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.

随着大数据时代的到来，深度学习（DL）已成为人工智能（AI）领域中必不可少的研究主题。与传统的机器学习（ML）方法相比，DL算法具有强大的特征学习和表达能力，从而吸引了来自不同领域的全球研究人员，其应用越来越广泛。此外，在地质工程领域中，DL已经在各种研究主题中被广泛采用，期望对其进行总结的全面综述。因此，本研究介绍了DL在岩土工程中的实践状况，并描述了已发表论文的统计趋势。四种主要算法，包括前馈神经（FNN），递归神经网络（RNN），阐述了卷积神经网络（CNN）和生成对抗网络（GAN）及其在岩土工程中的应用。此外，还编写了一份详尽的摘要，其中包含已发表的文献，相应的参考案例，采用的DL算法以及相关的岩土工程主题。此外，提出并讨论了地下工程在岩土工程中的未来发展所面临的挑战和前景。