No / Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and
early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer
neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear
capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%,
respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from
the developed neural network models are in much better agreement with test results than those determined from shear provisions of
different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the
neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17,
respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams
predicted by the developed neural network shows consistent agreement with those experimentally observed.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/959 |
| Date | January 2007 |
| Creators | Yang, Keun-Hyeok, Ashour, Ashraf, Song, J-K. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article |
| Relation | http://www.ceric.net/wonmun2/kci/KCI_3_2007_1_1_63(C).pdf |
Page generated in 0.0017 seconds