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 in | Engineering with computers Vol. 38; no. 3; pp. 2567 - 2582 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Springer London
01.06.2022
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 0177-0667 1435-5663 |
DOI | 10.1007/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. |
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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 |
Author_xml | – sequence: 1 givenname: Armin surname: Morasaei fullname: Morasaei, Armin organization: Department of Civil Engineering, K.N. Toosi University of Technology – sequence: 2 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 – sequence: 3 givenname: Soheila surname: Aghlmand fullname: Aghlmand, Soheila organization: Faculty Member of Architecture, Urbanism and Art Department Architecture, Urmia University – sequence: 4 givenname: Maziar surname: Yazdani fullname: Yazdani, Maziar organization: Faculty of Built Environment, University of New South Wales – sequence: 5 givenname: Shahrizan surname: Baharom fullname: Baharom, Shahrizan organization: Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM – sequence: 6 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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Title | Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework |
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