Advanced Search
  • 全国中文核心期刊
  • 中国科技核心期刊
  • 美国工程索引（EI）收录期刊
  • Scopus数据库收录期刊
Volume 44 Issue 12
Turn off MathJax
Article Contents
Abstract
References
CHEN Xiang-sheng, HONG Cheng-yu, SU Dong. Intelligent geotechnical engineering[J]. Chinese Journal of Geotechnical Engineering, 2022, 44(12): 2151-2159. DOI: 10.11779/CJGE202212001
Citation: CHEN Xiang-sheng, HONG Cheng-yu, SU Dong. Intelligent geotechnical engineering[J]. Chinese Journal of Geotechnical Engineering, 2022, 44(12): 2151-2159. DOI: 10.11779/CJGE202212001
shu

Intelligent geotechnical engineering

  • 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

More Information
  • Received Date: September 21, 2021
  • Available Online: December 13, 2022
    Graphical Abstract
    • 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.
    • FullText(HTML)
    • References (52)
  • [1]
    赵艳莉. 基于计算机三维地质模型的岩土工程设计与可视分析——评《岩土工程勘察与设计》[J]. 岩土工程学报, 2019, 41(7): 1381. https://www.cnki.com.cn/Article/CJFDTOTAL-YTGC201907028.htm

    ZHAO Yan-li. Based on the design and visual analysis of geotechnical engineering based on computer three-dimensional geological model[J]. Journal of Geotechnical Engineering, 2019, 41(7): 1381. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTGC201907028.htm
    [2]
    谭顺辉. 隧道掘进机多功能化及智能化的发展与推广[J]. 隧道建设, 2020, 40(9): 1243–1250. https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD202009001.htm

    TAN Shun-hui. Development and application of multi-functional and intelligent tunnel boring machine[J]. Tunnel Construction, 2020, 40(9): 1243–1250. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD202009001.htm
    [3]
    陈祖煜, 赵宇飞, 邹斌, 等. 填筑碾压施工无人驾驶技术的研究与应用[J]. 水利水电技术, 2019, 50(8): 1–7. https://www.cnki.com.cn/Article/CJFDTOTAL-SJWJ201908001.htm

    CHEN Zu-yu, ZHAO Yu-fei, ZOU Bin, et al. Research and application of drive technology of dam filling rolling construction[J]. Water Resources & Hydropower Technology, 2019, 50(8): 1–7. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SJWJ201908001.htm
    [4]
    熊自明, 卢浩, 王明洋, 等. 我国大型岩土工程施工安全风险管理研究进展[J]. 岩土力学, 2018, 39(10): 3703–3716. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201810028.htm

    XIONG Zi-ming, LU Hao, WANG Ming-yang, et al. Research progress on safety risk management for large scale geotechnical engineering construction in China[J]. Rock and Soil Mechanics, 2018, 39(10): 3703–3716. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201810028.htm
    [5]
    戴妙林, 屈佳乐, 刘晓青, 等. 基于GA-BP算法的岩质边坡稳定性和加固效应预测模型及其应用研究[J]. 水利水电技术, 2018, 49(5): 165–171. https://www.cnki.com.cn/Article/CJFDTOTAL-SJWJ201805024.htm

    DAI Miao-lin, QU Jia-le, LIU Xiao-qing, et al. Study on GA-BP hybrid algorithm-based prediction model and its application to rock slope stability and reinforcement effect[J]. Water Resources and Hydropower Engineering, 2018, 49(5): 165–171. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SJWJ201805024.htm
    [6]
    ONSEL I E, CHANG O, MYSIOREK J, et al. Applications of mixed and virtual reality techniques in site characterization[C]// 26th Vancouver Geotechnical Society Symp. Vancouver, 2019.
    [7]
    WANG R X, WANG R, FU P C, et al. Portable interactive visualization of large-scale simulations in geotechnical engineering using Unity3D[J]. Advances in Engineering Software, 2020, 148: 102838. doi: 10.1016/j.advengsoft.2020.102838
    [8]
    VAN ECK N J, WALTMAN L, VOSviewer Manual[M]. Leiden: Univeristeit Leiden, 2011.
    [9]
    韩同春, 林博文, 何露, 等. 基于GIS与数值模拟软件耦合的三维边坡建模方法及其稳定性研究[J]. 岩土力学, 2019, 40(7): 2855–2865. doi: 10.16285/j.rsm.2018.0439

    HAN Tong-chun, LIN Bo-wen, HE Lu, et al. Three-dimensional slope modelling method and its stability based on coupled GIS and numerical simulation software[J]. Rock and Soil Mechanics, 2019, 40(7): 2855–2865. (in Chinese) doi: 10.16285/j.rsm.2018.0439
    [10]
    VALERIA N, ROBERTA V, VITTORIA C A. A new frontier of BIM process: geotechnical BIM[C]// Proceedings of the Ⅻ European Conference on Soil Mechanics and Geotechnical Engineering, Icelandic Geotechnical Society. Reykjavik, 2019.
    [11]
    ZOU Y, KIVINIEMI A, JONES S W. A review of risk management through BIM and BIM-related technologies[J]. Safety Science, 2017, 97: 88–98. doi: 10.1016/j.ssci.2015.12.027
    [12]
    FABOZZI S, BIANCARDO S A, VEROPALUMBO R, et al. I-BIM based approach for geotechnical and numerical modelling of a conventional tunnel excavation[J]. Tunnelling and Underground Space Technology, 2021, 108: 103723. doi: 10.1016/j.tust.2020.103723
    [13]
    HONG C Y, ZHANG Y F, ZHANG M X, et al. Application of FBG sensors for geotechnical health monitoring, a review of sensor design, implementation methods and packaging techniques[J]. Sensors and Actuators A: Physical, 2016, 244: 184–197. doi: 10.1016/j.sna.2016.04.033
    [14]
    ZHANG C C, SHI B, ZHU H H, et al. Toward distributed fiber-optic sensing of subsurface deformation: a theoretical quantification of ground-borehole-cable interaction[J]. Journal of Geophysical Research: Solid Earth, 2020, 125(3): e2019JB018878.
    [15]
    ZHANG C C, ZHU H H, CHEN D D, et al. Feasibility study of anchored fiber-optic strain-sensing arrays for monitoring soil deformation beneath model foundation[J]. Geotechnical Testing Journal, 2019, 42(4): 20170321. doi: 10.1520/GTJ20170321
    [16]
    SHI B, ZHANG D, HONGHU Z, et al. DFOS Applications to Geo-Engineering Monitoring[J]. Photonic Sensors, 2021, 11(2): 158–186. doi: 10.1007/s13320-021-0620-y
    [17]
    CMIELEWSKI B. Use of low-cost MEMS technology in early warning system against landslide threats[J]. Acta Geodynamica et Geomaterialia, 2013: 485–490. doi: 10.13168/AGG.2013.0049
    [18]
    BENNETT V, ABDOUN T, O'MEARA K, et al. Wireless MEMS-based in-place inclinometer-accelerometer array for real-time geotechnical instrumentation[C]//Engineering Geology and Geological Engineering for Sustainable Use of the Earth's Resources, Urbanization and Infrastructure Protection from Geohazards. Springer, 2018.
    [19]
    KIM J, KWON S, PARK S, et al. A MEMS-based commutation module with vibration sensor for wireless sensor network-based tunnel-blasting monitoring[J]. KSCE Journal of Civil Engineering, 2013, 17(7): 1644–1653. doi: 10.1007/s12205-013-0108-4
    [20]
    XU C Y, CHEN J W, ZHU H C, et al. Design and laboratory testing of a MEMS accelerometer array for subsidence monitoring[J]. Review of Scientific Instruments, 2018, 89(8): 085103. doi: 10.1063/1.5036666
    [21]
    GAO Z R, LI F J, LIU Y, et al. Tunnel contour detection during construction based on digital image correlation[J]. Optics and Lasers in Engineering, 2020, 126: 105879. doi: 10.1016/j.optlaseng.2019.105879
    [22]
    SALOMA UY E E. Image processing for geotechnical laboratory measurements[J]. International Journal of Geomate, 2016, 10(22): 1964–1970.
    [23]
    STANIER S A, BLABER J, TAKE W A, et al. Improved image-based deformation measurement for geotechnical applications[J]. Canadian Geotechnical Journal, 2016, 53(5): 727–739. doi: 10.1139/cgj-2015-0253
    [24]
    MILENKOVIĆ M, PFEIFER N, GLIRA P. Applying terrestrial laser scanning for soil surface roughness assessment[J]. Remote Sensing, 2015, 7(2): 2007–2045. doi: 10.3390/rs70202007
    [25]
    MAZZANTI P. Displacement monitoring by terrestrial SAR interferometry for geotechnical purposes[J]. Geotechnical News, 2011, 29(2): 25.
    [26]
    XU H, LI H B, YANG X G, et al. Integration of terrestrial laser scanning and NURBS modeling for the deformation monitoring of an earth-rock dam[J]. Sensors, 2018, 19(1): 22. doi: 10.3390/s19010022
    [27]
    OSKOUIE P, BECERIK-GERBER B, SOIBELMAN L. Automated measurement of highway retaining wall displacements using terrestrial laser scanners[J]. Automation in Construction, 2016, 65: 86–101. doi: 10.1016/j.autcon.2015.12.023
    [28]
    XIE X Y, LU X Z. Development of a 3D modeling algorithm for tunnel deformation monitoring based on terrestrial laser scanning[J]. Underground Space, 2017, 2(1): 16–29. doi: 10.1016/j.undsp.2017.02.001
    [29]
    LI Z H, FANG L Q, SUN X K, et al. 5G IoT-based geohazard monitoring and early warning system and its application[J]. EURASIP Journal on Wireless Communications and Networking, 2021: 160.
    [30]
    HUDSON T S, BAIRD A F, KENDALL J M, et al. Distributed Acoustic Sensing (DAS) for natural microseismicity studies: a case study from Antarctica[J]. Journal of Geophysical Research: Solid Earth, 2021, 126(7): e2020JB021493.
    [31]
    江月新, 黄云龙, 余建军. 基于WiFi通信的矿井监测无线传感器网络研究[J]. 煤炭技术, 2017, 36(6): 278–280. https://www.cnki.com.cn/Article/CJFDTOTAL-MTJS201706106.htm

    JIANG Yue-xin, HUANG Yun-long, YU Jian-jun. Research on mine monitoring wireless sensor network based on WiFi communication[J]. Coal Technology, 2017, 36(6): 278–280. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MTJS201706106.htm
    [32]
    DONG W K, LI W G, SUN Z H, et al. Intrinsic graphene/cement-based sensors with piezoresistivity and superhydrophobicity capacities for smart concrete infrastructure[J]. Automation in Construction, 2022, 133: 103983. doi: 10.1016/j.autcon.2021.103983
    [33]
    DAI Y Y, ZHANG K, MAHARJAN S, et al. Edge intelligence for energy-efficient computation offloading and resource allocation in 5G beyond[J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 12175–12186. doi: 10.1109/TVT.2020.3013990
    [34]
    ARMAGHANI D J, AZIZI A. Empirical, statistical, and intelligent techniques for TBM performance predictionp[J]. Applications of Artificial Intelligence in Tunnelling and Underground Space Technology, Springer, 2021: 17–32.
    [35]
    DING Z, JIN J K, HAN T C. Analysis of the zoning excavation monitoring data of a narrow and deep foundation pit in a soft soil area[J]. Journal of Geophysics and Engineering, 2018, 15(4): 1231–1241. doi: 10.1088/1742-2140/aaadd2
    [36]
    EID A, HESTER J G D, TENTZERIS M M. 5G as a wireless power grid[J]. Scientific Reports, 2021, 11: 636. doi: 10.1038/s41598-020-79500-x
    [37]
    REN J, ZHANG D Y, HE S W, et al. A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudlet[J]. ACM Computing Surveys, 2020, 52(6): 125.
    [38]
    RAWLINGS C. Geotechnical finite element analysis-a practical guide[C]// Proceedings of the Institution of Civil Engineers-Civil Engineering, 2017.
    [39]
    ZHANG W G, ZHANG R H, WU C Z, et al. State-of-the-art review of soft computing applications in underground excavations[J]. Geoscience Frontiers, 2020, 11(4): 1095–1106.
    [40]
    JAFARI M. System identification of a soil tunnel based on a hybrid artificial neural network–numerical model approach[J]. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2020, 44(3): 889–899.
    [41]
    HASANPOUR R, ROSTAMI J, SCHMITT J, et al. Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2020, 12(1): 21–31.
    [42]
    CHEN R P, ZHANG P, KANG X, et al. Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods[J]. Soils and Foundations, 2019, 59(2): 284–295.
    [43]
    CAO B T, OBEL M, FREITAG S, et al. Artificial neural network surrogate modelling for real-time predictions and control of building damage during mechanised tunnelling[J]. Advances in Engineering Software, 2020, 149: 102869.
    [44]
    QIU H, FANG W, BAO H, et al. A feasibility study of virtual reality technology in guidance design of underground space[C]// International Conference on Applied Human Factors and Ergonomics. Springer, 2021.
    [45]
    CHI H L, KANG S C, WANG X Y. Research trends and opportunities of augmented reality applications in architecture, engineering, and construction[J]. Automation in Construction, 2013, 33: 116–122.
    [46]
    BERTAM J, MOSKALIAK J. Virtual reality training: making construction work safer[J]. Computers in Human Behavior, 2015, 43: 284–292.
    [47]
    XU J Q, MOREU F. A review of augmented reality applications in civil infrastructure during the 4th industrial revolution[J]. Frontiers in Built Environment, 2021, 7: 640732.
    [48]
    HANSEN L H, FLECK P, STRANNER M, et al. Augmented reality for subsurface utility engineering, revisited[J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(11): 4119–4128.
    [49]
    唐德琪, 俞峰, 黄祥国, 等. 开挖诱发坑内既有基桩附加内力的模型试验[J]. 浙江大学学报(工学版), 2019, 53(8): 1457–1466. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201908004.htm

    TANG De-qi, YU Feng, HUANG Xiang-guo, et al. Chamber tests for investigating additional internal forces in existing foundation piles induced by excavation[J]. Journal of Zhejiang University (Engineering Science), 2019, 53(8): 1457–1466. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201908004.htm
    [50]
    LI M G, DEMEIJER O, CHEN J J. Effectiveness of servo struts in controlling excavation-induced wall deflection and ground settlement[J]. Acta Geotechnica, 2020, 15(9): 2575–2590.
    [51]
    赵自强. 深基坑自动伺服系统应用的变形控制分析[C]// 第十五届全国工程物探与岩土工程测试学术大会. 厦门, 2017.

    ZHAO Zi-qiang. Deformation control analysis of automatic servo system application in deep foundation pit[C]// The 15th National Engineering Property Exploration and Geotechnical Engineering Test Academic Conference. Xiamen, 2017. (in Chinese)
    [52]
    CHEN B G, YAN T F, SONG D B, et al. Experimental investigations on a deep excavation support system with adjustable strut length[J]. Tunnelling and Underground Space Technology, 2021, 115: 104046.
    • Related Articles
  • Supplements (0)

  • Cited by

    Periodical cited type(5)

    1. 冯世进，史嘉梁，郑奇腾. 填埋场复合衬垫界面动力剪切特性研究. 结构工程师. 2024(03): 125-134 .
    2. 臧年永，肖成志，杨仕钊. 膨润土防水毯搭接界面力学性能的影响因素. 深圳大学学报(理工版). 2024(05): 626-634 .
    3. 林海，时花豹，周创兵，吕志涛. 黏土-膨润土混合土衬里的渗透特性试验研究. 材料导报. 2024(23): 96-101 .
    4. 林海，曾一帆，周创兵，董平霄，施建勇. 褶皱土工膜+针刺钠基膨润土防水毯复合衬里的剪切试验研究. 岩土力学. 2023(02): 355-361+372 .
    5. 章玲玲，林海. 土工膜GCL复合衬里剪切强度确定方法. 人民长江. 2023(12): 174-178+201 .

    Other cited types(3)

Catalog

    Get Citation
    PDF
    XML
    Article views PDF downloads Cited by(8)
    Related
    Copyright © 2010 Editorial By Chinese Journal of Geotechnical Engineering 苏ICP备05007122号-11公安联网备案号：32010602011255
    Editorial Office address：34 Hujuguan,Nanjing 210024,ChinaPostal Code：210024Tel：025-85829534，85829556E-mail: ge@nhri.cn

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return