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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Vydané v:Tunnelling and underground space technology Ročník 113; s. 103946
Hlavní autori: Jong, S.C., Ong, D.E.L., Oh, E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.07.2021
Elsevier BV
Predmet:
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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Popis
Shrnutí:•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.
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ISSN:0886-7798
1878-4364
DOI:10.1016/j.tust.2021.103946