人工智能在基坑工程中的应用综述

周润生; 徐明; 宋二祥

doi:10.11959/j.issn.2096-6652.202540

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人工智能在基坑工程中的应用综述

综述与展望 | 更新时间：2026-03-18

- 人工智能在基坑工程中的应用综述
- A review of the application of artificial intelligence in deep excavation engineering
- 智能科学与技术学报 2025年7卷第4期页码：412-432
- 作者机构：
  
  清华大学土木工程系，北京 100084
- 作者简介：
  
  [ "周润生（2000- ），男，清华大学土木工程系博士生，主要研究方向为基坑工程智能化理论和应用。" ]
  [ "徐明（1974- ），男，清华大学土木工程系副教授、博士生导师，地下工程研究所副所长，主要研究方向为岩土力学试验研究及理论分析等。" ]
  [ "宋二祥（1958- ），男，清华大学土木工程系教授、博士生导师，主要研究方向为岩土工程数值方法、深基坑支护设计分析方法、地基基础及地下结构静、动力分析等。" ]
- 基金信息：
  
  国家重点研发计划项目(2024YFB2605600);国家自然科学基金项目(42577179)
- DOI：10.11959/j.issn.2096-6652.202540
  中图分类号： TU46+3
- 收稿：2025-06-21，
  
  修回：2025-10-29，
  
  录用：2025-11-04，
  
  纸质出版：2025-12-15
- 稿件说明：
移动端阅览
周润生,徐明,宋二祥.人工智能在基坑工程中的应用综述[J].智能科学与技术学报,2025,07(04):412-432.

ZHOU Runsheng,XU Ming,SONG Erxiang.A review of the application of artificial intelligence in deep excavation engineering[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):412-432.
周润生,徐明,宋二祥.人工智能在基坑工程中的应用综述[J].智能科学与技术学报,2025,07(04):412-432. DOI： 10.11959/j.issn.2096-6652.202540.

ZHOU Runsheng,XU Ming,SONG Erxiang.A review of the application of artificial intelligence in deep excavation engineering[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):412-432. DOI： 10.11959/j.issn.2096-6652.202540.

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。

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 multi-point 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.

关键词

Keywords

references

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