Optimum design of reinforced earth walls using evolutionary optimization algorithms

Kashani, AR; Saneirad, A; Gandomi, AH

Optimum design of reinforced earth walls using evolutionary optimization algorithms

Kashani, AR Saneirad, A Gandomi, AH

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Publisher:: SPRINGER LONDON LTD
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2020, 32, (16), pp. 12079-12102
Issue Date:: 2020-08-01

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Field	Value	Language
dc.contributor.author	Kashani, AR
dc.contributor.author	Saneirad, A
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2021-03-24T19:41:47Z
dc.date.available	2021-03-24T19:41:47Z
dc.date.issued	2020-08-01
dc.identifier.citation	Neural Computing and Applications, 2020, 32, (16), pp. 12079-12102
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/147509
dc.description.abstract	© 2019, Springer-Verlag London Ltd., part of Springer Nature. This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results.
dc.language	English
dc.publisher	SPRINGER LONDON LTD
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-019-04264-z
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Optimum design of reinforced earth walls using evolutionary optimization algorithms
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
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/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-24T19:41:42Z
pubs.issue	16
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	16

Abstract:

© 2019, Springer-Verlag London Ltd., part of Springer Nature. This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results.

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http://hdl.handle.net/10453/147509