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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(Adapted from Goodfellow et al. 2016)
(Adapted from Favio Vázquez; https://www.google.com)
(Source: Web of Science; literature search last updated in November 2020)
(Adapted from Shrestha and Mahmood 2019)
(Adapted from Yang et al. 2019)
(Adapted from Yuan et al. 2019)
(Adapted from Azevedo et al. 2020)
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Acknowledgements
The authors are grateful to the financial supports from National Key R&D Program of China (Project No. 2019YFC1509605), Program of Distinguished Young Scholars, Natural Science Foundation of Chongqing, China (cstc2020jcyj-jq0087) and Chongqing Construction Science and Technology Plan Project (No. 2019-0045).
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Zhang, W., Li, H., Li, Y. et al. Application of deep learning algorithms in geotechnical engineering: a short critical review. Artif Intell Rev 54, 5633–5673 (2021). https://doi.org/10.1007/s10462-021-09967-1
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DOI: https://doi.org/10.1007/s10462-021-09967-1