Predicting Model of Dual-Mode Shield Tunneling Parameters in Complex Ground Using Recurrent Neural Networks and Multiple Optimization Algorithms
文献类型：期刊论文
| 作者 | Yang, Taihua2; Wen, Tian2; Huang, Xing3; Liu, Bin3; Shi, Hongbing4; Liu, Shaoran1; Peng, Xiaoxiang2; Sheng, Guangzu5; Correia, Jose Antonio |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2024 |
| 卷号 | 14期号:2页码:18 |
| 关键词 | shield tunneling complex strata EPB/TBM dual-mode shield tunneling tunneling parameter prediction recurrent neural network |
| DOI | 10.3390/app14020581 |
| 英文摘要 | Based on the left tunnel of the Liuxiandong Station to Baimang Station section of Shenzhen Metro Line 13 (China), a prediction model for the advanced rate of dual-mode shield tunneling in complex strata was established to explore intelligent tunneling technology in complex ground. Firstly, geological parameters of the complex strata and on-site monitoring parameters of EPB/TBM dual-mode shield tunneling were collected, with tunneling parameters, shield tunneling mode, and strata parameters selected as input features. Subsequently, the Isolation Forest algorithm was employed to remove outliers from the original advance parameters, and an improved mean filtering algorithm was applied to eliminate data noise, resulting in the steady-state phase parameters of the shield tunneling process. The base model was chosen as the Long-Short Term Memory (LSTM) recurrent neural network. During the model training process, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and Bayesian optimization (BO) algorithms were, respectively, combined to optimize the model's hyperparameters. Via rank analysis based on evaluation metrics, the BO-LSTM model was found to have the shortest runtime and highest accuracy. Finally, the dropout algorithm and five-fold time series cross-validation were incorporated into the BO-LSTM model, creating a multi-algorithm-optimized recurrent neural network model for predicting tunneling speed. The results indicate that (1) the Isolation Forest algorithm can conveniently identify outliers while considering the relationship between tunneling speed and other parameters; (2) the improved mean filtering algorithm exhibits better denoising effects on cutterhead speed and tunneling speed; and (3) the multi-algorithm optimized LSTM model exhibits high prediction accuracy and operational efficiency under various geological parameters and different excavation modes. The minimum Mean Absolute Percentage Error (MAPE) prediction result is 8.3%, with an average MAPE prediction result below 15%. |
| 资助项目 | National Natural Science Foundation of China Regional Joint Key Project |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001149077400001 |
| 出版者 | MDPI |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/40419]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Huang, Xing; Liu, Bin |
| 作者单位 | 1.China Construct South Investment Co Ltd, Shenzhen 518000, Peoples R China 2.Wuhan Univ Sci & Technol, Sch Urban Construct, Wuhan 430083, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 4.China Construct Civil Infrastruct Corp Ltd, Beijing 100029, Peoples R China 5.Wuhan Urban Construct Grp Construct Management Co, Wuhan 430040, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Taihua,Wen, Tian,Huang, Xing,et al. Predicting Model of Dual-Mode Shield Tunneling Parameters in Complex Ground Using Recurrent Neural Networks and Multiple Optimization Algorithms[J]. APPLIED SCIENCES-BASEL,2024,14(2):18. |
| APA | Yang, Taihua.,Wen, Tian.,Huang, Xing.,Liu, Bin.,Shi, Hongbing.,...&Correia, Jose Antonio.(2024).Predicting Model of Dual-Mode Shield Tunneling Parameters in Complex Ground Using Recurrent Neural Networks and Multiple Optimization Algorithms.APPLIED SCIENCES-BASEL,14(2),18. |
| MLA | Yang, Taihua,et al."Predicting Model of Dual-Mode Shield Tunneling Parameters in Complex Ground Using Recurrent Neural Networks and Multiple Optimization Algorithms".APPLIED SCIENCES-BASEL 14.2(2024):18. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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