The Optimal Artificial Neural Network for Small Datasets on Shear Resistance Prediction of Steel Fibre Reinforced Concrete Beam

Authors:
  • SSS. F. Senin
    S. F. Senin
  • RRR. Rohim
    R. Rohim
  • AYA. Yusuff
    A. Yusuff
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Abstract
This study presents an approach for the prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams using the Artificial Neural Network (ANN) developed based on existing experimental shear resistance results from various researchers. The experimental results database containing 42 sample numbers of SFRC beams (with shear span-to-depth ratio exceeding 2.5) without stirrups, with compressive strength of concrete varying from 24.9 to 68.6 MPa and steel fibers of hooked end type are used to develop an ANN model. The developed ANN model is trained by using 70% and 90% of the data and another 30 to 10% served as the validation data purpose. The shear strengths prediction based on ANN model was found to be in perfect agreement with the experimental values when the optimal neuron number is 2 and by fixing the training set size as 90%. Results showed that this ANN model has strong potential as a feasible design tool for predicting the shear strength of SFRC beams without transverse reinforcement or stirrups within the range of input parameters considered in this study.