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Predicting the Shear Resistance of RC Beams without Shear Reinforcement Using a Bayesian Neural Network (bibtex)
by Osimen Iruansi, Maurizio Guadagnini, Kypros Pilakoutas, Kyriacos Neocleous
Abstract:
Advances in neural computing have shown that a neural learning approach that uses Bayesian inference can essentially eliminate the problem of over fitting, which is common with conventional back propagation Neural Networks. In addition, Bayesian Neural Network can provide the confidence (error) associated with its prediction. This paper presents the application of Bayesian learning to train a multi layer perceptron network to predict the shear resistance of reinforced concrete beams without shear reinforcement. The Automatic Relevance Determination technique was employed to assess the relative importance of the different input variables considered in this study on the shear resistance of reinforced concrete beams. The performance of the Bayesian Neural Network is examined and discussed along with that of current shear design provisions.
Reference:
Predicting the Shear Resistance of RC Beams without Shear Reinforcement Using a Bayesian Neural Network (Osimen Iruansi, Maurizio Guadagnini, Kypros Pilakoutas, Kyriacos Neocleous), In International Journal of Reliability and Safety (IJRS), volume 6, 2012. (Special Issue on: "Robust Design Coping with Hazards Risk and Uncertainty")
Bibtex Entry:
@ARTICLE{Iruansi-etal:2012,
  author = {Osimen Iruansi and Maurizio Guadagnini and Kypros Pilakoutas and
	Kyriacos Neocleous},
  title = {Predicting the Shear Resistance of RC Beams without Shear Reinforcement
	Using a Bayesian Neural Network},
  journal = {International Journal of Reliability and Safety (IJRS)},
  year = {2012},
  volume = {6},
  pages = {82-109},
  number = {1/2/3},
  note = {Special Issue on: "Robust Design  Coping with Hazards Risk and Uncertainty"},
  abstract = {Advances in neural computing have shown that a neural learning approach
	that uses Bayesian inference can essentially eliminate the problem
	of over fitting, which is common with conventional back propagation
	Neural Networks. In addition, Bayesian Neural Network can provide
	the confidence (error) associated with its prediction. This paper
	presents the application of Bayesian learning to train a multi layer
	perceptron network to predict the shear resistance of reinforced
	concrete beams without shear reinforcement. The Automatic Relevance
	Determination technique was employed to assess the relative importance
	of the different input variables considered in this study on the
	shear resistance of reinforced concrete beams. The performance of
	the Bayesian Neural Network is examined and discussed along with
	that of current shear design provisions.},
  doi = {10.1504/IJRS.2012.044299},
  issn = {1479-3903},
  keywords = {Bayesian learning, Neural Networks, Reinforced Concrete, Shear, Uncertainty
	modelling},
  owner = {Maurizio},
  timestamp = {2011.06.22},
  url = {http://www.inderscience.com/search/index.php?action=record&rec_id=44299&prevQuery=&ps=10&m=or}
}
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