Yes / A 9 x 18 x 1 feed-forward neural network (NN) model
trained using a resilient back-propagation algorithm and
early stopping technique is constructed to predict the
shear strength of deep reinforced concrete beams. The
input layer covering geometrical and material properties
of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been
achieved using a comprehensive database compiled from
362 simple and 71 continuous deep beam specimens.
The shear strength predictions of deep beams obtained
from the developed NN are in better agreement with
test results than those determined from strut-and-tie
models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1.028 and 0.154, respectively, for simple deep beams, and 1.0 and 0.122, respectively, for continuous deep beams. In addition, the
trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/865 |
| Date | January 2008 |
| Creators | Yang, Keun-Hyeok, Ashour, Ashraf, Song, J-K., Lee, E-T. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | © 2008 Thomas Telford Ltd. Reproduced in accordance with the publisher's self-archiving policy. |
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