$A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment$
Código QR

A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment

Structural health monitoring is an important research field being investigated around the globe. In recent years, meta-heuristics are being used to solve the complex inverse problem of structural damage assessment. In this work, a novel approach depending on a new meta-heuristic and effective object...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nizar Faisal Alkayem, Lei Shen, Panagiotis G. Asteris, Milan Sokol, Zhiqiang Xin, Maosen Cao
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Structural damage assessment Stochastic fractal search Quasi-oppositional learning Modal features Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/9dcdbcd071534e4aad43d4d9f22332e0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9dcdbcd071534e4aad43d4d9f22332e0
record_format	dspace
spelling	oai:doaj.org-article:9dcdbcd071534e4aad43d4d9f22332e02021-11-30T04:13:45ZA new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment1110-016810.1016/j.aej.2021.06.094https://doaj.org/article/9dcdbcd071534e4aad43d4d9f22332e02022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821004592https://doaj.org/toc/1110-0168Structural health monitoring is an important research field being investigated around the globe. In recent years, meta-heuristics are being used to solve the complex inverse problem of structural damage assessment. In this work, a novel approach depending on a new meta-heuristic and effective objective function formulation is proposed. Firstly, by considering some research shortcomings, a triple modal-based objective function combination is employed to improve the precision of damage identification. Secondly, a new self-adaptive algorithm which combines the powerful features of the stochastic fractal search with improved mechanisms into one framework, is developed. Moreover, the concept of quasi-oppositional learning is utilized to improve the overall exploration in both initial and executive stages. The new algorithm, called the self- adaptive quasi-oppositional stochastic fractal search (SA-QSFS), is benchmarked using well-known benchmark functions and applied on the IASC-ASCE FE model for damage assessment. Various damage scenarios are studied using partial modal data and noisy conditions. The proposed technique demonstrates outstanding performance and can be recommended to solve continuous optimization problems.Nizar Faisal AlkayemLei ShenPanagiotis G. AsterisMilan SokolZhiqiang XinMaosen CaoElsevierarticleStructural damage assessmentStochastic fractal searchQuasi-oppositional learningModal featuresEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 3, Pp 1922-1936 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Structural damage assessment Stochastic fractal search Quasi-oppositional learning Modal features Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Structural damage assessment Stochastic fractal search Quasi-oppositional learning Modal features Engineering (General). Civil engineering (General) TA1-2040 Nizar Faisal Alkayem Lei Shen Panagiotis G. Asteris Milan Sokol Zhiqiang Xin Maosen Cao A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment
description	Structural health monitoring is an important research field being investigated around the globe. In recent years, meta-heuristics are being used to solve the complex inverse problem of structural damage assessment. In this work, a novel approach depending on a new meta-heuristic and effective objective function formulation is proposed. Firstly, by considering some research shortcomings, a triple modal-based objective function combination is employed to improve the precision of damage identification. Secondly, a new self-adaptive algorithm which combines the powerful features of the stochastic fractal search with improved mechanisms into one framework, is developed. Moreover, the concept of quasi-oppositional learning is utilized to improve the overall exploration in both initial and executive stages. The new algorithm, called the self- adaptive quasi-oppositional stochastic fractal search (SA-QSFS), is benchmarked using well-known benchmark functions and applied on the IASC-ASCE FE model for damage assessment. Various damage scenarios are studied using partial modal data and noisy conditions. The proposed technique demonstrates outstanding performance and can be recommended to solve continuous optimization problems.
format	article
author	Nizar Faisal Alkayem Lei Shen Panagiotis G. Asteris Milan Sokol Zhiqiang Xin Maosen Cao
author_facet	Nizar Faisal Alkayem Lei Shen Panagiotis G. Asteris Milan Sokol Zhiqiang Xin Maosen Cao
author_sort	Nizar Faisal Alkayem
title	A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment
title_short	A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment
title_full	A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment
title_fullStr	A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment
title_full_unstemmed	A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment
title_sort	new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/9dcdbcd071534e4aad43d4d9f22332e0
work_keys_str_mv	AT nizarfaisalalkayem anewselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT leishen anewselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT panagiotisgasteris anewselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT milansokol anewselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT zhiqiangxin anewselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT maosencao anewselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT nizarfaisalalkayem newselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT leishen newselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT panagiotisgasteris newselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT milansokol newselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT zhiqiangxin newselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment AT maosencao newselfadaptivequasioppositionalstochasticfractalsearchfortheinverseproblemofstructuraldamageassessment
_version_	1718406879021891584

A new self-adaptive quasi-oppositional stochastic fractal search for the inverse problem of structural damage assessment

Ejemplares similares