
Construction and Building Materials, Год журнала: 2022, Номер 342, С. 127997 - 127997
Опубликована: Июнь 9, 2022
Язык: Английский
Construction and Building Materials, Год журнала: 2022, Номер 342, С. 127997 - 127997
Опубликована: Июнь 9, 2022
Язык: Английский
Automation in Construction, Год журнала: 2023, Номер 154, С. 104982 - 104982
Опубликована: Июнь 27, 2023
Язык: Английский
Процитировано
63Advanced Engineering Informatics, Год журнала: 2023, Номер 58, С. 102130 - 102130
Опубликована: Авг. 11, 2023
Язык: Английский
Процитировано
49Tunnelling and Underground Space Technology, Год журнала: 2023, Номер 136, С. 105104 - 105104
Опубликована: Март 21, 2023
Язык: Английский
Процитировано
45Neural 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
Процитировано
21Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2022, Номер 14(3), С. 671 - 673
Опубликована: Янв. 19, 2022
Язык: Английский
Процитировано
59Tunnelling and Underground Space Technology, Год журнала: 2022, Номер 131, С. 104830 - 104830
Опубликована: Ноя. 14, 2022
Язык: Английский
Процитировано
59Acta 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
Процитировано
43Journal 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
Процитировано
34Automation in Construction, Год журнала: 2023, Номер 150, С. 104813 - 104813
Опубликована: Март 21, 2023
Язык: Английский
Процитировано
31Tunnelling and Underground Space Technology, Год журнала: 2023, Номер 142, С. 105440 - 105440
Опубликована: Окт. 4, 2023
Язык: Английский
Процитировано
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