Application of artificial intelligence in geotechnical and geohazard investigations

Zhang, W; Pradhan, B; Stuyts, B; Xu, C

Application of artificial intelligence in geotechnical and geohazard investigations

Zhang, W Pradhan, B

Stuyts, B Xu, C

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Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Geological Journal, 2023, 58, (6), pp. 2187-2194
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Zhang, W
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Stuyts, B
dc.contributor.author	Xu, C
dc.date.accessioned	2024-03-25T04:02:03Z
dc.date.available	2024-03-25T04:02:03Z
dc.date.issued	2023-06-01
dc.identifier.citation	Geological Journal, 2023, 58, (6), pp. 2187-2194
dc.identifier.issn	0072-1050
dc.identifier.issn	1099-1034
dc.identifier.uri	http://hdl.handle.net/10453/177087
dc.description.abstract	The application of artificial intelligence (AI) and big data in geohazard investigations has gained popularity due to the development of machine learning algorithms and data collection methods. Previous studies have compared and applied various machine learning-based methods, such as conventional machine learning, deep learning, and transfer learning in different areas. This special issue provides state-of-the-art information on the use of AI in geotechnical research, particularly in the Three Gorges Reservoir (TGR) area and adjoining regions. The aim of this volume is to serve as a reference for future researchers interested in exploring the potential of AI in geohazard investigations. It is hoped that this special issue will contribute to the development of guidelines for enhancing the application of AI and big data in geotechnical research, thereby improving our understanding of geological terrains and their associated hazards.
dc.language	en
dc.publisher	Wiley
dc.relation.ispartof	Geological Journal
dc.relation.isbasedon	10.1002/gj.4779
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0402 Geochemistry, 0403 Geology, 0404 Geophysics
dc.subject.classification	Geology
dc.subject.classification	3703 Geochemistry
dc.subject.classification	3705 Geology
dc.title	Application of artificial intelligence in geotechnical and geohazard investigations
dc.type	Journal Article
utslib.citation.volume	58
utslib.for	0402 Geochemistry
utslib.for	0403 Geology
utslib.for	0404 Geophysics
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-25T04:01:56Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	58
utslib.citation.issue	6

Abstract:

The application of artificial intelligence (AI) and big data in geohazard investigations has gained popularity due to the development of machine learning algorithms and data collection methods. Previous studies have compared and applied various machine learning-based methods, such as conventional machine learning, deep learning, and transfer learning in different areas. This special issue provides state-of-the-art information on the use of AI in geotechnical research, particularly in the Three Gorges Reservoir (TGR) area and adjoining regions. The aim of this volume is to serve as a reference for future researchers interested in exploring the potential of AI in geohazard investigations. It is hoped that this special issue will contribute to the development of guidelines for enhancing the application of AI and big data in geotechnical research, thereby improving our understanding of geological terrains and their associated hazards.

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http://hdl.handle.net/10453/177087