TBM penetration rate prediction based on the long short-term memory neural network

• Published in: Underground space (Beijing) Vol. 6; no. 6; pp. 718 - 731
• Main Authors: Gao, Boyang, Wang, RuiRui, Lin, Chunjin, Guo, Xu, Liu, Bin, Zhang, Wengang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021; KeAi Communications Co., Ltd
• Subjects: Long short-term memory; Penetration rate; TBM performance prediction; Water conveyance tunnel; Water conveyance tunnel; Penetration rate; Long short-term memory; TBM performance prediction
• ISSN: 2467-9674; 2467-9674
• DOI: 10.1016/j.undsp.2020.01.003

Tunnel boring machines (TBMs) are widely used in tunnel engineering because of their safety and efficiency. The TBM penetration rate (PR) is crucial, as its real-time prediction can reflect the adaptation of a TBM under current geological conditions and assist the adjustment of operating parameters. In this study, deep learning technology is applied to TBM performance prediction, and a PR prediction model based on a long short-term memory (LSTM) neuron network is proposed. To verify the performance of the proposed model, the machine parameters, rock mass parameters, and geological survey data from the water conveyance tunnel of the Hangzhou Second Water Source project were collected to form a dataset. Furthermore, 2313 excavation cycles were randomly composed of training datasets to train the LSTM-based model, and 257 excavation cycles were used as a testing dataset to test the performance. The root mean square error and the mean absolute error of the proposed model are 4.733 and 3.204, respectively. Compared with Recurrent neuron network (RNN) based model and traditional time-series prediction model autoregressive integrated moving average with explanation variables (ARIMAX), the overall performance on proposed model is better. Moreover, in the rapidly increasing period of the PR, the error of the LSTM-based model prediction curve is significantly smaller than those of the other two models. The prediction results indicate that the LSTM-based model proposed herein is relatively accurate, thereby providing guidance for the excavation process of TBMs and offering practical application value.