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http://hdl.handle.net/123456789/5604
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Title: | Artificial Neural Network Model for Low Strength RC Beam Shear Capacity |
Authors: | Owusu Afrifa, R. Adom-Asamoah, M. Owusu-Ansah, E. |
Keywords: | Shear strength reinforced concrete Artificial Neural Network design equations |
Issue Date: | Aug-2012 |
Citation: | Journal of Science and Technology, Vol. 32, No. 2, 2012, pp 119-132 |
Abstract: | This research was to investigate how the shear strength prediction of low strength reinforced
concrete beams will improve under an ANN model. An existing database of 310 reinforced concrete
beams without web reinforcement was divided into three sets of training, validation and
testing. A total of 224 different architectural networks were tried, considering networks with one
hidden layer as well as two hidden layers. Error measures of strength ratios were used to select
the best ANN model which was then compared with 3 conventional design code equations in
predicting the shear strength of 26 low strength RC beams. Even though the ANN was the most
accurate, it was less conservative compared with the design code equations. A model reduction
factor based on the characteristic strength concept is derived in this research and used to modify
the ANN output. The modified ANN model is conservative in terms of safety and economy but
not overly conservative as the conventional design equations. The procedure has been automated
such that when new experimental sets are added to the database, the model can be updated and a
new model could be developed. |
Description: | Article published in the Journal of Science and Technology, Vol. 32, No. 2, 2012, pp 119-132 |
URI: | http://hdl.handle.net/123456789/5604 |
Appears in Collections: | Journal of Science and Technology 2000-
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