Simulation and behaviour of single-span portal frames, Part II: parametric analysis and practical implications
Author(s)
Zhu, Xiaobo
Ahmed, Aya
Walport, Fiona
Gardner, Leroy
Type
Journal Article
Abstract
The finite element (FE) model developed and validated in the companion paper is employed herein to systematically study the influence of key parameters, including span, height and length, on the structural behaviour of single-span portal frames under serviceability levels of vertical, wind sway, wind uplift and thermal loading. The effects of stressed skin action, as influenced by these parameters, on the overall frame behaviour are also investigated. Additionally, a new parameter, the shear angle, is proposed to facilitate the identification of critical locations and trends for screw forces within the secondary steelwork and cladding system. The main findings are that (1) among the loading cases considered, wind sway loading is the most critical in terms of screw forces, with screw displacements exceeding 20 mm and 7 mm on the gable and roof cladding, respectively, in the largest models, while thermal loading is less detrimental, (2) the gable frame experiences up to four times higher reaction forces than expected, especially under wind sway loading, and this increases with increasing stiffness of the gable frame relative to the internal frames, (3) the side lap stitching screws are the critical (most heavily loaded) connectors and the proposed shear angle is able to effectively identify the critical areas and the trends in screw forces, and (4) introducing bracing reduces the shear forces resisted by the screws and cladding. Furthermore, practical suggestions for mitigating the risk of screw failure are discussed.
Date Issued
2025-11-01
Date Acceptance
2025-08-06
Citation
Engineering structures, 2025, 343 (Part A)
ISSN
0141-0296
Publisher
Elsevier
Journal / Book Title
Engineering structures
Volume
343
Issue
Part A
Copyright Statement
Copyright © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Article Number
121138
Date Publish Online
2025-09-02