Thursday, August 13, 2009

A Neural Network Model for Shear of RC Beams

Experiments have shown that as the depth of the beam increases, the intensity of shear stress decreases especially in lightly reinforced beams. This phenomenon is referred to as “size effect”. Shear strength is not constant as given by some design codes like the ACI. To understand size effect, an artificial neural network (ANN) model was developed for RC beams without stirrups which fail under diagonal tension.

The ANN model consists of five input nodes representing (1) the compressive strength of concrete, f’c, (2) beam width, b, (3) effective beam depth, d, (4) shear span to depth ratio, a/d, and (5) longitudinal steel ratio. The output is the shear stress, Vu/bd. The graphical user interface of the Visual Basic program of the ANN model is shown.
The figure shows the simulation where the depth (d) was varied from 20 cm to 100 cm for two values of f’c and a/d and constant values for b at 15 cm and r at 2.75%. The size effect is obvious where the shear stress decreases with increasing depth. The experimental results by Kani shows a similar trend as the model. The shear stress also depends on the shear span to depth ratio – a shorter beam (a/d = 2.5) has a larger shear strength than a longer beam (a/d = 5.0).

How safe our our large RC beams with respect to shear failure? Structural engineers must take note of the decrease in shear strength of concrete for large beams so that they can provide adequate shear reinforcements or stirrups.

Reference: Oreta, A.W.C. (2004). "Simulating size effect on shear strength of RC beams without stirrups using neural networks." Eng'g Structures 26(2004) 681-691, Elsevier.

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