Torsional Behavior Evaluation of Reinforced Concrete Beams Using Artificial Neural Network

• Main Authors: Muhammad Haroon, Seungbum Koo, DongIk Shin, Changhyuk Kim
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Applied Sciences
• Subjects: RC beam; torsion; artificial neural network; machine learning; PCA; autoencoder
• Online Access: https://www.mdpi.com/2076-3417/11/10/4465
• ISSN: 2076-3417

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.