Application of deep learning algorithms in geotechnical engineering: a short critical review
Citation
Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y., Ding, X. (2021). Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review, 54(8): 5633-5673. Link to paper
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 geotechnical 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 network (FNN), recurrent neural network (RNN), convolutional neural network (CNN) and generative adversarial network (GAN) along with their geotechnical applications were elaborated. In the review, all relevant references were collected and categorized based on publication year, journal, DL algorithm and application, which provides insight for the future development trend of DL in geotechnical engineering. Particularly, typical applications including foundation engineering, tunneling and underground engineering, slope engineering, earth dam and rock engineering, geoenvironmental engineering were addressed in detail, which could inspire readers with different research interests. Moreover, the review of state of the art DL algorithms in geotechnical engineering can serve as a benchmark for upcoming studies and help to promote future progress.