An experimental investigation and machine learning-based prediction for seismic performance of steel tubular column filled with recycled aggregate concrete-Reference-Cited by-同舟云学术

An experimental investigation and machine learning-based prediction for seismic performance of steel tubular column filled with recycled aggregate concrete

Published:2022-01-01 Issue:1 Volume:61 Page:849-872
ISSN:1605-8127
Container-title:REVIEWS ON ADVANCED MATERIALS SCIENCE
language:en
Short-container-title:

Author:

Tang Yunchao¹²³,Wang Yufei⁴,Wu Dongxiao⁵,Liu Zhonghe⁶,Zhang Hexin⁷,Zhu Ming⁸,Chen Zheng¹,Sun Junbo⁹,Wang Xiangyu⁴

Affiliation:

1. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil Engineering and Architecture, Guangxi University , Nanning , 530004 , China

2. Guangdong Lingnan Township Green Building Industrialization Engineering Technology Research Center, College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering , Guangzhou , 510225 , China

3. Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, School of Civil Engineering and Architecture, Guangxi University , Nanning , 530004 , China

4. School of Design and Built Environment, Curtin University , Perth , WA 6102 , Australia

5. College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering , Guangzhou , 510225 , China

6. Liyang Market Comprehensive Inspection and Testing Center , Jiangsu, 213300 , China

7. School of Engineering and the Built Environment, Edinburgh Napier University , 10 Colinton Road , Edinburgh , Scotland, EH10 5DT , UK

8. School of Civil Engineering and Architecture, East China JiaoTong University , Nanchang , 530004 , China

9. Institute for Smart City of Chongqing University in Liyang, Chongqing University , Jiangsu, 213300 , China

Abstract

Abstract This work presents the design and application of a low-cycle reciprocating loading test on 23 recycled aggregate concrete-filled steel tube columns and 3 ordinary concrete-filled steel tube columns. Additionally, a systematic study on the influence of various parameters (e.g., slenderness ratio, axial compression ratio, etc.) was conducted on the seismic performance of the specimens. The results show that all the specimens have good hysteresis performance and a similar development trend of skeleton curve. The influence of slenderness ratio on the seismic index of the specimens is more significant than that of the axial compression ratio and the steel pipe wall thickness. Furthermore, artificial intelligence was applied to estimate the influence of parameter variation on the seismic performance of concrete columns. Specifically, Random Forest with hyperparameters tuned by Firefly Algorithm was chosen. The high correlation coefficients (R) and low root mean square error values from the prediction results showed acceptable accuracy. In addition, sensitivity analysis was applied to rank the influence of the aforementioned input variables on the seismic performance of the specimens. The research results can provide experimental reference for the application of steel tube recycled concrete in earthquake areas.

Publisher

Walter de Gruyter GmbH

Subject

Condensed Matter Physics,General Materials Science

Link

https://www.degruyter.com/document/doi/10.1515/rams-2022-0274/pdf

Reference47 articles.

1. Sun, J., Y. Wang, S. Liu, A. Dehghani, X. Xiang, J. Wei, et al. Mechanical, chemical and hydrothermal activation for waste glass reinforced cement. Construction and Building Materials, Vol. 301, 2021, id. 124361.

2. Xu, J., Z. Wu, H. Chen, L. Shao, X. Zhou, and S. Wang. Triaxial shear behavior of basalt fiber-reinforced loess based on digital image technology. KSCE Journal of Civil Engineering, Vol. 25, No. 10, 2021, pp. 3714–3726.

3. Bai, Y., D. C. Nardi, X. Zhou, R. A. Picon, and J. Florez-Lopez. A new comprehensive model of damage for flexural subassemblies prone to fatigue. Computers & Structures, Vol. 256, 2021, id. 106639.

4. Guo, Y., Y. Yang, Z. Kong, and J. He. Development of similar materials for liquid-solid coupling and its application in water outburst and mud outburst model test of deep tunnel. Geofluids, Vol. 2022, 2022. id. 8784398.

5. Li, W., Z. Luo, C. Wu, V. W. Tam, W. H. Duan, and S. P. Shah. Experimental and numerical studies on impact behaviors of recycled aggregate concrete-filled steel tube after exposure to elevated temperature. Materials & Design, Vol. 136, 2017, pp. 103–118.

Cited by 41 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-objective optimization of engineered cementitious composite based on machine learning and generative adversarial network;Journal of Building Engineering;2024-11

2. Performance evaluation of hybrid fiber-reinforced concrete based on electrical resistivity: Experimental and data-driven method;Construction and Building Materials;2024-10

3. Datasets, clues and state-of-the-arts for multimedia forensics: An extensive review;Expert Systems with Applications;2024-09

4. Machine learning-based modeling of interface creep behavior of grouted soil anchors with varying soil moistures;Transportation Geotechnics;2024-09

5. Spatio-temporal autogenous shrinkage and cracking behavior of core concrete in full-scale CFST: Insights from the world's largest span arch bridge;Thin-Walled Structures;2024-07