智能岩土工程初探

陈湘生; 洪成雨; 苏栋

doi:10.11779/CJGE202212001

智能岩土工程初探 English Version

陈湘生^{1, 2, 3, 4,},
洪成雨^{1, 2, 3, 4, ,},
苏栋^{1, 2, 3, 4}

1.
深圳大学土木与交通工程学院, 广东深圳 518060
2.
深圳市地铁地下车站绿色高效智能建造重点实验室(深圳大学), 广东深圳 518060
3.
深圳大学未来地下城市研究院, 广东深圳 518060
4.
滨海城市韧性基础设施教育部重点实验室(深圳大学), 广东深圳 518060

基金项目:

国家自然科学基金重大项目 52090084

国家自然科学基金重点项目 51938008

详细信息

作者简介:
陈湘生(1956—)，湖南湘潭人，博士，教授，中国工程院院士，主要从事隧道与地下工程、城市轨道交通工程、智能岩土工程、特殊岩土工程、人工冻土力学、建井工程等研究工作。E-mail: xschen@szu.edu.cn

通讯作者:
洪成雨, E-mail: cyhong@szu.edu.cn

中图分类号: TU441
计量
- 文章访问数: 1060
- HTML全文浏览量: 296
- PDF下载量: 371
出版历程
- 收稿日期: 2021-09-21
- 网络出版日期: 2022-12-13
- 刊出日期: 2022-11-30

Intelligent geotechnical engineering

CHEN Xiang-sheng^{1, 2, 3, 4,},
HONG Cheng-yu^{1, 2, 3, 4, ,},
SU Dong^{1, 2, 3, 4}

1.
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
2.
Shenzhen Key Laboratory of Green, Efficient and Intelligent Construction of Underground Metro Station, Shenzhen 518060, China
3.
Underground Polis Academy, Shenzhen University, Shenzhen 518060, China
4.
Key Laboratory for Resilient Infrastructures of Coastal Cities (Shenzhen University), MOE, Shenzhen 518060, China

摘要

摘要: 以物联网、现代通讯、大数据、人工智能等技术为核心的第四次工业革命成为了众多研究领域智能化升级的平台，新时代条件下传统岩土工程研究遇到了前所未有的机遇和挑战，岩土工程与最新的信息技术、计算机科学技术相互融合，如建筑信息模型、物联网、人工智能、深度学习、增强现实等可实现岩土工程的智能化转型。研究初步构建了“智能岩土工程”的知识图谱，探索了相应的实现路径，阐述了基于新技术的三维地质建模–物联网–深度学习–扩展现实的岩土工程智能化转型方法；介绍了建筑信息模型（BIM）与地理信息系统（GIS）一体化的三维地质建模、“端–边–云–网”的技术架构、以主动伺服加载系统为代表的岩土工程（深基坑工程）风险主动控制系统；阐明了物联网传感器技术在岩土工程领域的应用情况、虚拟现实与增强现实技术在岩土工程领域的应用现状；分析了人工智能（深度学习）在岩土工程风险预测预警方面的关键作用；构建了未来智能岩土工程的知识图谱，为拟从事智能岩土工程相关的研究人员提供借鉴。
- 智能岩土工程 /
- 三维地质建模 /
- 物联网 /
- 人工智能 /
- 深度学习 /
- 扩展现实
Abstract: The fourth industrial revolution based on the core technologies of Internet of Things (IoT), modern communication, big data and artificial intelligence (AI) is an upgrading platform for many different research fields. The traditional geotechnical engineering has great opportunities and grand challenges in this new era. Integration of the geotechnical engineering, innovative information technology and computer science technology such as building information modelling (BIM), IoT, AI, deep learning and argument reality can be used to realize intelligent transformation of the traditional geotechnical engineering. The knowledge mapping of the intelligent geotechnical engineering is preliminarily established, and the relevant realization paths are investigated. The transformation method for the intelligent geotechnical engineering "3D geological modelling-IoT-deep learning-extended reality" based on the innovative technologies is depicted. 3D geological modelling using the fusion of BIM and the geographic information system, the technological frame of "end-edge-cloud-network" and the active risk control for geotechnical engineering (deep excavation engineering) based on the active servo-loading system are introduced. The application status of the IoT sensoring technology visual reality and argument reality in the geotechnical engineering is introduced. The key role of AI (deep learning) in the geotechnical engineering for monitoring and early warning is analyzed. The knowledge mapping of future development of the intelligent geotechnical engineering is proposed, providing advice and guidance for the relevant researchers in the geotechnical engineering.
- intelligent geotechnical engineering /
- 3D geological modelling /
- Internet of Things /
- artificial intelligence /
- deep learning /
- extended reality

HTML全文

图 1 智能岩土工程关键词关联性分析

Figure 1. Mapping analysis of co-occurrence of keywords related to intelligent geotechnical engineering

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图 2 岩土监测MEMS传感器^[19-20]

Figure 2. MEMS sensors for geotechnical monitoring

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图 3 隧道图像测量设备^[21]

Figure 3. Measurement devices for tunnel topography

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图 4 沿激光扫描仪视线拍摄的地面混合像素横截面^[24]

Figure 4. Example of automatically-detected mixed pixels in a single scan station

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图 5 激光扫描仪获取的隧道点云图与处理方法^[28]

Figure 5. Obtained point cloud of a tunnel

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图 6 沉降预测与建筑物损坏评估的软件SMART^[43]

Figure 6. Settlement prediction and building damage assessment- based software SMART^[43]

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图 7 微软vGIS与Trimble Site Vision屏幕截图 ^[48]

Figure 7. Screens of vGIS from Microsoft company (upper figure) and Trimble Site Vision (lower figure) ^[48]

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图 8 深基坑主动伺服加载系统工作原理^[50]

Figure 8. Operating principle of servo-struts in a deep excavation^[50]

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图 9 智能岩土工程框架知识图谱

Figure 9. Knowledge mapping of intelligent geotechnical engineering

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