A modified back analysis method for deep excavation with multi-objective optimization procedure
文献类型：期刊论文
| 作者 | Zhao, Chenyang1,3; Chen, Le1; Ni, Pengpeng1,4; Xia, Wenjun2; Wang, Bin3 |
| 刊名 | JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
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| 出版日期 | 2024-04-01 |
| 卷号 | 16期号:4页码:1373-1387 |
| 关键词 | Multi -objective optimization Back analysis Surrogate model Multi -objective particle swarm optimization (MOPSO) Deep excavation |
| ISSN号 | 1674-7755 |
| DOI | 10.1016/j.jrmge.2023.05.007 |
| 英文摘要 | Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety. This paper proposes a modified back analysis method with multi-objective optimization procedure, which enables a real-time prediction of horizontal displacement of retaining pile during construction. As opposed to the traditional stage-by-stage back analysis, time series monitoring data till the current excavation stage are utilized to form a multi-objective function. Then, the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification. The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages. To achieve efficient parameter optimization and real-time prediction of system behavior, the back propagation neural network (BPNN) is established to substitute the finite element model, which is further implemented together with MOPSO for automatic operation. The proposed approach is applied in the Taihu tunnel excavation project, where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data. The method is reliable with a prediction accuracy of more than 90%. Moreover, different optimization algorithms, including non-dominated sorting genetic algorithm (NSGA-II), Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO, are compared, and their influences on the prediction accuracy at different excavation stages are studied. The results show that MOPSO has the best performance for high dimensional optimization task. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). |
| 资助项目 | National Natural Science Foundation of China[52208380] ; National Natural Science Foundation of China[51979270] ; Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences[SKLGME021022] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001271906400001 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42064]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Wang, Bin |
| 作者单位 | 1.Sun Yat Sen Univ, Sch Civil Engn, Guangdong Res Ctr Underground Space Exploitat Tec, Guangzhou 510275, Peoples R China 2.Jiangsu Prov Transportat Engn Construct Bur, Nanjing 210004, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomechan & Geotech Engn, Wuhan 430071, Peoples R China 4.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Chenyang,Chen, Le,Ni, Pengpeng,et al. A modified back analysis method for deep excavation with multi-objective optimization procedure[J]. JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING,2024,16(4):1373-1387. |
| APA | Zhao, Chenyang,Chen, Le,Ni, Pengpeng,Xia, Wenjun,&Wang, Bin.(2024).A modified back analysis method for deep excavation with multi-objective optimization procedure.JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING,16(4),1373-1387. |
| MLA | Zhao, Chenyang,et al."A modified back analysis method for deep excavation with multi-objective optimization procedure".JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING 16.4(2024):1373-1387. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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