Deformation mode classification in extended endplate connections and implications on hysteretic response
Deformation mode classification in extended endplate connections and implications on hysteretic response
Determining the mode by which steel connections deform under rotational demands is essential for assessing damage, quantifying the associated losses, tuning design, and characterizing the connection’s cyclic behavior. In this paper, a classification model is developed to predict the deformation mode in extended endplate connections (EEPCs) as a function of their layout, material, and geometric properties. The model covers six modes inclusive of those expected to occur in either fully rigid or partial strength EEPCs. Such modes, and particularly interactive ones, can be challenging to predict using traditional mechanical or analytical methods. The classification model utilizes the Random Forest algorithm and is trained using a large dataset of experimental and simulation data to achieve a high accuracy larger than 95 %. Additionally, recommendations are provided for characterizing hysteretic phenomenological models depending on the deformation mode. This includes an empirical formula for defining the cyclic pinching parameters in EEPCs undergoing endplate bending. This aims to support system-level seismic simulations employing the lumped plasticity approach.
Extended endplate connection, Machine learning models, Damage mode classification, Finite element simulation, Hysteretic response
Ding, Zizhou
d2f57f07-1ba2-4fce-8eca-f3cfae32dd6a
Elkady, Ahmed
8e55de89-dff4-4f84-90ed-6af476e328a8
1 January 2026
Ding, Zizhou
d2f57f07-1ba2-4fce-8eca-f3cfae32dd6a
Elkady, Ahmed
8e55de89-dff4-4f84-90ed-6af476e328a8
Ding, Zizhou and Elkady, Ahmed
(2026)
Deformation mode classification in extended endplate connections and implications on hysteretic response.
Engineering Structures, 346 (Pt. B), [121709].
(doi:10.1016/j.engstruct.2025.121709).
Abstract
Determining the mode by which steel connections deform under rotational demands is essential for assessing damage, quantifying the associated losses, tuning design, and characterizing the connection’s cyclic behavior. In this paper, a classification model is developed to predict the deformation mode in extended endplate connections (EEPCs) as a function of their layout, material, and geometric properties. The model covers six modes inclusive of those expected to occur in either fully rigid or partial strength EEPCs. Such modes, and particularly interactive ones, can be challenging to predict using traditional mechanical or analytical methods. The classification model utilizes the Random Forest algorithm and is trained using a large dataset of experimental and simulation data to achieve a high accuracy larger than 95 %. Additionally, recommendations are provided for characterizing hysteretic phenomenological models depending on the deformation mode. This includes an empirical formula for defining the cyclic pinching parameters in EEPCs undergoing endplate bending. This aims to support system-level seismic simulations employing the lumped plasticity approach.
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Accepted/In Press date: 31 October 2025
e-pub ahead of print date: 3 November 2025
Published date: 1 January 2026
Keywords:
Extended endplate connection, Machine learning models, Damage mode classification, Finite element simulation, Hysteretic response
Identifiers
Local EPrints ID: 507297
URI: http://eprints.soton.ac.uk/id/eprint/507297
ISSN: 0141-0296
PURE UUID: acb01c62-0496-41ae-a079-70408a9197d1
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Date deposited: 03 Dec 2025 17:36
Last modified: 08 Jan 2026 03:04
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Author:
Zizhou Ding
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