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

No Thumbnail Available
File version
Author(s)
Jong, SC
Ong, DEL
Oh, E
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2021
Size
File type(s)
Location
License
Abstract

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.

Journal Title
Tunnelling and Underground Space Technology
Conference Title
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject
Civil engineering
Resources engineering and extractive metallurgy
Persistent link to this record
Citation
Jong, SC; Ong, DEL; Oh, E, State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction, Tunnelling and Underground Space Technology, 2021, 113, pp. 103946
Collections