Artificial neural networks coupled with numerical approach for the stability prediction of non-uniform functionally graded microscale cylindrical structures
文献类型：期刊论文
| 作者 | Liang, Zhentang4,5; Zhao, Yunying5; Yu, Haiwen7; Habibi, Mostafa1,2,3,8; Mahmoudi, Tayebeh6 |
| 刊名 | STRUCTURES
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 60页码:15 |
| 关键词 | Concrete materials Steel-concrete structures Nonlinear buckling Artificial Neural Network (ANN) Functionally graded materials |
| ISSN号 | 2352-0124 |
| DOI | 10.1016/j.istruc.2023.105826 |
| 英文摘要 | Functionally graded materials (FGMs) have garnered significant attention in research due to their potential for creating innovative materials with diverse applications. This study focuses on investigating the behavior of steelconcrete functionally graded structures under axial loading, specifically exploring nonlinear buckling. The research considers cylindrical structures with simply-supported boundary conditions and analyzes four different configurations with varying thickness. The governing equations are derived using the modified couple stress theory, incorporating assumptions of nonlinear von-K ' arm ' an theory coupled with classical beam theory. Numerical solutions are obtained using the general differential quadrature (GDQ) method. A notable aspect of this work is the use of Artificial Neural Networks (ANN) to predict nonlinear buckling forces, greatly enhancing the accuracy and efficiency of the predictions. This approach offers a novel method for analyzing concrete structures. The results are validated by comparing them with data from other published works, using dimensionless parameters for consistent comparisons. The findings provide a comprehensive analysis of various parameters, including wall thickness function and porosity, on the buckling stability of the cylindrical structure. Both numerical and ANN approaches are employed to report the effects. The study concludes that the uniform thickness cylinder exhibits the highest resistance to axial loading, resulting in buckling. |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001154969900001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40563]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Zhao, Yunying |
| 作者单位 | 1.Saveetha Inst Med & Tech Sci, Saveetha Dent Coll & Hosp, Dept Biomat, Chennai 600077, India 2.UTE Univ, Fac Architecture & Urbanism, Calle Rumipamba S-N & Bourgeois, Quito, Ecuador 3.Sharif Univ Technol, Ctr Excellence Design Robot & Automat, Dept Mech Engn, Azadi Ave,POB 11365-9567, Tehran, Iran 4.South China Univ Technol, Sch Elect & Informat, Guangzhou 510641, Guangdong, Peoples R China 5.Sichuan Univ, Business Sch, Chengdu 610064, Sichuan, Peoples R China 6.Hoonam Sanat Farnak Engn & Technol Knowledge Based, Ilam, Iran 7.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Hubei, Peoples R China 8.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam |
| 推荐引用方式 GB/T 7714 | Liang, Zhentang,Zhao, Yunying,Yu, Haiwen,et al. Artificial neural networks coupled with numerical approach for the stability prediction of non-uniform functionally graded microscale cylindrical structures[J]. STRUCTURES,2024,60:15. |
| APA | Liang, Zhentang,Zhao, Yunying,Yu, Haiwen,Habibi, Mostafa,&Mahmoudi, Tayebeh.(2024).Artificial neural networks coupled with numerical approach for the stability prediction of non-uniform functionally graded microscale cylindrical structures.STRUCTURES,60,15. |
| MLA | Liang, Zhentang,et al."Artificial neural networks coupled with numerical approach for the stability prediction of non-uniform functionally graded microscale cylindrical structures".STRUCTURES 60(2024):15. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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