Neural network-based formula for shear capacity prediction of one-way
slabs under concentrated loads
Abstract
According 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.