期刊
AUTOMATION IN CONSTRUCTION
卷 146, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2022.104672
关键词
Deep learning; GCN; Advanced geological prediction; LSTM; Tunnel construction
资金
- National Natural Science Foundation of China [72271101]
- Start-Up Grant at Huazhong University of Science and Technology [3004242122]
This paper investigates a hybrid deep learning approach, combining graph convolutional network (GCN) and long short-term memory (LSTM) networks, for accurate prediction of geological conditions ahead of tunnel boring machines (TBM). The results from the case study demonstrate that the proposed approach provides estimation with high accuracy, outperforming state-of-the-art methods.
This paper investigates the prediction of geological conditions ahead of tunnel boring machines (TBM) using a hybrid deep learning approach. By integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, the spatial and temporal features from TBM parameters and geological information are extracted for accurate prediction. The results from the case study indicate that (1) The proposed approach provides estimation with a high accuracy of 0.9986; (2) The past geological information has a significant contribution to the model; (3) The proposed approach outperforms several state-of-the-art methods including support vector machine (SVM), extreme gradient boosting (XGBoost) and LSTM method. The proposed hybrid deep learning approach can be a useful tool that provides reliable estimation of the advanced geological conditions in real-time.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据