An innovative multi-objective optimization approach for compact concrete-filled steel tubular (CFST) column design utilizing lightweight high-strength concrete

被引：0
作者：
Faridmehr I. [1 ]
Nehdi M.L. [2 ]
Nejad A.F. [3 ]
Sahraei M.A. [4 ]
Kamyab H. [5 ,6 ,7 ]
Valerievich K.A. [8 ]
机构：
[1] Civil Engineering Department, Faculty of Engineering, Girne American University
[2] Department of Civil Engineering, McMaster University, Hamilton, L8S 4M6, ON
[3] Faculty of Engineering, School of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru
[4] Department of Civil Engineering, College of Engineering, University of Buraimi, Al Buraimi
[5] Faculty of Architecture and Urbanism, UTE University, Calle Rumipamba S/N and Bourgeois, Quito
[6] Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai
[7] Faculty of Social Sciences, Media and Communication, University of Religions and Denominations, Qom, Pardisan
[8] South Ural State University, 454080 Chelyabinsk, Lenin Prospect 76, Russian Federation
来源：
International Journal of Lightweight Materials and Manufacture | 2024年 / 7卷 / 03期
关键词：
Artificial intelligence; CFST column; Composite; Finite element modeling; Multi-objective optimization; Reinforced concrete;
D O I：
10.1016/j.ijlmm.2024.01.004
中图分类号：
学科分类号：
摘要：
Incorporating sustainability into Concrete-Filled Steel Tubular (CFST) columns' optimization can enhance efficiency and sustainability in construction. Discrepancies in international standards for ultimate load capacity computation in compact CFST columns under eccentric loading, particularly with lightweight high-strength concrete, pose challenges. This research compile a dataset of compact CFST columns, evaluating design codes (AISC 360-16, Eurocode 4) against experimental results. Besides, a comprehensive finite-element model predicts compact CFST column performance, investigating axial force-moment (P-M) interaction behavior with respect to the material strength ratio (fy/fc′). In the second phase of the study, an ANN model, incorporating input parameters, estimates axial load capacity, facilitating multi-objective optimization for optimal CFST column geometry. The results confirmed that Eurocode 4 outperforms AISC 360-16 in experimental axial capacity predictions (Nuc/Nuc,theoretical) where, the mean and standard deviation for Eurocode 4 were estimated at 1.07 and 0.22, respectively, compared to 1.21 and 0.29 for AISC 360-16. Besides, statistical metrics confirm the precision of the ANN model, particularly with high-strength concrete, promising efficiency in future computational intelligence-based structural design platforms. © 2024 The Authors
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页码：405 / 425
页数：20
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