@inproceedings{ChudobaScholzenHegger,
  author    = {Chudoba, Rostislav and Scholzen, A. and Hegger, Josef},
  title     = {MICROPLANE MODEL WITH INITIAL AND DAMAGE-INDUCED ANISOTROPY APPLIED TO TEXTILE-REINFORCED CONCRETE},
  editor    = {G{\"u}rlebeck, Klaus and K{\"o}nke, Carsten},
  organization    = {Bauhaus-Universit{\"a}t Weimar},
  issn      = {1611-4086},
  doi       = {10.25643/bauhaus-universitaet.2836},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20170314-28367},
  pages     = {8},
  abstract  = {The presented material model reproduces the anisotropic characteristics of textile reinforced concrete in a smeared manner. This includes both the initial anisotropy introduced by the textile reinforcement, as well as the anisotropic damage evolution reflecting fine patterns of crack bridges. The model is based on the microplane approach. The direction-dependent representation of the material structure into oriented microplanes provides a flexible way to introduce the initial anisotropy. The microplanes oriented in a yarn direction are associated with modified damage laws that reflect the tension-stiffening effect due to the multiple cracking of the matrix along the yarn.},
  subject      = {Angewandte Informatik},
  language  = {en}
}
@inproceedings{TahaSherifHegger2004,
  author    = {Taha, M. M. Reda and Sherif, Alaa and Hegger, Josef},
  title     = {A nouvelle approach for predicting the shear cracking angle in RC and PC beams using artificial neural networks},
  doi       = {10.25643/bauhaus-universitaet.107},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-1071},
  year      = {2004},
  abstract  = {The truss model for predicting shear resistance of reinforced concrete beams has usually been criticized because of its underestimation of the concrete shear strength especially for beams with low shear reinforcement. Two challengers are commonly encountered in any truss model and are responsible for its inaccurate shear strength prediction. First: the cracking angle is usually assumed empirically and second the shear contribution of the arching action is usually neglected. This research introduces a nouvelle approach, by using Artificial Neural Network (ANN) for accurately evaluating the shear cracking angle of reinforced and prestressed concrete beams. The model inputs include the beam geometry, concrete strength, the shear reinforcement ratio and the prestressing stress if any. ...},
  subject      = {Neuronales Netz},
  language  = {en}
}