Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

Contenido principal del artículo

Jun He
Miguel Abambres

Resumen

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%.

Detalles del artículo

Cómo citar
He, J., & Abambres, M. (2020). Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web. ACI Avances En Ciencias E Ingenierías, 11(3). https://doi.org/10.18272/aci.v11i3.1388
Sección
SECCIÓN C: INGENIERÍAS

Citas

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