【摘要】 人工智能（artificial intelligence,AI）与基坑工程的深度融合是推动地下空间智能化开发的关键路径。系统综述了人工智能技术在基坑工程全生命周期中的研究现状与发展趋势。首先，基于基坑工程的关键阶段，梳理了传统人工智能模型、深度学习算法和生成式人工智能模型在基坑工程中的应用脉络。其次，针对人工智能在基坑工程中应用的基本原理、适用性和局限性，从智能勘察、智能设计和智能监测3个方面进行了系统归纳和对比。对于智能勘察，人工智能算法实现了岩土体高精度分类与三维地质模型重建；对于智能设计，人工智能方法通过参数反演、优化及生成式设计提升了支护方案的设计效率与创新性。此外，人工智能还与基坑监测紧密结合，支撑了单测点时序预测、多测点时空关联预测及多源数据融合数字孪生模型的构建。最后，建议人工智能与基坑工程的融合可以向4个方向发展：建立多源综合数据库，研发通用智能算法，构建“数据-理论-经验”融合驱动模型，完善用户生态体系。更多还原

【Abstract】 The integration of artificial intelligence(AI) with deep excavation engineering represents a key path for the intelligent development of underground space. This paper provides a state-of-the-art review of the research status and development trends of AI applications in the full lifecycle of deep excavation engineering. First, based on the key stages of deep excavation, the applications of traditional AI algorithms, deep learning methods, and generative AI models are briefly summarized. Subsequently, it systematically summarizes and compares the fundamental principles, applicability, and limitations of AI in deep excavation engineering from three perspectives: intelligent geological investigation, intelligent design, and intelligent monitoring. For intelligent investigation, AI algorithms have enabled high-precision classification of rock and soil masses and the reconstruction of 3D geological models. For intelligent design, AI methods have enhanced the efficiency and creativity of design schemes through parameter inversion, optimization, and generation. Furthermore, AI is closely integrated with deep excavation monitoring, enabling single-point time-series prediction and multipoint spatiotemporal correlation prediction of deformation, as well as the establishment of a digital twin enhanced by multi-source data fusion. Finally, four directions for the integration of AI and deep excavation engineering are proposed: establishing comprehensive multi-source databases, developing universal intelligent algorithms for deep excavations, constructing models driven by data-theory-experience fused mechanism, and improving the user ecosystem for AI in deep excavations.更多还原