Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

Wang, Qiang; Xie, Xuansong; Shahrour, Isam

doi:10.1109/access.2020.3041032

Cited by 24 publications

(7 citation statements)

References 60 publications

(68 reference statements)

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“…Wang et al suggested a shield construction advancement model based on LSTM RNNs in an effort to increase the shield building advancement rate. After the model's validity was confirmed, it was discovered that the measured and projected values had a strong correlation (a correlation coefficient of 0.93), which has practical applications in the development of shields [17].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Management of Coal Mine Underground Shield Machine Digging Speed Based on Improved Residual Neural Networks

Xu,

Qi,

Liang

2024

IEEE Access

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Aiming at the lack of accuracy and effectiveness of the current shield machine speed prediction method, the study proposes to improve the residual network and combine this improved algorithm with the surrounding rock category prediction model to construct the underground shield machine digging speed prediction model. With an average accuracy of 87.4%, an F1 value of 0.86, and an accuracy of 0.84, the study's prediction model of surrounding rock categories was determined to be valid and superior to the other compared models. The effectiveness of the improved residual algorithm constructed by the study was verified, and it was found to have a better fit to the actual values, with a maximum deviation error value of 4.6 mm/min and a root mean square error of 1.835, which was lower than the other comparative algorithms. The empirical analysis of the underground shield machine digging speed prediction model constructed by the study revealed that the area under the line of the work characteristic curve of the subjects was 0.74, and the F1 value was 0.35, and the accuracy was as high as 84.6%, which was significantly better than that of other comparative models. The shield machine digging speed prediction model, which is based on an enhanced residual network built in the study, performs better than other comparison models, according to the results, which can serve as a theoretical guide for the digital management of coal mine output.

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Section: Related Workmentioning

confidence: 99%

Real-Time Management of Coal Mine Underground Shield Machine Digging Speed Based on Improved Residual Neural Networks

Xu,

Qi,

Liang

2024

IEEE Access

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“…AI methods can analyze massive data with relatively few computational resources and obtain complex relationships between input parameters and responses [1,19]. Compared with manual decisions, AI methods obtained by summarizing data can provide better guidance for adjusting tunnelling parameters [9,39]. Therefore, developing an intelligent method for predicting shield tunnelling parameters is of great significance for both construction safety and efficiency.…”

Section: Introductionmentioning

confidence: 99%

An intelligent construction model in whole process for shield tunnelling

Lu,

Liu,

Kong

et al. 2024

Preprint

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Predicting shield tunnelling parameters in the whole construction process is of great importance, which can effectively control ground stability and improve tunnelling efficiency. A novel deep learning method is developed considering transfer learning, incremental learning and Bi-LSTM fusing with available data of the next ring to be excavated (ADNRE) to predict shield tunnelling parameters in the whole process. Before construction, transfer learning uses data from similar projects to determine initial network parameters, then solve the insufficient data in the prophase of the project. As the shield machine begins to excavate, incremental learning is used to continuously accept new data and adjust model parameters in real time during the whole process. A feature fusion module in Bi-LSTM is proposed to integrate ADNRE and data of the adjacent excavated rings. The proposed Bi-LSTM method can consider the mutation of stratum conditions during tunnelling. The applicability of the proposed method is explored by predicting the shield cutter head torque of a tunnel project in Qingdao, China. The influence of fine-tuning epochs and project similarity on model performance is further discussed. Overall, the proposed method can provide reasonable whole process prediction for shield tunnelling parameters, which improves construction safety and efficiency.

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“…Benardos et al [10] employed the Artificial Neural Network (ANN) to predict the hard rock TBM advance rate in the Athens metro tunnel. Wang et al [11] used time aggregation random forest to rank the importance of interpretative features and established a long short-term memory recurrent neural network model. The results show that the LSTM model can effectively realize advance rate prediction.…”

Section: Introductionmentioning

confidence: 99%

Research on Prediction of EPB Shield Tunneling Parameters Based on LGBM

Wang,

Feng,

et al. 2024

Buildings

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At present, the determination of tunnel parameters mainly rely on engineering experience and human judgment, which leads to the subjective decision of parameters and an increased construction risk. Machine learning algorithms could provide an objective theoretical basis for tunnel parameter decision making. However, due to the limitations of a machine learning model’s performance and parameter selection methods, the prediction model had poor prediction results and low reliability for parameter research. To solve the above problems, based on a large number of construction parameters of a composite section subway in Shenzhen, this paper combined dimensionality reduction data with service analysis to optimize the selection process of shield tunneling parameters, and determined the total propulsion force, cutter head torque, cutter head speed, and advance rate as key tunneling parameters. Based on an LGBM algorithm and Bayesian optimization, the prediction model of key tunneling parameters of an earth pressure balance shield was established. The results showed that the average error of the LGBM model on the test set was 8.18%, the average error of the cutter head torque was 13.93%, the average error of the cutter head speed was 3.16%, and the average error of advance rate was 13.35%. Compared with the RF model, the prediction effect and the generalization on the test set were better. Therefore, an LGBM algorithm could be used as an effective prediction method for tunneling parameters in tunnel construction and provide guidance for the setting of tunneling parameters.

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Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

Cited by 24 publications

References 60 publications

Real-Time Management of Coal Mine Underground Shield Machine Digging Speed Based on Improved Residual Neural Networks

Real-Time Management of Coal Mine Underground Shield Machine Digging Speed Based on Improved Residual Neural Networks

An intelligent construction model in whole process for shield tunnelling

Research on Prediction of EPB Shield Tunneling Parameters Based on LGBM

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