A review of the application of artificial intelligence in deep excavation engineering

ZHOU Runsheng; XU Ming; SONG Erxiang

doi:10.11959/j.issn.2096-6652.202540

您当前的位置：

首页 >

文章列表页 >

A review of the application of artificial intelligence in deep excavation engineering

Surveys and Prospectives | 更新时间：2026-03-18

- A review of the application of artificial intelligence in deep excavation engineering
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 4, Pages: 412-432(2025)
- 作者机构：
  
  清华大学土木工程系，北京 100084
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2024YFB2605600);The National Natural Science Foundation of China(42577179)
- DOI：10.11959/j.issn.2096-6652.202540
  CLC： TU46+3
- Received：21 June 2025，
  
  Revised：2025-10-29，
  
  Accepted：04 November 2025，
  
  Published：15 December 2025
- 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

WANG W D, XU Z H, LI Q. Design and construction of deep excavations in Shanghai, China[J]. Geotechnical Research, 2018, 5(3): 143-161.

张绪虎, 徐明, 宋二祥, 等. 北京城市副中心站综合交通枢纽深大基坑两级反压土支护数值分析[J]. 工程力学, 2024: 10.6052/j.issn.1000-4750.2024.04.0297.

ZHANG X H, XU M, SONG E X, et al. Numerical analysis for two-stage earth Berm support of a deep excavation in Beijing sub-central station[J]. Engineering Mechanics, 2024: 10.6052/j.issn.1000-4750.2024.04.0297.

刘国彬, 王卫东. 基坑工程手册. 2[M]. 北京: 中国建筑工业出版社, 2009.

LIU G B, WANG W D. Handbook of deep excavations. Volume 2[M]. Beijing: China Architecture and Building Press, 2009.

宋二祥, 付浩, 李贤杰. 基坑坑底抗隆起稳定安全系数计算方法改进研究[J]. 土木工程学报, 2021, 54(3): 109-118.

SONG E X, FU H, LI X J. Improvement of calculation method for safety factor of base heave stability of deep excavation[J]. China Civil Engineering Journal, 2021, 54(3): 109-118.

QIU T, CHEN X S, SU DONG, et al. Full-scale experiments and modeling of precast two-wall-in-one diaphragm wall components for oceanic artificial cities[J]. Ocean Engineering, 2023, 288: 116074.

THAYANAN B, YADANA A A, VIROON K, et al. Diaphragm wall lateral movement in deep excavations in Bangkok clays: impacts and influencing factors[J]. Canadian Geotechnical Journal, 2024, cgj-2024-0058.

ZHOU W X, XU M. DEM analysis of the influence of particle shape on the small-strain stiffness of granular materials[J]. Computers and Geotechnics, 2025, 179: 106996.

PHOON K K, CHING J Y, CAO Z J. Unpacking data-centric geotechnics[J]. Underground Space, 2022, 7(6): 967-989.

YU O, YUSUKE H. Challenges in geotechnical design revealed by reliability assessment: review and future perspectives[J]. Soils and Foundations, 2022, 62: 101129.

蒋艳. 人工智能发展报告(2023~2024)[M]. 北京: 电子工业出版社, 2024.

JIANG Y. Annual report on the development of artificial intelligence(2023—2024)[M]. Beijing: Publishing House of Electronics Industry, 2024.

刘泽禹, 张慧, 李浥东. 面向自动驾驶的感知决策一体化综述[J]. 智能科学与技术学报, 2025, 7(1): 4-20.

LIU Z Y, ZHANG H, LI Y D. A review of integrated perception and decision-making for autonomous driving[J]. Chinese Journal of Intelligent Science and Technology, 2025, 7(1): 4-20.

陈天炎, 韩泽明, 黄允浒, 等. 基于视觉的列车轨道缺陷检测综述[J]. 智能科学与技术学报, 2024, 6(3): 367-380.

CHEN T Y, HAN Z M, HUANG Y H, et al. Survey on vision-based railway track defect detection[J]. Chinese Journal of Intelligent Science and Technology, 2024, 6(3): 367-380.

杜倩龙, 宗成庆, 苏克毅. 利用上下文相似度增强词对齐效果的自然语言推理方法[J]. 智能科学与技术学报, 2020, 2(1): 26-35.

DU Q L, ZONG C Q, SU K Y. Enhancing alignment with context similarity for natural language inference[J]. Chinese journal of intelligent science and technology, 2020, 2(1): 26-35.

BAGHBANI A, CHOUDHURY T, COSTA S, et al. Application of artificial intelligence in geotechnical engineering: a state-of-the-art review[J]. Earth-Science Reviews, 2022, 228: 103991.

陈湘生, 洪成雨, 苏栋. 智能岩土工程初探[J]. 岩土工程学报, 2022, 44(12): 2151-2159.

CHEN X S, HONG C Y, SU D. Intelligent geotechnical engineering[J]. Chinese Journal of Geotechnical Engineering, 2022, 44(12): 2151-2159.

HOLLAND J. Adaptation in natural and artificial systems: an introductory analysis with application to biology, control, and artificial intelligence[M]. MA: MIT Press, 1992.

陈昌富, 吴子儒, 曹佳, 等. 水泥土墙支护结构遗传进化优化设计方法[J]. 岩土工程学报, 2005, 27(2): 224-229.

CHEN C F, WU Z R, CAO J, et al. Gas-based optimization design method of cement-soil retaining wall[J]. Chinese Journal of Geotechnical Engineering, 2005, 27(2): 224-229.

BHATTACHARYA B, SOLOMATINE D P. Machine learning in soil classification[J]. Neural Networks, 2006, 19(2): 186-195.

SMIRNOFF A, BOISVERT E, PARADIS S J. Support vector machine for 3D modelling from sparse geological information of various origins[J].Computers & Geosciences, 2008, 34(2):127-143.

KHAJEHZADEH M, TAHA R, EL-SHAFIE A, et al. Economic design of retaining wall using particle swarm optimization with passive congregation[J]. Australian Journal of Basic and Applied Sciences, 2010, 4(11): 5500-5507.

HASHASH Y M A, LEVASSEUR S, OSOULI A, et al. Comparison of two inverse analysis techniques for learning deep excavation response[J]. Computers and Geotechnics, 2010, 37(3): 323-333.

曹正. 基于BP神经网络的基坑土钉支护结构优化设计[J]. 公路交通科技, 2014, 10(5): 45-47.

CAO Z. Optimization design of soil nails for foundation pit based on BP neural network[J]. Journal of highway and transportation research and development, 2014, 10(5): 45-47.

左自波, 黄玉林, 吴小建. 基于智能优化算法的深大基坑施工反分析[J]. 岩土工程学报, 2017, 39(z2): 128-131.

ZUO Z B, HUANG Y L, WU X J, et al. Back analysis of construction of large deep excavations using intelligent optimization algorithm[J]. Chinese Journal of Geotechnical Engineering, 2017, 39(z2): 128-131.

SHINYA I, INTUI S, JOTISANKASA A, et al. Artificial intelligence system for supporting soil classification[J]. Results in Engineering, 2020, 8: 100188.

洪宇超, 钱建固, 叶源新, 等. 基于时空关联特征的CNN-LSTM模型在基坑工程变形预测中的应用[J]. 岩土工程学报, 2021, 43(z2): 108-111.

HONG Y C, QIAN J G, YE Y X, et al. Application of CNN-LSTM model based on spatiotemporal correlation characteristics in deformation prediction of excavation engineering[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(z2): 108-111.

LI X, PAN Y, CHEN J, et al. Dynamic and explainable deep learning-based risk prediction on adjacent building induced by deep excavation[J]. Tunnelling and underground space technology, 2023, 140: 105243.

SALIMA A, MOHAMED S, LABIB T S, et al. LSTM-based prediction of active earth pressure coefficient against rigid retaining walls[C]//2024 8th International Conference on Image and Signal Processing and their Applications. Biskra:IEEE Press, 2024: 1-8.

LYU B R, WANG Y, SHI C. Multi-scale generative adversarial networks (GAN) for generation of three-dimensional subsurface geological models from limited boreholes and prior geological knowledge[J]. Computers and Geotechnics, 2024, 170: 106336.

周润生, 徐明, 周文轩. 基于GAN网络的地下连续墙加内支撑智能设计算法[J]. 地下空间与工程学报, 2025: 0856.002.

ZHOU R S, XU M, ZHOU W X. A GAN-based intelligent design algorithm for diaphragm wall with inner supports[J]. Chinese Journal of Underground Space and Engineering, 2025: 0856.002.

HANG Z Q, TAO X, CHEN J P, et al. A 3D geological modeling method using the Transformer model: a solution for sparse borehole data[J]. Minerals, 2025, 15(3): 301.

DENG Z P, CAO M Y, RAI L, et al. A two-stage classification method for borehole-wall images with support vector machine[J]. PloS one, 2018, 13(6): e0199749.

LI L Z, ISKANDER M. Classification of sand using deep learning[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2023, 149(11): 04023103.

ZHAN L T, GUO Q M, CHEN Y M, et al. An efficient classification system for excavated soils using soil image deep learning and TDR cone penetration test[J]. Computers and Geotechnics 2023, 155: 105207.

刘华吉, 孙红林, 张占荣, 等. 基于随钻参数的砂岩与砂质泥岩地层分界面智能识[J]. 隧道建设, 2023, 43(S1): 304-312.

LIU H J, SUN H L, ZHANG Z R, et al. Intelligent identification of sandstone-sandy mudstone interface based on drilling parameters[J]. Tunnel Construction, 2023, 43(S1): 304-312.

TSE K K C, WONG L N Y, CHEUNG S H. Advanced classification of drill core rock type and weathering grade using detection transformer-based artificial intelligence techniques[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2025, 17(7): 4036-4045.

中华人民共和国建设部. 岩土工程勘察规范(GB 50021—2001)[S]. 北京: 中国建筑工业出版社, 2002.

Ministry of Construction of the People's Republic of China. Geotechnical Investigation Code (GB 50021—2001)[S]. Beijing: China Architecture and Building Press, 2002.

上海市城市规划设计研究院. 上海市工程建设规范岩土工程勘察规范(DGJ 08-37—2012)[S]. 北京: 中国计划出版社, 2012.

Shanghai Urban Planning and Design Research Institute. Shanghai engineering construction standard for geotechnical investigation (DGJ 08-37—2012) [S]. Beijing: China Planning Press, 2012.

GUAN Z, WANG Y, PHOON, K K. Dictionary learning of spatial variability at a specific site using data from other sites[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2024, 150(9): 04024072.

KIM H S, JI Y. Three-dimensional geotechnical-layer mapping in Seoul using borehole database and deep neural network-based model[J]. Engineering Geology, 2022, 297: 106489.

PUNTHIN P, TANAPALUNGKORN W, LIKITLERSUANG S. Enhancing 3D geological and geotechnical engineering model of Bangkok subsoil using optimal deep neural network models[J]. Environmental Earth Sciences, 2024, 83(22): 644.

潘秋景, 孙广灿, 蔡永敏, 等. 深圳土工参数数据库及基于生成对抗网络的多元参数分布预测模型研究[J]. 岩土力学, 2025, 46(2): 563-572.

PAN Q J, SUN G C, CAI Y M, et al. Shenzhen geotechnical parameter database and multivariate parameter distribution prediction model based on generative adversarial network[J]. Rock and Soil Mechanics, 2025, 46(2): 563-572.

LI H Y, SHI C. Few-shot learning of geological cross-sections from sparse data using large language model[J]. Geodata and AI, 2025, 2: 100010.

程涛, 晏克勤, 董必昌. 基于神经网络的地质勘测反分析研究[J]. 岩土力学, 2007, 28(4): 807-811.

CHENG T, YAN K Q, DONG B C. Research of back analysis of geological examination based on ANN[J]. Rock and Soil Mechanics, 2007, 28(4): 807-811.

王朱贺, 李楠, 张希瑞, 等. 基于机器学习的深基坑三维土层重建[J]. 重庆大学学报, 2021, 44(5): 11.

WANG Z H, LI N, ZHANG X R. 3D soil layer reconstruction of deep foundation pit based on machine learning[J]. Journal of Chongqing University, 2021, 44(5): 11.

AZEVEDO L, PANEIRO G, SANTOS A, et al. Generative adversarial network as a stochastic subsurface model reconstruction[J]. Computational Geosciences, 2020, 24: 1673-1692.

GONG X N, ZHANG X C. Excavation collapse of Hangzhou subway station in soft clay and numerical investigation based on orthogonal experiment method[J]. Journal of Zhejiang University Science A, 2012, 13: 760-767.

ENDICOTT J. Case histories of failure of deep excavation examination of where things went wrong: Nicoll highway collapse, singapore[C]//Proceedings of the Seventh International Conference on Case Histories in Geotechnical Engineering. Chicago: Missouri University of Science and Technology, 2013: 1-7.

ZHOU Z P, GOH Y M, SHI Q Q, et al. Data-driven determination of collapse accident patterns for the mitigation of safety risks at metro construction sites[J]. Tunneling and Underground Space Technology. 2022, 127: 104616.

彭军龙, 张学民, 阳军生, 等. 地铁深基坑支护的遗传神经网络位移反分析[J]. 岩土力学, 2007, 28(10): 2118-2122.

PENG J L, ZHANG X M, YANG J S. Displacement back analysis of deep foundation pit for metro based on genetic algorithm and neural network[J]. Rock and Soil Mechanics, 2007, 28(10): 2118-2122.

李步遥, 司马军. 基于MEC-BP神经网络的基坑水平位移反演分析[J]. 铁道科学与工程学报, 2021, 18(7): 1764-1772.

LI B Y, SIMA J. Horizontal displacement back-analysis for deep excavation using MEC-BP neural network[J]. Journal of Railway Science and Engineering, 2021, 18(7): 1764-1772.

XIAO J F, WANG Q W, ZHANG Q W, et al. Multiple parameters determination method of hardening soil model based on particle swarm optimization[J]. Advances in Civil Engineering, 2024, 1: 5561981.

HE P, GUO H J, DI H G, et al. Application of soil parameter back analysis method based on MLAPSO in staged excavation deformation prediction[J]. Expert Systems with Applications, 2026, 296: 129244.

程秋实, 杨志双, 秦胜伍, 等. 基于PSO-MLSSVR的土体参数反演方法在深基坑工程中的应用[J]. 工程地质学报, 2022, 30(2): 520-532.

CHENG Q S, YANG Z S, QIN S W. Application of soil parameters inversion based on PSO-MLSSVR in deep foundation pit engineering[J]. Journal of Engineering Geology, 2022, 30(2): 520-532.

邓子胜. 基于径向基神经网络的深基坑非线性位移反分析[J]. 岩土工程学报, 2005, 27(5): 554-557.

DENG Z S. Nonlinear displacement back-analysis for deep excavation based on radial basis neural network[J]. Chinese Journal of Underground Space and Engineering, 2005, 27(5): 554-557.

冯夏庭, 张治强, 杨成祥. 位移反分析的进化神经网络方法研究[J]. 岩石力学与工程学报, 1999, 18(5): 529-529.

FENG X T, ZHANG Z Q, YANG C X. Study on genetic-neural network method of displacement back analysis[J]. Chinese Journal of Rock Mechanics and Engineering, 1999, 18(5): 529-529.

GOH A T C, WONG K S, BROMS B B. Estimation of lateral wall movements in braced excavations using neural networks[J]. Canadian Geotechnical Journal, 1995, 32(6): 1059-1064.

孟凡丽, 王小刀, 王逸晨, 等. 基于TSNE降维算法的基坑水平位移预测研究[J]. 浙江工业大学学报, 2023, 51(4): 403-411.

MENG F L, WANG X D, WANG Y C. Prediction of horizontal displacement of foundation pit by TSNE dimensionality reduction algorithm[J]. Journal of Zhejiang University of Technology, 2023, 51(4): 403-411.

张绪虎. 基于深度学习反分析的反压土支护基坑变形预测及设计[D]. 北京: 清华大学, 2023: 7-41.

ZHANG X H. Deformation prediction and design of excavation supported by earth berm based on deep learning inverse analysis[D]. Beijing:Tsinghua University, 2023: 7-41.

LIU W Y, TONG L Y, LI H J, et al. Multi-parameter intelligent inverse analysis of a deep excavation considering path-dependent behavior of soils. Computers and Geotechnics[J ] . 2024, 174 : 106597.

ZHANG R, WU C, GOH A T C, et al. Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning[J]. Geoscience Frontiers, 2021, 12(1): 365-373.

ZHOU D, WU H, MAI J, et al. The research on distributed parallel optimal design system of foundation pit engineering[C]//Proceedings of 2010 International Conference on Computational Intelligence and Software Engineering. Wuhan: IEEE Press, 2010: 1-4.

肖专文, 龚晓南, 谭昌明. 基坑土钉支护优化设计的遗传算法[J]. 土木工程学报, 1999, 32(3): 73-80.

XIAO Z W, GONG X N, TAN C M. Genetic algorithm for optimal design of soil nailing for deep excavations[J]. China Civil Engineering Journal, 1999, 32(3): 73-80.

陈昌富, 吴子儒, 龚晓南. 复合形模拟退火算法及其在水泥土墙优化设计中的应用[J]. 岩土力学, 2007, 28(12): 2543-2548.

CHEN C F, WU Z R, GONG X N. Complex simulated annealing and its application to optimization design of cement-soil retaining walls[J]. Rock and Soil Mechanics, 2007, 28(12): 2543-2548.

陈朝军. 蚁群算法与基坑土钉支护优化设计[D]. 辽宁: 辽宁科技大学, 2007: 38-50.

CHEN C J. Ant colony algorithm and optimization design of soil nails for foundation pit[D]. Liaoning:Liaoning University of Science and Technology, 2007: 38-50.

吴恒, 李陶深, 韦日钰, 等. 遗传算法在深基坑支护工程优化设计中的应用[J]. 广西大学学报(自然科学版), 2000, 25(1): 1-4.

WU H, LI T S, WEI R Y, et al. Application of genetic algorithm in optimization design of deep excavation support[J]. Journal of Guangxi University (Nat Sci Ed), 2000, 25(1): 1-4.

TAIYARI F, HAJIHASSANI M, KHARGHANI M. Efficiency of the evolutionary methods on the optimal design of secant pile retaining systems in a deep excavation[J]. Neural Computing and Applications, 2022, 34(22): 20313-20325.

KASHANI A R, CAMP C V, AZIZI K, et al. Multi‐objective optimization of mechanically stabilized earth retaining wall using evolutionary algorithms[J]. International journal for numerical and analytical methods in geomechanics, 2022, 46(8): 1433-1465.

KHAJEHZADEH M, TAHA M R, EL-SHAFIE A, et al. Modified particle swarm optimization for optimum design of spread footing and retaining wall[J]. Journal of Zhejiang University-Science A, 2011(12): 415-427.

TAIYARI F, KHARGHANI M, HAJIHASSANI M. Optimal design of pile wall retaining system during deep excavation using swarm intelligence technique[J]. Structures, 2020, 28: 1991-1999.

KASHANI A R, GANDOMI A H, AZIZI, et al. Multi-objective optimization of reinforced concrete cantilever retaining wall: a comparative study[J]. Structural and Multidisciplinary Optimization, 2022, 65(9): 262.

HONG L, WANG X Y, ZHANG W G, et al. System reliability-based robust design of deep foundation pit considering multiple failure modes[J]. Geoscience Frontiers, 2024, 15(2): 101761.

LI H, WANG X Y, ZHANG W G, et al. System reliability-based robust design of deep foundation pit considering multiple failure modes[J]. Geoscience Frontiers, 2024, 15(2): 101761.

顾宝和. 浅谈岩土工程的专业特点[J]. 岩土工程界, 2007, 1: 19-23.

GU B H. A brief discussion on the professional characteristics of geotechnical engineering[J]. Geotechnical Engineering World, 2007, 1: 19-23.

HUANG W, ZHENG H. Architectural drawings recognition and generation through machine learning[C]//Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA). Fargo: Association for Computer Aided Design in Architecture (ACADIA), 2018: 156-165

NAUATA N, CHANG K H, CHENG C Y, et al. House-GAN: relational generative adversarial networks for graph-constrained house layout generation[C]//European Conference on Computer Vision 2020. Online: Springer, 2020: 162-177 .

LIAO W J, LU X Z, HUANG Y L, et al. Automated structural design of shear wall residential buildings using generative adversarial networks[J]. Automation in construction, 2021, 132:103931.

LU X Z, LIAO W J, ZHANG Y, et al. Intelligent structural design of shear wall residence using physics‐enhanced generative adversarial networks[J]. Earthquake engineering and structural dynamics, 2022, 51(7): 1657-1676.

LIAO W J, HUANG Y L, ZHENG Z, et al. Intelligent generative structural design method for shear wall building based on "fused-text-image-to-image" generative adversarial networks[J]. Expert Systems with Applications, 2022, 210:118530.

ZHOU Y, LENG H, MENG S Q, et al. StructDiffusion: end-to-end intelligent shear wall structure layout generation and analysis using diffusion model[J]. Engineering Structures, 2024, 309: 118068.

XU H R, ZHANG N, YIN Z Y, et al. GeoLLM: a specialized large language model framework for intelligent geotechnical design[J]. Computers and Geotechnics, 2025, 177: 106849.

孙钧, 王东栋. 地铁施工变形预测与控制的智能方法[J]. 施工技术, 2009, 38(1): 3-9.

SUN J, WANG D D. Intelligent prediction and control method of deformation in metro construction[J]. Construction Technology, 2009, 38(1): 3-9.

KUANG H W, AI Z Y, GU G L, et al. A dynamic prediction model for maximum lateral displacement of foundation pit enclosure structure using multi-source fusion method[J]. Computers and Geotechnics, 2025, 187: 107468.

FAN H Y, LI L P, ZHOU S, et al. Prediction and risk assessment of lateral collapse in deep foundation pits using machine learning[J]. Automation in Construction, 2025, 171: 106011.

黄达, 朱双中, 宋宜祥. 基于LSTM神经网络的基坑工程智能预警系统研发与应用[J]. 工程地质学报, 2024, 32(2): 667-677.

HUANG D, ZHU S Z, SONG Y X. Development and application of intelligent warning system for foundation pit based on LSTM[J]. Journal of Engineering Geology, 2024, 32(2): 667-677.

WEI X, CHENG S, CHEN R, et al. ANN deformation prediction model for deep foundation pit with considering the influence of rainfall[J]. Scientific Reports, 2023, 13: 22664.

刘俊城, 谭勇, 张生杰. 地铁车站深基坑开挖变形智能多步预测方法[J]. 上海交通大学学报, 2024, 58(7): 1108-1117.

LIU J C, TAN Y, ZHANG S J. Multi-step prediction of excavation deformation of subway station based on intelligent algorithm[J]. Journal of Shanghai Jiao Tong University, 2024, 58(7): 1108-1117.

ZHOU X J, PAN Y, QIN J J, et al. Spatio-temporal prediction of deep excavation-induced ground settlement: a hybrid graphical network approach considering causality[J]. Tunneling and Underground Space Technology, 2024, 146: 105605.

GAO Y Y, XIAO Z Y, GONG Z Q, et al. Spatiotemporal deformation prediction model for retaining structures integrating ConvGRU and cross-attention mechanism[J]. Buildings, 2025, 15(14): 2537.

李柏佚, 王桂林, 袁军. 基于关联监测点数据的非线性变形预测模型[J]. 振动与冲击, 2021, 40(8): 124-130.

LI B Y, WANG G L, YUAN J. A nonlinear deformation prediction model based on associated monitoring point data[J]. Journal of vibration and shock, 2021, 40(8): 124-130.

YANG C, WANG C, WU B, et al. Settlement estimation during foundation excavation using pattern analysis and explainable AI modeling[J]. Automation in Construction, 2024, 166: 105651.

QI Q L, TAO F. Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison[J]. IEEE Access, 2018, 6: 3585-3593.

ZHANG J Q, JIANG S H. Digital twin for sustainable development in building automation[J]. Engineering, Construction and Architectural Management, 2024, 1024.

ZHANG X Y, ZHENG J H, LI P X, et al. A review of building digital twins: framework and enabling technologies[J]. Journal of Building Engineering, 2025, 111: 113117.

WANG X, PAN Y, CHEN J J. Digital twin with data-mechanism-fused model for smart excavation management[J]. Automation in Construction, 2024, 168: 105749.

WANG X, PAN Y, CHEN J J. Digital twin with uncertainty‐informed deep learning for prompt quantitative risk assessment of deep excavation[J]. Computer-Aided Civil and Infrastructure Engineering, 2025, 70049.

Views

239

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

On intelligence: the origin and the goal

LinYun: a domain-specific large language model for intelligent question answering and decision support in forestry

“Breathing sovereignty” in new AI era: global technological competition with no genesis traps

RadiFlow: AI agents reconstructing radiology workflow

Cerberus: a cross-site social bot detection system based on deep learning

Related Author

Wang Fei-Yue

Zhao Weikang

Wang Haoyu

Hua Jing

Liu Yaobing

Wang Xiujuan

Kang Mengzhen

GE Jingwei

Related Institution

Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macao

The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences

Faculty of Innovation Engineering, Macau University of Science and Technology, Macao

Jiangheng Digital Intelligence Ecological Technology Co., Ltd.

The School of Artificial Intelligence, University of Chinese Academy of Sciences

Postal code：100079
Tel：（010）53879206 Email：cjist@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：161982 Visits Today：145

⁰