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Predicting the Shear Strength of RC Beams Without Stirrups Using 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 on experimental test on Reinforced Concrete (RC) beams without stirrups failing in shear. The trained network was found to provide good estimate of shear strength when the input variables (i.e. shear parameters) are within the range in the experimental database used for training. Within the Bayesian framework, a process known as the Automatic Relevance Determination is employed to assess the relative importance of different input variables on the output (i.e. shear strength). Finally the network is utilised to simulate typical RC beams failing in shear.
Reference:
Predicting the Shear Strength of RC Beams Without Stirrups Using Bayesian Neural Network (Osimen Iruansi, Maurizio Guadagnini, Kypros Pilakoutas, Kyriacos Neocleous), In Proceedings of the 4th International Workshop on Reliable Engineering Computing, Robust Design - Coping with Hazards, Risk and Uncertainty (R.L. Muhana M. Beer, R. L. Mullen, eds.), 2010. (Research Scientific)
Bibtex Entry:
@INPROCEEDINGS{Iruansi-etal:2010,
  author = {Osimen Iruansi and Maurizio Guadagnini and Kypros Pilakoutas and
	Kyriacos Neocleous},
  title = {Predicting the Shear Strength of RC Beams Without Stirrups Using
	Bayesian Neural Network},
  booktitle = {Proceedings of the 4th International Workshop on Reliable Engineering
	Computing, Robust Design - Coping with Hazards, Risk and Uncertainty},
  year = {2010},
  editor = {M. Beer, R.L. Muhana and R. L. Mullen},
  pages = {597--613},
  address = {Singapore},
  month = {March},
  note = {Research Scientific},
  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 on experimental test on Reinforced Concrete (RC)
	beams without stirrups failing in shear. The trained network was
	found to provide good estimate of shear strength when the input variables
	(i.e. shear parameters) are within the range in the experimental
	database used for training. Within the Bayesian framework, a process
	known as the Automatic Relevance Determination is employed to assess
	the relative importance of different input variables on the output
	(i.e. shear strength). Finally the network is utilised to simulate
	typical RC beams failing in shear.},
  doi = {10.3850/978-981-08-5118-7_067},
  isbn = {978-981-08-5118-7},
  keywords = {Bayesian learning, Neural networks, Reinforced concrete, Shear, Uncertainty
	modelling}
}
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