Please use this identifier to cite or link to this item:
|Title:||Predicting the shear resistance of RC beams without shear reinforcement using a Bayesian neural network||Authors:||Iruansi, Osimen
|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|
Show full item record
checked on Nov 15, 2019
checked on Nov 16, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.