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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| Published in | The Artificial intelligence review Vol. 54; no. 8; pp. 5633 - 5673 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.12.2021
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Zhang, Wengang Chen, Yumin Li, Hongrui Ding, Xuanming Li, Yongqin Liu, Hanlong |
| Author_xml | – sequence: 1 givenname: Wengang surname: Zhang fullname: Zhang, Wengang email: zhangwg@cqu.edu.cn organization: Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, School of Civil Engineering, Chongqing University – sequence: 2 givenname: Hongrui surname: Li fullname: Li, Hongrui organization: Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, School of Civil Engineering, Chongqing University – sequence: 3 givenname: Yongqin surname: Li fullname: Li, Yongqin organization: Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, School of Civil Engineering, Chongqing University – sequence: 4 givenname: Hanlong surname: Liu fullname: Liu, Hanlong organization: Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, School of Civil Engineering, Chongqing University – sequence: 5 givenname: Yumin surname: Chen fullname: Chen, Yumin organization: College of Civil and Transportation Engineering, Hohai University – sequence: 6 givenname: Xuanming surname: Ding fullname: Ding, Xuanming organization: Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, School of Civil Engineering, Chongqing University |
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| SubjectTerms | Algorithms Artificial Intelligence Artificial neural networks Computational linguistics Computer Science Data mining Deep learning Engineering Generative adversarial networks Geotechnical engineering Geotechnology Language processing Machine learning Natural language interfaces Neural networks Recurrent neural networks |
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| Title | Application of deep learning algorithms in geotechnical engineering: a short critical review |
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