Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework

In this research, the principal purpose is investigating the performance of structural elements significantly degrades at elevated temperatures. Steel–concrete composite floor systems are one of the most relevant components in building construction in which fire-induced problems directly damage thei...

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Published inEngineering with computers Vol. 38; no. 3; pp. 2567 - 2582
Main Authors Morasaei, Armin, Ghabussi, Aria, Aghlmand, Soheila, Yazdani, Maziar, Baharom, Shahrizan, Assilzadeh, Hamid
Format Journal Article
LanguageEnglish
Published London Springer London 01.06.2022
Springer Nature B.V
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Online AccessGet full text
ISSN0177-0667
1435-5663
DOI10.1007/s00366-020-01228-z

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Abstract In this research, the principal purpose is investigating the performance of structural elements significantly degrades at elevated temperatures. Steel–concrete composite floor systems are one of the most relevant components in building construction in which fire-induced problems directly damage their performance. The purpose of this study is on employing analytical intelligence technique to predict two major structural characteristics of the steel–concrete composite floor system. Two intelligent methods, ELM-PSO and ELM-GWO, were used as a multi-combination AI method to predict the shear and tensile response of these composite floor systems at high temperature. Accordingly, authenticated data on monotonic loading response of this steel–concrete composite floor system in different heat stages had been employed from different literatures. The results show that ELM-GWO technique presented the best prediction of split-tensile load, and the ELM-GWO provided the best estimation of slip value. By giving each prediction equations, the best equations were proposed. As a result, employing ELM-GWO, successfully presented reliable and decisive results and proved the proficiency of the techniques.
AbstractList In this research, the principal purpose is investigating the performance of structural elements significantly degrades at elevated temperatures. Steel–concrete composite floor systems are one of the most relevant components in building construction in which fire-induced problems directly damage their performance. The purpose of this study is on employing analytical intelligence technique to predict two major structural characteristics of the steel–concrete composite floor system. Two intelligent methods, ELM-PSO and ELM-GWO, were used as a multi-combination AI method to predict the shear and tensile response of these composite floor systems at high temperature. Accordingly, authenticated data on monotonic loading response of this steel–concrete composite floor system in different heat stages had been employed from different literatures. The results show that ELM-GWO technique presented the best prediction of split-tensile load, and the ELM-GWO provided the best estimation of slip value. By giving each prediction equations, the best equations were proposed. As a result, employing ELM-GWO, successfully presented reliable and decisive results and proved the proficiency of the techniques.
Author Aghlmand, Soheila
Baharom, Shahrizan
Morasaei, Armin
Assilzadeh, Hamid
Yazdani, Maziar
Ghabussi, Aria
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  surname: Morasaei
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  organization: Department of Civil Engineering, K.N. Toosi University of Technology
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  givenname: Aria
  orcidid: 0000-0003-0312-3666
  surname: Ghabussi
  fullname: Ghabussi, Aria
  email: aria.ghabussi@ttu.edu
  organization: Department of Civil, Environmental and Construction Engineering, Texas Tech University
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  surname: Aghlmand
  fullname: Aghlmand, Soheila
  organization: Faculty Member of Architecture, Urbanism and Art Department Architecture, Urmia University
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  surname: Yazdani
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  organization: Faculty of Built Environment, University of New South Wales
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  surname: Baharom
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  organization: Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM
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  givenname: Hamid
  surname: Assilzadeh
  fullname: Assilzadeh, Hamid
  email: hamidassilzadeh@duytan.edu.vn
  organization: Institute of Research and Development, Duy Tan University
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Keywords Steel–concrete
Extreme learning machine
Genetic algorithm
Radial basis function network
Composite floor system
Adaptive neuro-fuzzy inference system
Particle swarm optimization
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Snippet In this research, the principal purpose is investigating the performance of structural elements significantly degrades at elevated temperatures. Steel–concrete...
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SubjectTerms Artificial intelligence
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Composite materials
Computer Science
Computer-Aided Engineering (CAD
Concrete
Control
Fire damage
Floors
Heuristic methods
High temperature
Math. Applications in Chemistry
Mathematical analysis
Mathematical and Computational Engineering
Original Article
Steel structures
Structural members
Systems Theory
Tensile stress
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  priority: 102
  providerName: Springer Nature
Title Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework
URI https://link.springer.com/article/10.1007/s00366-020-01228-z
https://www.proquest.com/docview/2672839944
Volume 38
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