Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review
Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algori...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/4/2/87 |
| _version_ | 1797595062161375232 |
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| author | Feng Shan Xuzhen He Haoding Xu Danial Jahed Armaghani Daichao Sheng |
| author_facet | Feng Shan Xuzhen He Haoding Xu Danial Jahed Armaghani Daichao Sheng |
| author_sort | Feng Shan |
| collection | DOAJ |
| description | Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects. |
| first_indexed | 2024-03-11T02:31:04Z |
| format | Article |
| id | doaj.art-c19ae343e36148ca8feee39c2ed341ea |
| institution | Directory Open Access Journal |
| issn | 2673-4117 |
| language | English |
| last_indexed | 2024-03-11T02:31:04Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj.art-c19ae343e36148ca8feee39c2ed341ea2023-11-18T10:15:09ZengMDPI AGEng2673-41172023-05-01421516153510.3390/eng4020087Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic ReviewFeng Shan0Xuzhen He1Haoding Xu2Danial Jahed Armaghani3Daichao Sheng4School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, AustraliaSchool of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, AustraliaSchool of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, AustraliaSchool of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, AustraliaSchool of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, AustraliaTunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects.https://www.mdpi.com/2673-4117/4/2/87tunnel boring machinemachine learningTBM performancesurface settlementtime series forecasting |
| spellingShingle | Feng Shan Xuzhen He Haoding Xu Danial Jahed Armaghani Daichao Sheng Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review Eng tunnel boring machine machine learning TBM performance surface settlement time series forecasting |
| title | Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review |
| title_full | Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review |
| title_fullStr | Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review |
| title_full_unstemmed | Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review |
| title_short | Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review |
| title_sort | applications of machine learning in mechanised tunnel construction a systematic review |
| topic | tunnel boring machine machine learning TBM performance surface settlement time series forecasting |
| url | https://www.mdpi.com/2673-4117/4/2/87 |
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