Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization

Li, N; Asteris, PG; Tran, TT; Pradhan, B; Nguyen, H

Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization

Li, N Asteris, PG Tran, TT Pradhan, B

Nguyen, H

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Publisher:: TECHNO-PRESS
Publication Type:: Journal Article
Citation:: Steel and Composite Structures, 2022, 42, (6), pp. 733-745
Issue Date:: 2022-03-25

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Field	Value	Language
dc.contributor.author	Li, N
dc.contributor.author	Asteris, PG
dc.contributor.author	Tran, TT
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Nguyen, H
dc.date.accessioned	2023-04-18T04:01:47Z
dc.date.available	2023-04-18T04:01:47Z
dc.date.issued	2022-03-25
dc.identifier.citation	Steel and Composite Structures, 2022, 42, (6), pp. 733-745
dc.identifier.issn	1229-9367
dc.identifier.issn	1598-6233
dc.identifier.uri	http://hdl.handle.net/10453/169930
dc.description.abstract	This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.
dc.language	English
dc.publisher	TECHNO-PRESS
dc.relation.ispartof	Steel and Composite Structures
dc.relation.isbasedon	10.12989/scs.2022.42.6.733
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering
dc.subject.classification	Civil Engineering
dc.title	Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization
dc.type	Journal Article
utslib.citation.volume	42
utslib.for	0905 Civil Engineering
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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-18T04:01:44Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	6

Abstract:

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

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http://hdl.handle.net/10453/169930