Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/3048
DC FieldValueLanguage
dc.contributor.authorLeung, Andrew Yee Taken_US
dc.contributor.otherLi, Q. S.-
dc.contributor.otherLiu, D. K.-
dc.contributor.otherZhang, N.-
dc.contributor.otherTam, C. M.-
dc.contributor.otherYang, L. F.-
dc.date.accessioned2022-04-13T04:44:05Z-
dc.date.available2022-04-13T04:44:05Z-
dc.date.issued2000-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/3048-
dc.description.abstractThis paper proposes an integrated approach to the modelling and optimization of structural control systems in tall buildings. In this approach, an artificial neural network is applied to model the structural dynamic responses of tall buildings subjected to strong earthquakes, and a genetic algorithm is used to optimize the design problem of structural control systems, which constitutes a mixed-discrete, nonlinear and multi-modal optimization problem. The neural network model of the structural dynamic response analysis is included in the genetic algorithm and is used as a module of the structural analysis to estimate the dynamic responses of tall buildings. A numerical example is presented in which the general regression neural network is used to model the structural response analysis. The modelling method, procedure and the numerical results are discussed. Two Los Angeles earthquake records are adopted as earthquake excitations.en_US
dc.language.isoenen_US
dc.publisherJohn Wiley & Sonsen_US
dc.relation.ispartofThe Structural Design of Tall Buildingsen_US
dc.titleModelling of structural response and optimization of structural control system using neural network and genetic algorithmen_US
dc.typejournal articleen_US
dc.identifier.doi10.1002/1099-1794(200009)9:4<279::AID-TAL152>3.0.CO;2-2-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.issn1541-7808en_US
dc.description.volume9en_US
dc.description.issue4en_US
dc.description.startpage279en_US
dc.description.endpage293en_US
dc.cihe.affiliatedNo-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairetypejournal article-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
crisitem.author.deptYam Pak Charitable Foundation School of Computing and Information Sciences-
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