New predictive equations for LDB strength assessment of steel–concrete composite beams. (1st May 2022)

• Record Type: Journal Article
• Authors: Hosseinpour, Mahmoud; Rossi, Alexandre; Sander Clemente de Souza, Alex; Sharifi, Yasser
• Is Part Of: Engineering structures. Volume 258(2022)
• Publication Date: 2022-05-01
• Subjects: Lateral distortional buckling -- Finite element modeling -- Steel-concrete composite beam -- Artificial neural network -- Multiple regression; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114121
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: By conducting an extensive parametric study including 475 finite element model (FEM) of steel–concrete composite beams (SCCB), an attempt was made to provide a reliable database. The Lateral distortional buckling (LDB) strength of SCCB as one of the failure modes has been investigated. The MR, and ANN have been used to predict the formulae for better prediction of the LDB of SCCB by practical engineers. The accuracy of the developed formula is verified versus the actual data using several suitable criteria. The existing predictions given by the researchers were compared with the formula obtained from the MR, and ANN. Abstract: Lateral distortional buckling (LDB) mode has been investigated as one of the failure modes in steel–concrete composite beams (SCCB). The LDB mode in such beams is known by lateral displacement accompanied by rotation of the compressed bottom flange that occurs due to the web distortion. However, many studies have been conducted to investigate the behavior of this failure mode, no equation has yet been found that can accurately calculate the ultimate LDB resistance of SCCB. Consequently, in the current paper, by conducting an extensive parametric study including 475 finite element models (FEM), an attempt was made to provide a reliable database. Then, based on the provided database, two methods of artificial neural network (ANN) and multiple regression (MR) were employed, and based on them, high-precision equations were proposed to predict the ultimate LDB strength of SCCB. Finally, the proposed formulas were compared with the existing formulas for estimating the LDB strength of SCCB. The results showed that the proposed formulas not only present a reasonable accuracy compared to the existing formulations but also can be used by engineers as practical equations in the design process of SCCB.