Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning
文献类型：期刊论文
| 作者 | Yin, Xin2,3; Liu, Quansheng2,3; Huang, Xing1; Pan, Yucong2,3 |
| 刊名 | TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 120页码:20 |
| 关键词 | TBM Surrounding rock class Perception model Unsupervised learning Supervised learning |
| ISSN号 | 0886-7798 |
| DOI | 10.1016/j.tust.2021.104285 |
| 英文摘要 | The perception of surrounding rock geological conditions ahead the tunnel face is essential for TBM safe and efficient tunnelling. This paper developed a perception approach of surrounding rock class based on TBM operational big data and combined unsupervised-supervised learning. In data preprocessing, four data mining techniques (i.e., Z-score, K-NN, Kalman filtering, and wavelet packet decomposition) were used to detect outliers, substitute outliers, suppress noise, and extract features, respectively. Then, GMM was used to revise the original surrounding rock class through clustering TBM load parameters and performance parameters in view of the shortcomings of the HC method in the TBM-excavated tunnel. After that, five various ensemble learning classification models were constructed to identify the surrounding rock class, in which model hyper-parameters were automatically tuned by Bayes optimization. In order to evaluate model performance, balanced accuracy, Kappa, F-1-score, and training time were taken into account, and a novel multi-metric comprehensive ranking system was designed. Engineering application results indicated that LightGBM achieved the most superior performance with the highest comprehensive score of 6.9066, followed by GBDT (5.9228), XGBoost (5.4964), RF (3.7581), and AdaBoost (0.9946). Through the weighted purity reduction algorithm, the contributions of input features on the five models were quantitatively analyzed. Finally, the impact of class imbalance on model performance was discussed using the ADASYN algorithm, showing that eliminating class imbalance can further improve the model's perception ability. |
| 资助项目 | National Natural Science Foundation of China[41941018] ; National Natural Science Foundation of China[52074258] ; National Natural Science Foundation of China[42177140] ; National Natural Science Foundation of China[41807250] ; Key Research and Development Project of Hubei Province[2021BCA133] |
| WOS研究方向 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000765018400003 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/34696]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Liu, Quansheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China 3.Wuhan Univ, Sch Civil Engn, Key Lab Geotech & Struct Engn Safety Hubei Prov, Wuhan 430072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Xin,Liu, Quansheng,Huang, Xing,et al. Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2022,120:20. |
| APA | Yin, Xin,Liu, Quansheng,Huang, Xing,&Pan, Yucong.(2022).Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,120,20. |
| MLA | Yin, Xin,et al."Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 120(2022):20. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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