Abstract
Artificial neural networks (ANNs) are an emerging field of research and have proven to have significant potential for use in structural engineering. In previous literature, many studies successfully utilized ANNs to analyze the structures under different loading conditions and verified the accuracy of the approach. Several studies investigated the use of ANNs to analyze the shear behavior of reinforced concrete (RC) members. However, few studies have focused on the potential use of an ANN for analysis of the torsional behavior of an RC member. Torsion is a complex problem and modeling the torsional fracture mechanism using the traditional analytical approach is problematic. Recent studies show that the nonlinear behavior of RC members under torsion can be modeled using ANNs. This paper presents a comprehensive analytical and parametric study of the torsional response of RC beams using ANNs. The ANN model was trained and validated against an experimental database of 159 RC beams reported in the literature. The results were compared with the predictions of design codes. The results show that ANNs can effectively model the torsional behavior of RC beams. The parametric study presented in this paper provides greater insight into the torsional resistance mechanism of RC beams and its characteristic parameters.
Highlights
Most of the current design code [1,2,3,4] formulations for predicting the torsional response of reinforced concrete (RC) members are based on the space-truss model [5] and thin-walled tube theory [6]
A total number of 52 and 24 hidden nodes were selected for the Artificial neural networks (ANNs) models with two preprocessing algorithms: principal component analysis (PCA) and autoencoder
The maximum torsional strength of 151 specimens of the training dataset was predicted using the ANN models developed in this study and compared with the predictions of the most widely used design codes
Summary
Most of the current design code [1,2,3,4] formulations for predicting the torsional response of reinforced concrete (RC) members are based on the space-truss model [5] and thin-walled tube theory [6]. Previous research studies [23,24,25,26,27,28,29,30,31,32] investigated the potential use of ANNs to analyze the complex shear behavior of RC members and verified the existing design code formulation. These studies evaluated a variety of design parameters affecting the shear behavior of RC members with and without transverse reinforcement. The results of the ANN model were compared with the predictions of ACI 318-19, EC2-04, CSA-14, and JSCE-07
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