BILSTM-Based Deep Neural Network for Rock-Mass Classification Prediction Using Depth-Sequence MWD Data: A Case Study of a Tunnel in Yunnan, China
文献类型：期刊论文
| 作者 | Cheng, Xu1,2; Tang, Hua1,2; Wu, Zhenjun1,2; Liang, Dongcai1,2; Xie, Yachen3 |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2023-05-15 |
| 卷号 | 13期号:10页码:- |
| 关键词 | rock mass classification measurement while drilling deep neural networks Bi-directional Long Short-Term Memory |
| 英文摘要 | Measurement while drilling (MWD) data reflect the drilling rig-rock mass interaction; they are crucial for accurately classifying the rock mass ahead of the tunnel face. Although machine-learning methods can learn the relationship between MWD data and rock mechanics parameters to support rock classification, most current models do not consider the impact of the continuous drilling-sequence process, thereby leading to rock-classification errors, while small and unbalanced field datasets result in poor model performance. We propose a novel deep neural network model based on Bi-directional Long Short-Term Memory (BILSTM) to extract information-related sequences in MWD data and improve the accuracy of the rock-mass classification. Two optimization modules were designed to improve the model's generalization performance. Stratified K-fold cross-validation was used for model optimization in small and unbalanced datasets. Model validation is based on the MWD dataset of a highway tunnel in Yunnan, China. Multiple metrics show that the prediction ability of the network is significantly better than those of a multilayer perceptron (MLP) and a support-vector machine (SVM), while the model exhibits an improved generalization performance. The accuracy of the network can reach 90%, which is 13% and 15% higher than the MLP and SVM, respectively. |
| 学科主题 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000995522400001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/35099]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China 3.School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China |
| 推荐引用方式 GB/T 7714 | Cheng, Xu,Tang, Hua,Wu, Zhenjun,et al. BILSTM-Based Deep Neural Network for Rock-Mass Classification Prediction Using Depth-Sequence MWD Data: A Case Study of a Tunnel in Yunnan, China[J]. APPLIED SCIENCES-BASEL,2023,13(10):-. |
| APA | Cheng, Xu,Tang, Hua,Wu, Zhenjun,Liang, Dongcai,&Xie, Yachen.(2023).BILSTM-Based Deep Neural Network for Rock-Mass Classification Prediction Using Depth-Sequence MWD Data: A Case Study of a Tunnel in Yunnan, China.APPLIED SCIENCES-BASEL,13(10),-. |
| MLA | Cheng, Xu,et al."BILSTM-Based Deep Neural Network for Rock-Mass Classification Prediction Using Depth-Sequence MWD Data: A Case Study of a Tunnel in Yunnan, China".APPLIED SCIENCES-BASEL 13.10(2023):-. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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