Article ID: | iaor201113570 |
Volume: | 6 |
Issue: | 123 |
Start Page Number: | 82 |
End Page Number: | 109 |
Publication Date: | Dec 2012 |
Journal: | International Journal of Reliability and Safety |
Authors: | Iruansi Osimen, Guadagnini Maurizio, Pilakoutas Kypros, Neocleous Kyriacos |
Keywords: | Bayesian modelling |
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 multilayer 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.