Optimizing ANN models with PSO for predicting short building seismic response

Nguyen, H; Moayedi, H; Foong, LK; Al Najjar, HAH; Jusoh, WAW; Rashid, ASA; Jamali, J

Optimizing ANN models with PSO for predicting short building seismic response

Nguyen, H Moayedi, H Foong, LK Al Najjar, HAH Jusoh, WAW Rashid, ASA Jamali, J

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Publisher:: Springer Verlag
Publication Type:: Journal Article
Citation:: Engineering with Computers: an international journal for simulation-based engineering, 2020, 36, (3), pp. 823-837
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Nguyen, H
dc.contributor.author	Moayedi, H
dc.contributor.author	Foong, LK
dc.contributor.author	Al Najjar, HAH
dc.contributor.author	Jusoh, WAW
dc.contributor.author	Rashid, ASA
dc.contributor.author	Jamali, J
dc.date.accessioned	2021-03-23T22:39:08Z
dc.date.available	2021-03-23T22:39:08Z
dc.date.issued	2020-07-01
dc.identifier.citation	Engineering with Computers: an international journal for simulation-based engineering, 2020, 36, (3), pp. 823-837
dc.identifier.issn	0177-0667
dc.identifier.issn	1435-5663
dc.identifier.uri	http://hdl.handle.net/10453/147496
dc.description.abstract	© 2019, Springer-Verlag London Ltd., part of Springer Nature. The present study aimed to optimize the artificial neural network (ANN) with one of the well-established optimization algorithms called particle swarm optimization (PSO) for the problem of ground response approximation in short structures. Various studies showed that ANN-based solutions are a reliable method for complex engineering problems. Predicting the ground surface respond to seismic loading is one of the engineering problems that still has not received any ANN solution. Therefore, this paper aimed to assess the application of hybrid PSO-based ANN models to the calculation of horizontal deflection of columns in short building after being subjected to a significant seismic loading (e.g., The Chi-Chi earthquake used as one of the input databases). To prepare both of the training and testing datasets, for the ANN and PSO-ANN network models, a series of finite element (FE) modeling were performed. The used FEM simulation database consists of 8324 training datasets and 2081 testing datasets that is equal to 80% and 20% of the whole database, respectively. The input includes Chi-Chi earthquake dynamic time (s), friction angle (φ), dilation angle (ψ), unit weight (γ), soil elastic modulus (E), Poisson’s ratio (v), structure axial stiffness (EA), and bending stiffness (EI) where the output was taken horizontal deflection of the columns at their highest level (Ux). The result indicates higher reliability of the PSO-ANN model in estimating the ground response and horizontal deflection of structural columns in short structures after being subjected to earthquake loading.
dc.language	en
dc.publisher	Springer Verlag
dc.relation.ispartof	Engineering with Computers: an international journal for simulation-based engineering
dc.relation.isbasedon	10.1007/s00366-019-00733-0
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics
dc.subject.classification	Design Practice & Management
dc.title	Optimizing ANN models with PSO for predicting short building seismic response
dc.type	Journal Article
utslib.citation.volume	36
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0802 Computation Theory and Mathematics
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-23T22:39:06Z
pubs.issue	3
pubs.publication-status	Accepted
pubs.volume	36
utslib.citation.issue	3

Abstract:

© 2019, Springer-Verlag London Ltd., part of Springer Nature. The present study aimed to optimize the artificial neural network (ANN) with one of the well-established optimization algorithms called particle swarm optimization (PSO) for the problem of ground response approximation in short structures. Various studies showed that ANN-based solutions are a reliable method for complex engineering problems. Predicting the ground surface respond to seismic loading is one of the engineering problems that still has not received any ANN solution. Therefore, this paper aimed to assess the application of hybrid PSO-based ANN models to the calculation of horizontal deflection of columns in short building after being subjected to a significant seismic loading (e.g., The Chi-Chi earthquake used as one of the input databases). To prepare both of the training and testing datasets, for the ANN and PSO-ANN network models, a series of finite element (FE) modeling were performed. The used FEM simulation database consists of 8324 training datasets and 2081 testing datasets that is equal to 80% and 20% of the whole database, respectively. The input includes Chi-Chi earthquake dynamic time (s), friction angle (φ), dilation angle (ψ), unit weight (γ), soil elastic modulus (E), Poisson’s ratio (v), structure axial stiffness (EA), and bending stiffness (EI) where the output was taken horizontal deflection of the columns at their highest level (Ux). The result indicates higher reliability of the PSO-ANN model in estimating the ground response and horizontal deflection of structural columns in short structures after being subjected to earthquake loading.

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http://hdl.handle.net/10453/147496