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Face pressure control is vulnerable to human misjudgment and human errors can cause severe consequences, especially in difficult ground conditions. From a practical perspective, it is therefore beneficial to have a model capable of predicting the tunnel face pressure given operation and the changing geology. In this paper, we propose such a model based on deep learning. More specifically, a long short\u2010term memory (LSTM) recurrent neural network is employed for tunnel face pressure prediction. To correlate with PLC data, linear interpolation is employed to transform the borehole geological data into sequential geological data according to the shield machine position. The slurry pressure in the excavation chamber (SPE) is taken as the output in the case study of Nanning Metro, which is confronted with the clogging problem due to the mixed ground of mudstone and round gravel. The LSTM\u2010based SPE prediction model achieved an overall MAPE and RMSE of 3.83% and 10.3\u2009kPa, respectively, in mudstone rich ground conditions. Factors that influence the model, including different kinds and length of input data and comparison with the traditional machine learning\u2010based model, are also discussed.<\/jats:p>","DOI":"10.1155\/2021\/6678355","type":"journal-article","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T23:35:06Z","timestamp":1614036906000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Predicting Slurry Pressure Balance with a Long Short\u2010Term Memory Recurrent Neural Network in Difficult Ground Condition"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1336-1780","authenticated-orcid":false,"given":"Qiang","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5357-4003","authenticated-orcid":false,"given":"Xiongyao","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4797-4051","authenticated-orcid":false,"given":"Hongjie","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9063-1209","authenticated-orcid":false,"given":"Michael A","family":"Mooney","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,2,22]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.03.030"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.06.012"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/19648189.2017.1359113"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12665-016-5300-7"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/6708183"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0926-5805(96)00165-3"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2010.11.002"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2013.03.001"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8141259"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.11.013"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8439719"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2733548"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/2017GL075619"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.122884"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"JiaY. 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