Development of neural network based hysteretic models for steel beam-column connections through self-learning simulation

G. J. Yun, J. Ghaboussi, Amr S. Elnashai

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Beam-column connections are zones of highly complex actions and deformations interaction that often lead to failure under the effect of earthquake ground motion. Modeling of the beam-column connections is important both in understanding the behavior and in design. In this article, a framework for developing a neural network (NN) based steel beam-column connection model through structural testing is proposed. Neural network based inelastic hysteretic model for beam-column connections is combined with a new component based model under self-learning simulation framework. Self-learning simulation has the unique advantage in that it can use structural response to extract material models. Self-learning simulation is based on auto-progressive algorithm that employs the principles of equilibrium and compatibility, and the self-organizing nature of artificial neural network material models. The component based model is an assemblage of rigid body elements and spring elements which represent smeared constitutive behaviors of components; either nonlinear elastic or nonlinear inelastic behavior of components. The component based model is verified by a 3-D finite element analysis. The proposed methodology is illustrated through a self-learning simulation for a welded steel beam-column connection. In addition to presenting the first application of self-learning simulation to steel beam-column connections, a framework is outlined for applying the proposed methodology to other types of connections.

Original languageEnglish (US)
Pages (from-to)453-467
Number of pages15
JournalJournal of Earthquake Engineering
Volume11
Issue number3
DOIs
StatePublished - May 1 2007

Fingerprint

learning
steel
Neural networks
Steel
simulation
structural response
methodology
artificial neural network
ground motion
Earthquakes
Finite element method
earthquake
Testing
modeling
material

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Geotechnical Engineering and Engineering Geology

Cite this

@article{09735572cbac4e58a3d77b3dfccb8bde,
title = "Development of neural network based hysteretic models for steel beam-column connections through self-learning simulation",
abstract = "Beam-column connections are zones of highly complex actions and deformations interaction that often lead to failure under the effect of earthquake ground motion. Modeling of the beam-column connections is important both in understanding the behavior and in design. In this article, a framework for developing a neural network (NN) based steel beam-column connection model through structural testing is proposed. Neural network based inelastic hysteretic model for beam-column connections is combined with a new component based model under self-learning simulation framework. Self-learning simulation has the unique advantage in that it can use structural response to extract material models. Self-learning simulation is based on auto-progressive algorithm that employs the principles of equilibrium and compatibility, and the self-organizing nature of artificial neural network material models. The component based model is an assemblage of rigid body elements and spring elements which represent smeared constitutive behaviors of components; either nonlinear elastic or nonlinear inelastic behavior of components. The component based model is verified by a 3-D finite element analysis. The proposed methodology is illustrated through a self-learning simulation for a welded steel beam-column connection. In addition to presenting the first application of self-learning simulation to steel beam-column connections, a framework is outlined for applying the proposed methodology to other types of connections.",
author = "Yun, {G. J.} and J. Ghaboussi and Elnashai, {Amr S.}",
year = "2007",
month = "5",
day = "1",
doi = "10.1080/13632460601123180",
language = "English (US)",
volume = "11",
pages = "453--467",
journal = "Journal of Earthquake Engineering",
issn = "1363-2469",
publisher = "Taylor and Francis Ltd.",
number = "3",

}

Development of neural network based hysteretic models for steel beam-column connections through self-learning simulation. / Yun, G. J.; Ghaboussi, J.; Elnashai, Amr S.

In: Journal of Earthquake Engineering, Vol. 11, No. 3, 01.05.2007, p. 453-467.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Development of neural network based hysteretic models for steel beam-column connections through self-learning simulation

AU - Yun, G. J.

AU - Ghaboussi, J.

AU - Elnashai, Amr S.

PY - 2007/5/1

Y1 - 2007/5/1

N2 - Beam-column connections are zones of highly complex actions and deformations interaction that often lead to failure under the effect of earthquake ground motion. Modeling of the beam-column connections is important both in understanding the behavior and in design. In this article, a framework for developing a neural network (NN) based steel beam-column connection model through structural testing is proposed. Neural network based inelastic hysteretic model for beam-column connections is combined with a new component based model under self-learning simulation framework. Self-learning simulation has the unique advantage in that it can use structural response to extract material models. Self-learning simulation is based on auto-progressive algorithm that employs the principles of equilibrium and compatibility, and the self-organizing nature of artificial neural network material models. The component based model is an assemblage of rigid body elements and spring elements which represent smeared constitutive behaviors of components; either nonlinear elastic or nonlinear inelastic behavior of components. The component based model is verified by a 3-D finite element analysis. The proposed methodology is illustrated through a self-learning simulation for a welded steel beam-column connection. In addition to presenting the first application of self-learning simulation to steel beam-column connections, a framework is outlined for applying the proposed methodology to other types of connections.

AB - Beam-column connections are zones of highly complex actions and deformations interaction that often lead to failure under the effect of earthquake ground motion. Modeling of the beam-column connections is important both in understanding the behavior and in design. In this article, a framework for developing a neural network (NN) based steel beam-column connection model through structural testing is proposed. Neural network based inelastic hysteretic model for beam-column connections is combined with a new component based model under self-learning simulation framework. Self-learning simulation has the unique advantage in that it can use structural response to extract material models. Self-learning simulation is based on auto-progressive algorithm that employs the principles of equilibrium and compatibility, and the self-organizing nature of artificial neural network material models. The component based model is an assemblage of rigid body elements and spring elements which represent smeared constitutive behaviors of components; either nonlinear elastic or nonlinear inelastic behavior of components. The component based model is verified by a 3-D finite element analysis. The proposed methodology is illustrated through a self-learning simulation for a welded steel beam-column connection. In addition to presenting the first application of self-learning simulation to steel beam-column connections, a framework is outlined for applying the proposed methodology to other types of connections.

UR - http://www.scopus.com/inward/record.url?scp=34249656796&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34249656796&partnerID=8YFLogxK

U2 - 10.1080/13632460601123180

DO - 10.1080/13632460601123180

M3 - Article

VL - 11

SP - 453

EP - 467

JO - Journal of Earthquake Engineering

JF - Journal of Earthquake Engineering

SN - 1363-2469

IS - 3

ER -