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ANN-based model for predicting the nonlinear response of flush endplate 1 connections

ANN-based model for predicting the nonlinear response of flush endplate 1 connections
ANN-based model for predicting the nonlinear response of flush endplate 1 connections
Predicting the moment-rotation response parameters of semirigid steel connections can be challenging given the many components contributing to the connection’s elastic and plastic deformations. This is the case for the popular flush endplate beam-to-column connections (FEPCs). The literature has highlighted the limitations of current analytical, mechanical, and traditional empirical models in providing accurate predictions of the FEPCs’ moment-rotation response. Considering this limitation, machine-learning methods have gained wide attention recently in structural engineering applications to address problems associated with complex structural deformation and damage phenomena. To that end, the superior nonlinearity of artificial neural networks (ANN) is employed herein to predict the response characteristics of FEPCs. A large data set of about 200 specimens, collected from past experimental programs, is utilized to train the ANN for predicting the bilinear response of FEPCs including strain hardening. The paper describes the deduction of the response parameters from test data using data fitting, the determination of significant geometric, material, and layout features, the ANN architecture and algorithms, and the accuracy metrics of the new model. The Shapley algorithm is used to explain the inner workings of the model. A computer tool as well as a descriptive guide to the mathematical construction of the ANN are provided to aid with model implementation in practice.
Steel joints, artificial neural networks, endplate connections, machine learning, moment-rotation 25 response, Endplate connections, Machine learning, Artificial neural networks (ANNs), Moment-rotation response
0733-9445
Georgiou, Gregory
63b2be77-384d-41a2-a9f3-a5fec4634438
Elkady, Ahmed
8e55de89-dff4-4f84-90ed-6af476e328a8
Georgiou, Gregory
63b2be77-384d-41a2-a9f3-a5fec4634438
Elkady, Ahmed
8e55de89-dff4-4f84-90ed-6af476e328a8

Georgiou, Gregory and Elkady, Ahmed (2024) ANN-based model for predicting the nonlinear response of flush endplate 1 connections. Journal of Structural Engineering, 150 (5), [04024034]. (doi:10.1061/JSENDH.STENG-13119).

Record type: Article

Abstract

Predicting the moment-rotation response parameters of semirigid steel connections can be challenging given the many components contributing to the connection’s elastic and plastic deformations. This is the case for the popular flush endplate beam-to-column connections (FEPCs). The literature has highlighted the limitations of current analytical, mechanical, and traditional empirical models in providing accurate predictions of the FEPCs’ moment-rotation response. Considering this limitation, machine-learning methods have gained wide attention recently in structural engineering applications to address problems associated with complex structural deformation and damage phenomena. To that end, the superior nonlinearity of artificial neural networks (ANN) is employed herein to predict the response characteristics of FEPCs. A large data set of about 200 specimens, collected from past experimental programs, is utilized to train the ANN for predicting the bilinear response of FEPCs including strain hardening. The paper describes the deduction of the response parameters from test data using data fitting, the determination of significant geometric, material, and layout features, the ANN architecture and algorithms, and the accuracy metrics of the new model. The Shapley algorithm is used to explain the inner workings of the model. A computer tool as well as a descriptive guide to the mathematical construction of the ANN are provided to aid with model implementation in practice.

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Mak&Elkady_Paper_Accepted Manuscript - Accepted Manuscript
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Accepted/In Press date: 1 December 2023
e-pub ahead of print date: 24 February 2024
Published date: 1 May 2024
Additional Information: Publisher Copyright: © 2024 American Society of Civil Engineers.
Keywords: Steel joints, artificial neural networks, endplate connections, machine learning, moment-rotation 25 response, Endplate connections, Machine learning, Artificial neural networks (ANNs), Moment-rotation response

Identifiers

Local EPrints ID: 489242
URI: http://eprints.soton.ac.uk/id/eprint/489242
ISSN: 0733-9445
PURE UUID: d50d8c94-0f41-4ade-bcb6-c6620837dbf4
ORCID for Ahmed Elkady: ORCID iD orcid.org/0000-0002-1214-6379

Catalogue record

Date deposited: 18 Apr 2024 16:41
Last modified: 06 Jun 2024 02:06

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Contributors

Author: Gregory Georgiou
Author: Ahmed Elkady ORCID iD

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