Deep learning technologies for shield tunneling: Challenges and opportunities

Automation in Construction, Год журнала: 2023, Номер 154, С. 104982 - 104982

Опубликована: Июнь 27, 2023

Язык: Английский

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A LightGBM-based strategy to predict tunnel rockmass class from TBM construction data for building control

Advanced Engineering Informatics, Год журнала: 2023, Номер 58, С. 102130 - 102130

Опубликована: Авг. 11, 2023

Язык: Английский

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Deep reinforcement learning approach to optimize the driving performance of shield tunnelling machines

Tunnelling and Underground Space Technology, Год журнала: 2023, Номер 136, С. 105104 - 105104

Опубликована: Март 21, 2023

Язык: Английский

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Neural Computing and Applications, Год журнала: 2024, Номер 36(21), С. 12655 - 12699

Опубликована: Май 13, 2024

Abstract Artificial neural networks (ANN), machine learning (ML), deep (DL), and ensemble (EL) are four outstanding approaches that enable algorithms to extract information from data make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, EL models have found extensive application in predicting geotechnical geoenvironmental parameters. This research aims provide a comprehensive assessment of applications addressing forecasting within field related engineering, including soil mechanics, foundation rock environmental geotechnics, transportation geotechnics. Previous studies not collectively examined all algorithms—ANN, EL—and explored their advantages disadvantages engineering. categorize address this gap existing literature systematically. An dataset relevant was gathered Web Science subjected an analysis based on approach, primary focus objectives, year publication, geographical distribution, results. Additionally, study included co-occurrence keyword covered techniques, systematic reviews, review articles data, sourced Scopus database through Elsevier Journal, were then visualized using VOS Viewer further examination. The results demonstrated ANN is widely utilized despite proven potential methods engineering due real-world laboratory civil engineers often encounter. However, when it comes behavior scenarios, techniques outperform three other methods. discussed here assist understanding benefits geo area. enables practitioners select most suitable creating certainty resilient ecosystem.

Язык: Английский

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Editorial for Advances and applications of deep learning and soft computing in geotechnical underground engineering

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2022, Номер 14(3), С. 671 - 673

Опубликована: Янв. 19, 2022

Язык: Английский

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A new index for cutter life evaluation and ensemble model for prediction of cutter wear

Tunnelling and Underground Space Technology, Год журнала: 2022, Номер 131, С. 104830 - 104830

Опубликована: Ноя. 14, 2022

Язык: Английский

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Identification of geological characteristics from construction parameters during shield tunnelling

Acta Geotechnica, Год журнала: 2022, Номер 18(1), С. 535 - 551

Опубликована: Май 28, 2022

Abstract This paper proposes a framework to identify geological characteristics (GC) based on borehole data and operational during shield tunnelling using fuzzy C-means algorithm. The proposed model was established by integrating the K-means ++ algorithm into set theory. identified factors for GC include advance rate, cutterhead rotation speed, thrust, torque, penetration torque index, field specific energy. Principal component analysis employed reduce dimensions of these factors. first six principal components were analyse establish input in model. types determined elbow method, silhouette coefficient, partition coefficient profile from data. approach validated case Guangzhou intercity tunnel construction. results present that can effectively determine provide membership reveal proportion hard rock.

Язык: Английский

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Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2023, Номер 16(5), С. 1538 - 1551

Опубликована: Сен. 4, 2023

Tunnel boring machines (TBMs) have been widely utilised in tunnel construction due to their high efficiency and reliability. Accurately predicting TBM performance can improve project time management, cost control, risk management. This study aims use deep learning develop real-time models for the penetration rate (PR). The are built using data from Changsha metro project, performances evaluated unseen Zhengzhou Metro project. In one-step forecast, predicted follows trend of measured both training testing. autoregressive integrated moving average (ARIMA) model is compared with recurrent neural network (RNN) model. results show that univariate models, which only consider historical itself, perform better than multivariate take into account multiple geological operational parameters (GEO OP). Next, an RNN variant combining series last-step developed, it performs other models. A sensitivity analysis shows most important parameter, while a smaller impact on forecasting. It also found smoothed easier predict accuracy. Nevertheless, over-simplified lose real characteristics series. conclusion, accurately next-step rate, smoothing crucial provides practical guidance forecasting engineering.

Язык: Английский

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Estimating locations of soil–rock interfaces based on vibration data during shield tunnelling

Automation in Construction, Год журнала: 2023, Номер 150, С. 104813 - 104813

Опубликована: Март 21, 2023

Язык: Английский

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GFII: A new index to identify geological features during shield tunnelling

Tunnelling and Underground Space Technology, Год журнала: 2023, Номер 142, С. 105440 - 105440

Опубликована: Окт. 4, 2023

Язык: Английский

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