Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/14261
Title: Predicting the shear resistance of RC beams without shear reinforcement using a Bayesian neural network
Authors: Iruansi, Osimen 
Guadagnini, Maurizio 
Pilakoutas, Kypros 
Neocleous, Kyriacos 
Keywords: Bayesian learning;Neural networks;Reinforced concrete;Shear;Uncertainty modelling
Category: Civil Engineering
Field: Engineering and Technology
Issue Date: 2012
Source: International Journal of Reliability and Safety, Volume 6, Issue 1-3, 2012, Pages 82-109
Journal: International Journal of Reliability and Safety 
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 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. © 2012 Inderscience Enterprises Ltd.
URI: https://ktisis.cut.ac.cy/handle/10488/14261
ISSN: 1479389X
DOI: 10.1504/IJRS.2012.044299
Type: Article
Appears in Collections:Άρθρα/Articles

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