Multi-criteria decision-making optimization model for permeable breakwaters characterization

Gandomi, M; Pirooz, MD; Nematollahi, B; Nikoo, MR; Varjavand, I; Etri, T; Gandomi, AH

Multi-criteria decision-making optimization model for permeable breakwaters characterization

Gandomi, M Pirooz, MD Nematollahi, B Nikoo, MR Varjavand, I Etri, T Gandomi, AH

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Ocean Engineering, 2023, 280
Issue Date:: 2023-07-15

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Field	Value	Language
dc.contributor.author	Gandomi, M
dc.contributor.author	Pirooz, MD
dc.contributor.author	Nematollahi, B
dc.contributor.author	Nikoo, MR
dc.contributor.author	Varjavand, I
dc.contributor.author	Etri, T
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-04-29T03:36:34Z
dc.date.available	2024-04-29T03:36:34Z
dc.date.issued	2023-07-15
dc.identifier.citation	Ocean Engineering, 2023, 280
dc.identifier.issn	0029-8018
dc.identifier.issn	1873-5258
dc.identifier.uri	http://hdl.handle.net/10453/178446
dc.description.abstract	Permeable breakwaters have always been of interest due to their advantages over the traditional types. This study proposed a stochastic multi-criteria decision-making model to optimize the geometry of permeable breakwaters. A multi-objective optimization algorithm was conducted using the non-dominated sorting genetic algorithm-II (NSGA-II) coupling with the estimations made by a well-known machine learning (ML) model, the multi-layer perceptron neural network (MLP-NN) to achieve the objective. Considering the inherent uncertainties in the wave characteristics using the conditional value-at-risk (CVaR) method, the presented risk-based model could determine optimal tradeoffs between wave transmission, wave reflection, and rockfill materials volume. This CVaR-based multi-objective optimization model was experimentally applied to a permeable breakwater with maximum significant wave heights of 1–3.5 m at confidence levels of 50%, 75%, 90%, and 99% for risk analysis. To rank several alternatives between the Pareto-optimal solutions, a decisive so-called multi-criterion decision-making (MCDM) approach was employed, which coupled the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and analytical network process (ANP) procedure. Results indicated that the heavier permeable breakwater was the most appropriate for greater wave heights. To this extent, the relative rockfill materials height and width increased to 1.1 from 0.77 and 1.1 from 0.41, respectively, by increasing the specific wave height from 2 to 3.5 m.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Ocean Engineering
dc.relation.isbasedon	10.1016/j.oceaneng.2023.114447
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0405 Oceanography, 0905 Civil Engineering, 0911 Maritime Engineering
dc.subject.classification	Civil Engineering
dc.subject.classification	4005 Civil engineering
dc.subject.classification	4012 Fluid mechanics and thermal engineering
dc.subject.classification	4015 Maritime engineering
dc.title	Multi-criteria decision-making optimization model for permeable breakwaters characterization
dc.type	Journal Article
utslib.citation.volume	280
utslib.for	0405 Oceanography
utslib.for	0905 Civil Engineering
utslib.for	0911 Maritime Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-29T03:36:32Z
pubs.publication-status	Published
pubs.volume	280

Abstract:

Permeable breakwaters have always been of interest due to their advantages over the traditional types. This study proposed a stochastic multi-criteria decision-making model to optimize the geometry of permeable breakwaters. A multi-objective optimization algorithm was conducted using the non-dominated sorting genetic algorithm-II (NSGA-II) coupling with the estimations made by a well-known machine learning (ML) model, the multi-layer perceptron neural network (MLP-NN) to achieve the objective. Considering the inherent uncertainties in the wave characteristics using the conditional value-at-risk (CVaR) method, the presented risk-based model could determine optimal tradeoffs between wave transmission, wave reflection, and rockfill materials volume. This CVaR-based multi-objective optimization model was experimentally applied to a permeable breakwater with maximum significant wave heights of 1–3.5 m at confidence levels of 50%, 75%, 90%, and 99% for risk analysis. To rank several alternatives between the Pareto-optimal solutions, a decisive so-called multi-criterion decision-making (MCDM) approach was employed, which coupled the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and analytical network process (ANP) procedure. Results indicated that the heavier permeable breakwater was the most appropriate for greater wave heights. To this extent, the relative rockfill materials height and width increased to 1.1 from 0.77 and 1.1 from 0.41, respectively, by increasing the specific wave height from 2 to 3.5 m.

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http://hdl.handle.net/10453/178446