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.
Highlights
Plain concrete, being a brittle material, cracks when exposed to low tensile stresses caused by shear stresses at inclined portions of a beam [1]
The datasets used were the results of experimental works of forty-two steel fibre -reinforced concrete (SFRC) beams that were compiled from nine different independent authors
There is a great variety of possible ways of data transformation, from adding constant to multiplying, squaring, or converting to logarithmic scales etc. to transform the values of the shear resistance data, YA, to a symmetric distribution
Summary
Plain concrete, being a brittle material, cracks when exposed to low tensile stresses caused by shear stresses at inclined portions of a beam [1]. Several model expressions are available in the literature [3,4,5,6] and existing codes [7] by expensive laboratory testing of full-scale beams have been developed, and these expressions are often semi-empirical, which has certain limitations in its accuracies Due to this issue, there is a need to establish a computational solution that can be used to model shear resistance in SFRC due to a lack of simple equations in that describe shear resistance for fibre reinforced concrete (SFRC) beams. The ANN model development, and the prediction of SRFC shear resistance will be the conducted based on 1 hidden layer only to minimize the computational time usage. To demonstrate the methodology and its results, a case study on a small dataset of 42 shear resistance datasets is introduced
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