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Erschienen in: Neural Computing and Applications 14/2024

23.02.2024 | Original Article

Hybrid bio-inspired metaheuristic approach for design compressive strength of high-strength concrete-filled high-strength steel tube columns

verfasst von: Masoud Ahmadi, Mehdi Ebadi-Jamkhaneh, Ahmad Dalvand, Danial Rezazadeh Eidgahee

Erschienen in: Neural Computing and Applications | Ausgabe 14/2024

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Abstract

The specifications of AISC 360 and Eurocode 4 for the design of composite columns limit the maximum steel tube yield stress and concrete compressive strength. In this study, the limitations mentioned in the design codes are evaluated, and a new simplified relation for the nominal compressive capacity of square high-strength concrete-filled high-strength steel tube (HsCHsST) stub columns is proposed. The present study was carried out in three parts. The first part involves compiling a test database of square-filled composite columns with high-strength materials to achieve the aptest relation. The second part consists of developing a simplified relation for determining the effects of material strength on the nominal compressive strength of columns (\({P}_{{\text{n}}}\)) based on the compiled database using a hybrid gene expression programming-invasive weed optimization algorithm. Finally, a new resistance factor (\({\phi }_{{\text{c}}}\)) for axially loaded HsCHsST columns is determined in the third part. The predicted results for nominal compressive strength were compared with specifications of AISC 360 and Eurocode 4 in terms of various performance parameters based on measured results to validate the proposed relation. This study’s findings can be used as a suitable tool in the design compressive strength of square HsCHsST members.

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Metadaten
Titel
Hybrid bio-inspired metaheuristic approach for design compressive strength of high-strength concrete-filled high-strength steel tube columns
verfasst von
Masoud Ahmadi
Mehdi Ebadi-Jamkhaneh
Ahmad Dalvand
Danial Rezazadeh Eidgahee
Publikationsdatum
23.02.2024
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 14/2024
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-024-09494-4

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