Predicting flush end-plate connections response using artificial neural networks

Abstract

Predicting the moment-rotation response parameters of semi-rigid steel connections can be challenging given the multitude of components that contribute to the connection's elastic and plastic deformations. This applies to the popular bolted flush end-plate beam-to-column connections (FEPCs). The literature has highlighted the limitations of current analytical, mechanical, and empirical models in providing accurate predictions. Considering these limitations, the application of machine-learning methods in structural engineering, such as artificial neural networks (ANN), have gained wide attention recently in addressing problems associated with complex structural deformation and damage phenomena. To that end, the superior nonlinearity of ANNs is employed herein in to predict the response characteristics of FEPCs. A dataset of more than 200 specimens, collected from past experimental programs, is utilized to train the ANN for predicting the elastic stiffness, plastic strength, and posy-yield stiffness. The paper describes the deduction of response parameters from test data using data fitting, the determination of significant geometric and material features, the ANN architecture and algorithms, and the accuracy metrics of the new model.

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Identifiers

ORCID for Ahmed Elkady: orcid.org/0000-0002-1214-6379

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