Application of deep learning algorithms in geotechnical engineering: a short critical review

With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful feature learning and expression capabilities compared with the traditional machine learning (ML) methods, which att...

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Vydané v:The Artificial intelligence review Ročník 54; číslo 8; s. 5633 - 5673
Hlavní autori: Zhang, Wengang, Li, Hongrui, Li, Yongqin, Liu, Hanlong, Chen, Yumin, Ding, Xuanming
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.12.2021
Springer
Springer Nature B.V
Predmet:
Algorithms
Application
Artificial Intelligence
Artificial neural networks
Big Data
Computational linguistics
Computer Science
Data mining
Deep learning
Engineering
Generative adversarial networks
Geotechnical engineering
Geotechnology
Language processing
Machine learning
Natural language interfaces
Networks
Neural networks
Recurrent
Recurrent neural networks
Research subjects
Deep learning
Geotechnical engineering
Big data
Neural networks
ISSN:0269-2821, 1573-7462
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Popis
Shrnutí:With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful feature learning and expression capabilities compared with the traditional machine learning (ML) methods, which attracts worldwide researchers from different fields to its increasingly wide applications. Furthermore, in the field of geochnical engineering, DL has been widely adopted in various research topics, a comprehensive review summarizing its application is desirable. Consequently, this study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers. Four major algorithms, including feedforward neural (FNN), recurrent neural network (RNN), convolutional neural network (CNN) and generative adversarial network (GAN) along with their geotechnical applications were elaborated. In addition, a thorough summary containing pubilished literatures, the corresponding reference cases, the adopted DL algorithms as well as the related geotechnical topics was compiled. Furthermore, the challenges and perspectives of future development of DL in geotechnical engineering were presented and discussed.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-021-09967-1