Open AccessNeural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web
Author(s) -
Jun He,
Miguel Abambres
Publication year - 2019
Publication title -
avances en ciencias e ingenierías/avances en ciencias e ingenierías (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2528-7788
pISSN - 1390-5384
DOI - 10.18272/aci.v11i3.1388
Subject(s) - girder , structural engineering , artificial neural network , shear (geology) , engineering , shear strength (soil) , residual , beam (structure) , materials science , mathematics , computer science , composite material , geology , algorithm , machine learning , soil science , soil water
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%.