State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

•Critical review of Artificial Intelligence (AI) techniques used in geotechnical engineering.•Compilation of key literature related to underground works in the last ten years.•Discussion on impact of AI applications in underground soil-structure interaction.•Robustness of AI techniques increases the...

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Published in:Tunnelling and underground space technology Vol. 113; p. 103946
Main Authors: Jong, S.C., Ong, D.E.L., Oh, E.
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.07.2021
Elsevier BV
Subjects:
Artificial intelligence
Geotechnical engineering
Machine learning
Pile foundations
Rocks
Soil-structure interaction
Soils
State-of-the-art reviews
Tunnels
Underground construction
Underground space
Underground structures
Urbanization
Soil-structure interaction
Underground space
Artificial intelligence
Machine learning
ISSN:0886-7798, 1878-4364
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Abstract •Critical review of Artificial Intelligence (AI) techniques used in geotechnical engineering.•Compilation of key literature related to underground works in the last ten years.•Discussion on impact of AI applications in underground soil-structure interaction.•Robustness of AI techniques increases their demand for underground construction.•Comparison of features reveals the trends of AI in underground geotechnical analyses. There has been an increasing demand for underground construction due to urbanization and limited land in metropolitan cities in the recent years. However, the behavior of underground structures in soils and rocks is often not completely understood. The emergence of Artificial Intelligence (AI) techniques is envisaged to have a huge potential in addressing geotechnical problems that involve complex soil-structure interaction. This paper thus aims at reviewing the applications of AI techniques in studying underground soil-structure interaction, which focuses on aspects such as characterization of soils and rocks, pile foundations, deep excavations and tunneling. An overview of different AI techniques is provided and a list of key AI applications in underground works that have been published in the last ten years is also compiled to study the recent trend of machine learning techniques in underground construction. The capabilities and limitations of these techniques are discussed throughout the paper, to help readers understand various techniques that are suitable for different underground geotechnical applications. Lastly, some of the challenges that may be faced when applying the techniques are identified, and recent development of AI in geotechnical engineering is discussed in which possible countermeasures to overcome these limitations are highlighted.
AbstractList There has been an increasing demand for underground construction due to urbanization and limited land in metropolitan cities in the recent years. However, the behavior of underground structures in soils and rocks is often not completely understood. The emergence of Artificial Intelligence (AI) techniques is envisaged to have a huge potential in addressing geotechnical problems that involve complex soil-structure interaction. This paper thus aims at reviewing the applications of AI techniques in studying underground soil-structure interaction, which focuses on aspects such as characterization of soils and rocks, pile foundations, deep excavations and tunneling. An overview of different AI techniques is provided and a list of key AI applications in underground works that have been published in the last ten years is also compiled to study the recent trend of machine learning techniques in underground construction. The capabilities and limitations of these techniques are discussed throughout the paper, to help readers understand various techniques that are suitable for different underground geotechnical applications. Lastly, some of the challenges that may be faced when applying the techniques are identified, and recent development of AI in geotechnical engineering is discussed in which possible countermeasures to overcome these limitations are highlighted.
•Critical review of Artificial Intelligence (AI) techniques used in geotechnical engineering.•Compilation of key literature related to underground works in the last ten years.•Discussion on impact of AI applications in underground soil-structure interaction.•Robustness of AI techniques increases their demand for underground construction.•Comparison of features reveals the trends of AI in underground geotechnical analyses. There has been an increasing demand for underground construction due to urbanization and limited land in metropolitan cities in the recent years. However, the behavior of underground structures in soils and rocks is often not completely understood. The emergence of Artificial Intelligence (AI) techniques is envisaged to have a huge potential in addressing geotechnical problems that involve complex soil-structure interaction. This paper thus aims at reviewing the applications of AI techniques in studying underground soil-structure interaction, which focuses on aspects such as characterization of soils and rocks, pile foundations, deep excavations and tunneling. An overview of different AI techniques is provided and a list of key AI applications in underground works that have been published in the last ten years is also compiled to study the recent trend of machine learning techniques in underground construction. The capabilities and limitations of these techniques are discussed throughout the paper, to help readers understand various techniques that are suitable for different underground geotechnical applications. Lastly, some of the challenges that may be faced when applying the techniques are identified, and recent development of AI in geotechnical engineering is discussed in which possible countermeasures to overcome these limitations are highlighted.
ArticleNumber 103946
Author Jong, S.C.
Ong, D.E.L.
Oh, E.
Author_xml – sequence: 1
  givenname: S.C.
  surname: Jong
  fullname: Jong, S.C.
  email: siawchian.jong@griffithuni.edu.au
  organization: Griffith University, School of Engineering and Built Environment, 170 Kessels Road, Nathan, Queensland 4111, Australia
– sequence: 2
  givenname: D.E.L.
  surname: Ong
  fullname: Ong, D.E.L.
  email: d.ong@griffith.edu.au
  organization: Griffith University, School of Engineering and Built Environment, 170 Kessels Road, Nathan, Queensland 4111, Australia
– sequence: 3
  givenname: E.
  surname: Oh
  fullname: Oh, E.
  email: y.oh@griffith.edu.au
  organization: Griffith University, School of Engineering and Built Environment, 1 Parklands Drive, Gold Coast Campus, Southport, Queensland 4215, Australia
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Keywords Soil-structure interaction
Underground space
Artificial intelligence
Machine learning
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PQID 2545645783
PQPubID 2045384
ParticipantIDs proquest_journals_2545645783
crossref_citationtrail_10_1016_j_tust_2021_103946
crossref_primary_10_1016_j_tust_2021_103946
elsevier_sciencedirect_doi_10_1016_j_tust_2021_103946
PublicationCentury 2000
PublicationDate July 2021
2021-07-00
20210701
PublicationDateYYYYMMDD 2021-07-01
PublicationDate_xml – month: 07
  year: 2021
  text: July 2021
PublicationDecade 2020
PublicationPlace Oxford
PublicationPlace_xml – name: Oxford
PublicationTitle Tunnelling and underground space technology
PublicationYear 2021
Publisher Elsevier Ltd
Elsevier BV
Publisher_xml – name: Elsevier Ltd
– name: Elsevier BV
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Snippet •Critical review of Artificial Intelligence (AI) techniques used in geotechnical engineering.•Compilation of key literature related to underground works in the...
There has been an increasing demand for underground construction due to urbanization and limited land in metropolitan cities in the recent years. However, the...
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SubjectTerms Artificial intelligence
Geotechnical engineering
Machine learning
Pile foundations
Rocks
Soil-structure interaction
Soils
State-of-the-art reviews
Tunnels
Underground construction
Underground space
Underground structures
Urbanization
Title State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction
URI https://dx.doi.org/10.1016/j.tust.2021.103946
https://www.proquest.com/docview/2545645783
Volume 113
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