- Title
- Application of artificial intelligence in geotechnical engineering : a state-of-the-art review
- Creator
- Baghbani, Abolfazl; Choudhury, Tanveer; Costa, Susanga; Reiner, Johannes
- Date
- 2022
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188491
- Identifier
- vital:17258
- Identifier
-
https://doi.org/10.1016/j.earscirev.2022.103991
- Identifier
- ISSN:0012-8252 (ISSN)
- Abstract
- Geotechnical engineering deals with soils and rocks and their use in engineering constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of uncertainty in material modelling. Artificial intelligence (AI) methods have been developed and used by an increasing number of researchers in the field of geotechnical engineering in the last three decades. These methods have been considered successful due to their ability to predict complex nonlinear relationships. Based on more than one thousand (i.e. 1235) published literatures, this paper presents a detailed review of the performance of AI methods and algorithms used in geotechnical engineering. Nine key areas where the application of AI methods is prominent were identified: frozen soils and soil thermal properties, rock mechanics, subgrade soil and pavements, landslide and soil liquefaction, slope stability, shallow and piles foundations, tunnelling and tunnel boring machine, dams, and unsaturated soils. Artificial Neural Network (ANN) emerged as the most widely used and preferred AI method with 52% of studies relying on it. Other methods that were used to a lesser extent were FIS, ANFIS, SVM, LSTM, CNN, ResNet and GAN. The analysis shows that the success and accuracy of AI applications depends on the number and type of datasets and selection of input parameters. The paper also provides statistical information on research incorporating AI methods and discusses the opportunities and challenges for future research and practical applications in geotechnical engineering. © 2022 Elsevier B.V.
- Publisher
- Elsevier B.V.
- Relation
- Earth-Science Reviews Vol. 228, no. (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 Elsevier B.V.
- Subject
- 3705 Geology; 3706 Geophysics; 3709 Physical geography and environmental geoscience; Artificial intelligence; Artificial neural network; Geotechnical engineering; Modelling; Soil mechanics
- Reviewed
- Funder
- This research is supported by an Australian Government Research Training Program (RTP) Scholarship.
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