal parameter values and finally, the error in prognosis is determined. We compare the predictions of the optimized models with results from a forward run of the reference model to obtain the actual prediction errors. The analyses presented here were performed calibrating the hydro-mechanical model to 31 data sets of 100 observations of varying data types. The prognosis results improve when using diversified information for calibration. However, when using several types of information, the number of observations has to be increased to be able to cover a representative part of the model domain. For an analysis with constant number of observations, a compromise between data type availability and domain coverage proves to be the best solution. Which type of calibration information contributes to the best prognoses could not be determined in advance. The error in model prognosis does not depend on the error in calibration, but on the parameter error, which unfortunately cannot be determined in inverse problems since we do not know its real value. The best prognoses were obtained independent of calibration fit. However, excellent calibration fits led to an increase in prognosis error variation. In the case of excellent fits; parameters' values came near the limits of reasonable physical values more often. To improve the prognoses reliability, the expected value of the parameters should be considered as prior information on the optimization algorithm.},
  subject      = {Sensitivit{\"a}tsanalyse},
  language  = {en}
}