Title: Shear Capacity of Reinforced Concrete Using Neural network

Author(s): Keun-Hyeok Yang, Ashraf F. Ashour, and Jin-Kyu Song

Publication: IJCSM

Volume: 1

Issue: 1

Appears on pages(s): 63-73

Keywords: neural network; deep beams; slender beams; shear capacity; code provisions

DOI:

Date: 12/31/2007

Abstract:
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.