Application of deep learning algorithms in geotechnical engineering: a short critical review

Artificial Intelligence Review, Journal Year: 2021, Volume and Issue: 54(8), P. 5633 - 5673

Published: Feb. 16, 2021

Language: Английский

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Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2022, Volume and Issue: 14(4), P. 1292 - 1303

Published: April 14, 2022

Language: Английский

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A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque

Knowledge-Based Systems, Journal Year: 2021, Volume and Issue: 228, P. 107213 - 107213

Published: June 19, 2021

Language: Английский

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Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2022, Volume and Issue: 14(4), P. 1052 - 1063

Published: Feb. 12, 2022

Accurate prediction of shield tunneling-induced settlement is a complex problem that requires consideration many influential parameters. Recent studies reveal machine learning (ML) algorithms can predict the caused by tunneling. However, well-performing ML models are usually less interpretable. Irrelevant input features decrease performance and interpretability an model. Nonetheless, feature selection, critical step in pipeline, ignored most focused on predicting settlement. This study applies four techniques, i.e. Pearson correlation method, sequential forward selection (SFS), backward (SBS) Boruta algorithm, to investigate effect model's when maximum surface (Smax). The data set used this was compiled from two metro tunnel projects excavated Hangzhou, China using earth pressure balance (EPB) shields consists 14 single output (i.e. Smax). model trained selected algorithm demonstrates best both training testing phases. relevant chosen further indicate affected parameters related geometry, geological conditions operation. recently proposed Shapley additive explanations (SHAP) method explores how contribute It observed larger settlements induced during tunneling silty clay. Moreover, SHAP analysis reveals low magnitudes face at top increase output.

Language: Английский

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Real-time prediction of shield moving trajectory during tunnelling

Acta Geotechnica, Journal Year: 2022, Volume and Issue: 17(4), P. 1533 - 1549

Published: Feb. 4, 2022

Language: Английский

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Deep learning analysis for energy consumption of shield tunneling machine drive system

Tunnelling and Underground Space Technology, Journal Year: 2022, Volume and Issue: 123, P. 104405 - 104405

Published: Feb. 8, 2022

Language: Английский

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Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach

Automation in Construction, Journal Year: 2022, Volume and Issue: 141, P. 104386 - 104386

Published: June 4, 2022

Language: Английский

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Deep learning technologies for shield tunneling: Challenges and opportunities

Automation in Construction, Journal Year: 2023, Volume and Issue: 154, P. 104982 - 104982

Published: June 27, 2023

Language: Английский

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Physics-informed deep learning method for predicting tunnelling-induced ground deformations

Acta Geotechnica, Journal Year: 2023, Volume and Issue: 18(9), P. 4957 - 4972

Published: April 14, 2023

Language: Английский

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Deep learning-based prediction of steady surface settlement due to shield tunnelling

Automation in Construction, Journal Year: 2023, Volume and Issue: 154, P. 105006 - 105006

Published: July 7, 2023

Language: Английский

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