Neural network-based analytical model to predict the shear strength of
steel girders with a trapezoidal corrugated web
Abstract
Corrugated webs are used to increase the shear stability of steel webs
of beam-like members and to eliminate the need of transverse stiffeners.
Previously developed formulas for predicting the shear strength of
trapezoidal corrugated steel webs, along with the corresponding theory,
are summarized. An artificial neural network (ANN)-based model is
proposed to estimate the shear strength of steel girders with a
trapezoidal corrugated web, and under a concentrated load. 210 test
results from previous published research were collected into a database
according to relevant test specimen parameters in order to feed the
simulated ANNs. Seven (geometrical and material) parameters were
identified as input variables and the ultimate shear stress at failure
was considered the output variable. The proposed ANN-based analytical
model yielded maximum and mean relative errors of 0.0% for the 210
points from the database. Moreover, still based on those points, it was
illustrated that the ANN-based model clearly outperforms the other
existing analytical models, which yield mean errors larger than 13%.