Prediction of tunnel boring machine operating parameters using various machine learning algorithms
文献类型：期刊论文
| 作者 | Xu, Chen1; Liu, Xiaoli1,3; Wang, Enzhi1,3; Wang, Sijing1,2,3 |
| 刊名 | TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
 |
| 出版日期 | 2021-03-01 |
| 卷号 | 109页码:12 |
| 关键词 | TBM Operating parameters Machine learning algorithms CNN LSTM |
| ISSN号 | 0886-7798 |
| DOI | 10.1016/j.tust.2020.103699 |
| 英文摘要 | The operating parameters of a tunnel boring machine (TBM) reflect its geological conditions and working status and are accordingly critical data for ensuring safe and efficient tunnel construction. The accurate prediction of the advance rate, rotation speed, thrust, and torque indicators based on the operating parameters can guide the control and application of a TBM. In this study, we analyzed the relationships between the TBM operating parameters and daily collected TBM data. We used the smoothing method and outlier detection to process this data, and determined the stable values of four different TBM indicators in the ascending phase of a complete TBM operational segment. Then, we evaluated the application of five different statistical and ensemble machine learning methods (Bayesian ridge regression (BR), nearest neighbors regression, random forests, gradient tree boosting (GTB), and support vector machine) and two different deep neural networks (a convolutional neural network (CNN) and long short-term memory network (LSTM)) to establish prediction models. The GTB method provided the best prediction accuracy and the BR method provided the least calculation time of the five different statistical and ensemble machine learning methods evaluated. The LSTM method provided a higher prediction accuracy than the CNN model. The ensemble machine learning methods were found to be the most accurate for the relatively limited data sets used in this study, suggesting that sufficient data must be present before the advantages of deep neural networks can be truly realized. The successful application of statistical, ensemble, and deep neural network machine learning methods to predict TBM indicators in this study suggests the promise of machine learning in this application. |
| WOS关键词 | RECURRENT NEURAL-NETWORKS ; INTELLIGENCE ; REGRESSION ; MODEL |
| 资助项目 | National Key Research and Development Plan[2018YFC1504902] ; National Natural Science Foundation of China[52079068] ; National Natural Science Foundation of China[51479094] ; National Natural Science Foundation of China[41772246] ; National Program on Key Basic Research Project (973 Program)[2015CB058100] |
| WOS研究方向 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000612427700004 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Key Research and Development Plan ; National Key Research and Development Plan ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Program on Key Basic Research Project (973 Program) ; National Program on Key Basic Research Project (973 Program) ; National Key Research and Development Plan ; National Key Research and Development Plan ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Program on Key Basic Research Project (973 Program) ; National Program on Key Basic Research Project (973 Program) ; National Key Research and Development Plan ; National Key Research and Development Plan ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Program on Key Basic Research Project (973 Program) ; National Program on Key Basic Research Project (973 Program) ; National Key Research and Development Plan ; National Key Research and Development Plan ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Program on Key Basic Research Project (973 Program) ; National Program on Key Basic Research Project (973 Program) |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/100112]  |
| 专题 | 地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室 |
| 通讯作者 | Liu, Xiaoli |
| 作者单位 | 1.Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China 3.Tsinghua Univ, Sanjiangyuan Collaborat Innovat Ctr, Beijing 100084, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Chen,Liu, Xiaoli,Wang, Enzhi,et al. Prediction of tunnel boring machine operating parameters using various machine learning algorithms[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2021,109:12. |
| APA | Xu, Chen,Liu, Xiaoli,Wang, Enzhi,&Wang, Sijing.(2021).Prediction of tunnel boring machine operating parameters using various machine learning algorithms.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,109,12. |
| MLA | Xu, Chen,et al."Prediction of tunnel boring machine operating parameters using various machine learning algorithms".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 109(2021):12. |
入库方式： OAI收割
来源：地质与地球物理研究所
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