Neural network-based formula for shear capacity prediction of one-way slabs under concentrated loads
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to the current codes and guidelines, shear assessment of existing reinforced
concrete slab bridges sometimes leads to the conclusion that the bridge under
consideration has insufficient shear capacity. The calculated shear capacity,
however, does not consider the transverse redistribution capacity of slabs, thus
leading to overconservative values. This paper proposes an artificial neural
network (ANN)-based formula to come up with estimates of the shear capacity of
one-way reinforced concrete slabs under a concentrated load, based on 287 test
results gathered from the literature. The proposed model yields maximum and
mean relative errors of 0.0% for the 287 data points. Moreover, it was
illustrated to clearly outperform (mean Vtest
/ VANN =1.00) the Eurocode 2 provisions (mean VE,EC / VR,c =1.59)
for that dataset. A step-by-step assessment scheme for reinforced concrete slab
bridges by means of the ANN-based model is also proposed, which results in an
improvement of the current assessment procedures.