Application of optimized random forest regressors in predicting the maximum principal stress of aseismic tunnel lining
文献类型：期刊论文
| 作者 | Mei, Xian-cheng4,5; Ding, Chang-dong1; Zhang, Jia-min2; Li, Chuan-qi3; Cui, Zhen4,5; Sheng, Qian4,5; Chen, Jian4,5 |
| 刊名 | JOURNAL OF CENTRAL SOUTH UNIVERSITY
 |
| 出版日期 | 2024-06-20 |
| 页码 | 14 |
| 关键词 | maximum principal stress aseismic tunnel lining random forest regressor machine learning (sic)(sic)(sic)(sic)(sic) (sic)(sic)(sic)(sic)(sic)(sic) (sic)(sic)(sic)(sic)(sic)(sic) (sic)(sic)(sic)(sic) |
| ISSN号 | 2095-2899 |
| DOI | 10.1007/s11771-024-5680-x |
| 英文摘要 | Using flexible damping technology to improve tunnel lining structure is an emerging method to resist earthquake disasters, and several methods have been explored to predict mechanical response of tunnel lining with damping layer. However, the traditional numerical methods suffer from the complex modelling and time-consuming problems. Therefore, a prediction model named the random forest regressor (RFR) is proposed based on 240 numerical simulation results of the mechanical response of tunnel lining. In addition, circle mapping (CM) is used to improve Archimedes optimization algorithm (AOA), reptile search algorithm (RSA), and Chernobyl disaster optimizer (CDO) to further improve the predictive performance of the RFR model. The performance evaluation results show that the CMRSA-RFR is the best prediction model. The damping layer thickness is the most important feature for predicting the maximum principal stress of tunnel lining containing damping layer. This study verifies the feasibility of combining numerical simulation with machine learning technology, and provides a new solution for understanding the mechanical response of aseismic tunnel with damping layer. |
| 资助项目 | National Key R&D Programs for Young Scientists, China[2023YFB2390400] ; National Natural Science Foundation of China[U21A20159] ; National Natural Science Foundation of China[52079133] ; National Natural Science Foundation of China[52379112] ; National Natural Science Foundation of China[52309123] ; National Natural Science Foundation of China[41902288] ; Hubei Provincial Natural Science Foundation,China[2024AFB041] ; Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources, China[QTKS0034W23291] ; Visiting Researcher Fund Program of State Key Laboratory of Water Resources Engineering and Management, China[2023SGG07] ; Key Research Program of FSDI, China[2022KY56(ZDZX)-02] ; Key Research Program of the Ministry of Water Resources, China[SKS-2022103] ; Yunnan Major Science and Technology Special Program, China[202102AF080001] |
| WOS研究方向 | Metallurgy & Metallurgical Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001251786000002 |
| 出版者 | JOURNAL OF CENTRAL SOUTH UNIV |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/41780]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Li, Chuan-qi |
| 作者单位 | 1.Minist Water Resources, Changjiang River Sci Res Inst, Key Lab Geotech Mech & Engn, Wuhan 430010, Peoples R China 2.SINOPEC Res Inst Petr Engn, Beijing 100101, Peoples R China 3.Grenoble Alpes Univ, Lab 3SR, CNRS, UMR 5521, F-38000 Grenoble, France 4.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Mei, Xian-cheng,Ding, Chang-dong,Zhang, Jia-min,et al. Application of optimized random forest regressors in predicting the maximum principal stress of aseismic tunnel lining[J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY,2024:14. |
| APA | Mei, Xian-cheng.,Ding, Chang-dong.,Zhang, Jia-min.,Li, Chuan-qi.,Cui, Zhen.,...&Chen, Jian.(2024).Application of optimized random forest regressors in predicting the maximum principal stress of aseismic tunnel lining.JOURNAL OF CENTRAL SOUTH UNIVERSITY,14. |
| MLA | Mei, Xian-cheng,et al."Application of optimized random forest regressors in predicting the maximum principal stress of aseismic tunnel lining".JOURNAL OF CENTRAL SOUTH UNIVERSITY (2024):14. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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