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Backbone moment-rotation models for partial-strength steel endplate connections

Backbone moment-rotation models for partial-strength steel endplate connections
Backbone moment-rotation models for partial-strength steel endplate connections
Phenomenological rotational spring models are critical components of system-level numerical simulations that employ the concentrated plasticity approach. These models must accurately capture the nonlinear behavior and degradation mechanisms in structural members and connections. Robust models for partial strength/semi-rigid steel flush and extended endplate connections are currently missing. Using the large amount of experimental data in the literature for endplate connections, complemented with parametric continuum finite element simulation, robust nonlinear models are developed. The models characterize the nonlinear monotonic backbone moment-rotation response up to failure as well as the post-failure response. The models achieve high accuracy metrics with 75 % of the data being predicted with an error of less than ±10 %. Statistical metrics are also provided to quantify the model uncertainty. The regression-based models are consistent, in definition and format, with those found in existing modelling guidelines/standards such as ASCE 41. The proposed models support robust nonlinear analysis procedures, particularly as part of the performance-based evaluation, retrofit, and design framework.
Backbone curve, Finite element analysis, Modeling uncertainty, Moment-rotation response, Nonlinear modeling, Partial-strength endplate connections, Steel joints
0141-0296
Ding, Zizhou
d2f57f07-1ba2-4fce-8eca-f3cfae32dd6a
Elkady, Ahmed
8e55de89-dff4-4f84-90ed-6af476e328a8
Ding, Zizhou
d2f57f07-1ba2-4fce-8eca-f3cfae32dd6a
Elkady, Ahmed
8e55de89-dff4-4f84-90ed-6af476e328a8

Ding, Zizhou and Elkady, Ahmed (2025) Backbone moment-rotation models for partial-strength steel endplate connections. Engineering Structures, 345, [121465]. (doi:10.1016/j.engstruct.2025.121465).

Record type: Article

Abstract

Phenomenological rotational spring models are critical components of system-level numerical simulations that employ the concentrated plasticity approach. These models must accurately capture the nonlinear behavior and degradation mechanisms in structural members and connections. Robust models for partial strength/semi-rigid steel flush and extended endplate connections are currently missing. Using the large amount of experimental data in the literature for endplate connections, complemented with parametric continuum finite element simulation, robust nonlinear models are developed. The models characterize the nonlinear monotonic backbone moment-rotation response up to failure as well as the post-failure response. The models achieve high accuracy metrics with 75 % of the data being predicted with an error of less than ±10 %. Statistical metrics are also provided to quantify the model uncertainty. The regression-based models are consistent, in definition and format, with those found in existing modelling guidelines/standards such as ASCE 41. The proposed models support robust nonlinear analysis procedures, particularly as part of the performance-based evaluation, retrofit, and design framework.

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Accepted/In Press date: 25 September 2025
e-pub ahead of print date: 30 September 2025
Published date: 15 December 2025
Additional Information: Publisher Copyright: © 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
Keywords: Backbone curve, Finite element analysis, Modeling uncertainty, Moment-rotation response, Nonlinear modeling, Partial-strength endplate connections, Steel joints

Identifiers

Local EPrints ID: 505949
URI: http://eprints.soton.ac.uk/id/eprint/505949
ISSN: 0141-0296
PURE UUID: e88f8f26-8d05-4270-bf90-fa6229c840a4
ORCID for Ahmed Elkady: ORCID iD orcid.org/0000-0002-1214-6379

Catalogue record

Date deposited: 23 Oct 2025 17:07
Last modified: 24 Oct 2025 01:58

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Contributors

Author: Zizhou Ding
Author: Ahmed Elkady ORCID iD

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