This study explores the use of artificial neural networks in predicting the ultimate shear strength of reinforced concrete deep beams. 111 experimental data collected from the literature covers the simple case of simply supported beam with two point loads acting symmetrically with respect to the centre line of the span. The data is arranged in such a format that 10 input parameters cover the geometrical and material properties of deep beam while the corresponding output value is the ultimate shear strength. Among the available methods in the literature, ACI, Truss and Mau-Hsu methods were selected due to their accuracy and used to calculate the shear strength of each beam in the set. Later, artificial neural network is developed using two different software and the ultimate shear strength of each beam is determined from these networks. It is found that the average ratio of predicted and actual shear strength was 1.01 for the neural network, 0.48 for ACI method, 1.17 for Truss method and 1.19 for Mau-Hsu method. It is apparent that neural networks provide an efficient alternative method in predicting the shear strength capacity of reinforced concrete deep beams where several equations exist, none of them producing an accurate result.

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