Academic Journal

Structural Performance of Bolted Lateral Connections in Steel Beams under Bending Using the Component-Based Finite Element Method.

Bibliographic Details
Title: Structural Performance of Bolted Lateral Connections in Steel Beams under Bending Using the Component-Based Finite Element Method.
Authors: Morido-García, Guillermo, De Santos-Berbel, César
Source: Applied Sciences (2076-3417); May2024, Vol. 14 Issue 9, p3900, 13p
Subject Terms: Bolted joints, Finite element method, Stress concentration, Steel, Material plasticity
Abstract: Featured Application: The findings of this research can significantly contribute to the design and construction of efficient and safe steel joints for building structures. Structures must provide strength, stability, and stiffness to buildings and at the same time be efficient. This study addressed the effect of design elements and parameters on the strength of bolted lateral connections in steel beams under bending using the component-based finite element method. The variables evaluated were plate thickness, horizontal and vertical spacing between bolts, and geometric arrangement of bolts. Finite element software was used to evaluate the stress state of the junction plate, its plastic deformation, and bolt shear. A sensitivity analysis was performed to determine which bolt arrangements result in safer and more efficient designs using the same components. Stress distribution within the junction plate and plastic deformation values were used to evaluate the structural performance of the joints according to EuroCode 3. The results showed that placing bolts near the edge of a plate affected the bolts' utilization, especially with thinner plates. Additionally, introducing an offset between central and outer bolt rows is not recommended as it worsened the stress distribution and the structural performance. [ABSTRACT FROM AUTHOR]
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
ISSN:20763417
DOI:10.3390/app14093900