An Intelligent Bridge Construction Method Driven by Digital Twin
- Pages: 1-11(2023)
DOI： 10.11834/jrs.20232590
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摘要
智能化是桥梁建设的重要方向，数字孪生是实现桥梁智能建造的有效途径。数字孪生将现实环境中桥梁的结构、状态和行为等映射到虚拟环境，为透彻理解和精准控制桥梁建造过程提供新手段。本文首先分析了桥梁及其环境高精度数字孪生与桥梁智能建造背景及发展趋势，然后提出了桥梁建造全过程数字孪生构建方法，探讨了空天地一体化监测数据关联融合、数字孪生场景智能构建与增强可视化、过程动态仿真与智能预测等关键技术，最后以复杂艰险山区大型桥梁建造为例开展案例应用分析，本文方法可望为复杂环境大型桥梁工程智能建造提供有效的理论指导和关键技术支撑。
Abstract
In recent years, China's infrastructure construction has expanded into remote and rugged mountainous regions, making bridge construction more challenging than ever before. These difficult environments make it extremely challenging to collect data, simulate scenarios, and manage information effectively, resulting in the need for improved quality assurance during construction. To meet these challenges, the concept of digital twinning has emerged as an important tool for achieving intelligent construction. By mapping the attributes, structures, states, performances, and behaviors of real-world bridges onto a virtual twin, a highly realistic and interconnected digital representation of the bridge and its surroundings can be created. This virtual geographic environment provides a powerful means of comprehending and controlling the construction process more precisely. This paper provides an in-depth analysis of the background and development trends of high-precision digital twinning for bridges and their environment, as well as intelligent bridge construction. This paper introduces the theory of virtual geographic environments and digital twinning, and explore the use of dynamic data and simulation models to drive spatial modeling and virtual-real mapping throughout the bridge construction process. The proposed approach is divided into four stages. First, a monitoring data correlation fusion model is constructed by integrating space, air, and ground monitoring technology. This enables data perception of the physical space of the bridge construction and makes information on the bridge construction process visible. Second, intelligent modeling methods are studied for the digital twinning scene of bridge construction. This step achieves a refined characterization and accurate description of the bridge construction environment using advanced modeling techniques. Third, dynamic simulation and intelligent prediction methods are established for the bridge construction process. This step utilizes the bridge construction digital twinning scene and combines it with multi-source monitoring data to enable dynamic diagnosis, evaluation, and intelligent prediction of the states and quality of the bridge construction process. Fourth, the bridge data, simulation models, and modeling knowledge are integrated to establish an intelligent management mechanism for the entire bridge construction process. This enables active control of the construction process, completing the iterative and interactive evolution, and achieving an intelligent closed-loop of "data perception-simulation analysis-intelligent prediction-optimization control" of bridge construction. To validate our methodology, we present a case study of a large bridge constructed in a complex and difficult mountainous area. Our approach provides effective theoretical guidance and key technological support for the intelligent construction of large bridges in complex environments.
关键词
虚拟地理环境桥梁智能建造数字孪生仿真预测智能建模
Keywords
virtual geographic environmentintelligent construction of bridgedigital twinsimulation predictionintelligent modeling
references
Bolourian N, Hammad A. 2020. LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection. Automation in Construction, 117: 103250[DOI: 10.1016/j.autcon.2020.103250http://dx.doi.org/10.1016/j.autcon.2020.103250]
Fabianowski D, Jakiel P. 2019. An expert fuzzy system for management of railroad bridges in use. Automation in Construction, 106: 102856[DOI: 10.1016/j.autcon.2019.102856http://dx.doi.org/10.1016/j.autcon.2019.102856]
Jung S, Choi D, Song S and Myung H. 2020. Bridge inspection using unmanned aerial vehicle based on HG-SLAM: Hierarchical graph-based SLAM. Remote Sensing, 12(18): 3022[DOI: 10.3390/rs12183022http://dx.doi.org/10.3390/rs12183022]
Karim M M, Dagli C H, Qin R. 2020. Modeling and simulation of a robotic bridge inspection system. Procedia Computer Science, 168: 177-185[DOI: 10.1016/j.procs.2020.02.276http://dx.doi.org/10.1016/j.procs.2020.02.276]
Liu Y F, Nie X, Fan J S and Liu X G. 2020. Image‐based crack assessment of bridge piers using unmanned aerial vehicles and three‐dimensional scene reconstruction. Computer‐Aided Civil and Infrastructure Engineering, 35(5): 511-529[DOI: 10.1111/mice.12501http://dx.doi.org/10.1111/mice.12501]
Saleem M R, Park J W, Lee J H, Jung H J and Sarwar M Z. 2021. Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network. Structural Health Monitoring, 20(4): 1760-1777[DOI: 10.1177/1475921720932http://dx.doi.org/10.1177/1475921720932]
Tao F, Qi Q. 2019. Make more digital twins. Nature, 572: 490-491[DOI: 10.1038/d41586-019-02849-1http://dx.doi.org/10.1038/d41586-019-02849-1]
Zhan J, Zhang F, Siahkouhi M, Xuan K and He X. 2021. A damage identification method for connections of adjacent box-beam bridges using vehicle-bridge interaction analysis and model updating. Engineering Structures,228: 111551[DOI: 10.1016/j.engstruct.2020.111551http://dx.doi.org/10.1016/j.engstruct.2020.111551]
Zhang K, Wang C, Liu X. 2020. Research on construction of highway bridge quality engineering based on BIM technology. In IOP Conference Series: Earth and Environmental Science. IOP Publishing, 510(5): 052092[DOI: 10.1088/1755-1315/510/5/052092http://dx.doi.org/10.1088/1755-1315/510/5/052092]
Zhang Y M, Wang H, Mao J X, Xu Z D and Zhang Y F. 2021. Probabilistic framework with bayesian optimization for predicting typhoon-induced dynamic responses of a long-span bridge. Journal of Structural Engineering, 147(1): 04020297[DOI: 10.1061/(asce)st.1943-541x. 0002881http://dx.doi.org/10.1061/(asce)st.1943-541x.0002881]
Zhou X, Zhang X. 2019. Thoughts on the development of bridge technology in China. Engineering, 5(6): 1120-1130[DOI: 10.1016/j.eng.2019.10.001http://dx.doi.org/10.1016/j.eng.2019.10.001]
Zhou Y, Pei Y, Li Z, Fang L, Zhao Y, and Yi W. 2020. Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms. Measurement, 159: 107801[DOI: 10.1016/j.measurement.2020.107801http://dx.doi.org/10.1016/j.measurement.2020.107801]
Du M F. 2021. Research on the development of new technology and business form of China intelligent construction. Construction Technology, 50(13): 54-59.
杜明芳. 2021. 中国智能建造新技术新业态发展研究.施工技术(中英文), 50(13): 54-59[DOI: 10.7672/sgjs2021130054http://dx.doi.org/10.7672/sgjs2021130054]
Editorial Office of "Chinese Journal of Highways". 2021. Review on China's bridge engineering research: 2021. China Journal of Highway and Transport, 34(02): 1-97.
《中国公路学报》编辑部. 2021. 中国桥梁工程学术研究综述·2021. 中国公路学报, 34(02): 1-97[DOI: 10.19721/j.cnki.1001-7372.2021.02.001http://dx.doi.org/10.19721/j.cnki.1001-7372.2021.02.001]
Gou H Y, Yang B, Hua H, Xie X, Liu C, Liu Y and Pu Q H. 2020. State-of-the-art review of bridge informatization and intelligent bridge in 2019. Journal of Civil and Environmental Engineering, 42(05): 14-27.
勾红叶, 杨彪, 华辉, 谢蕊, 刘畅, 刘雨, 蒲黔辉. 2020. 桥梁信息化及智能桥梁2019年度研究进展. 土木与环境工程学报(中英文), 42(05):14-27[DOI: 10.11835/j.issn.2096-6717.2020.103http://dx.doi.org/10.11835/j.issn.2096-6717.2020.103]
Gou H Y, Yang B, Liu Y and Pu Q H. 2021. Deformation mapping relationship and running safety evaluation of train-track-bridge system for high-speed railway in complex conditions. China Journal of Highway and Transport, 34(04): 162-173.
勾红叶, 杨彪, 刘雨, 蒲黔辉. 2021. 复杂条件下车-轨-桥变形映射关系及行车安全评价. 中国公路学报, 34(04): 162-173 [DOI: 10.19721/j.cnki.1001-7372.2021.04.014http://dx.doi.org/10.19721/j.cnki.1001-7372.2021.04.014]
He S H, Wang A H, Zhu Z and Zhao Y. 2021.Research progress on intelligent detection technologies of highway bridges. China Journal of Highway and Transport, 34(12): 12-24.
贺拴海, 王安华, 朱钊, 赵煜. 2021.公路桥梁智能检测技术研究进展.中国公路学报, 34(12): 12-24[DOI: 10.19721/j.cnki.1001-7372.2021.12.002http://dx.doi.org/10.19721/j.cnki.1001-7372.2021.12.002]
Li D R, Zhang H Y, Jin W J. 2022. The Mission of Geo-spatial Information Science in New Infrastructure Era. Geomatics and Information Science of Wuhan University, 47(10): 1515-1522.
李德仁, 张洪云, 金文杰. 2022. 新基建时代地球空间信息学的使命.武汉大学学报(信息科学版), 47(10): 1515-1522[DOI: 10.13203/j.whugis20220078http://dx.doi.org/10.13203/j.whugis20220078]
Li D R. 2021. Smart city based on digital twin. China Internet, 7: 12.
李德仁. 2021.基于数字孪生的智慧城市.互联网天地, 7: 12
Lin H, Hu M Y, Chen M, Zhang F, You L and Chen Y T. 2020. Cognitive transformation from geographic information system to virtual geographic environments. Journal of Geo-Information Science, 22(04): 662-672.
林珲, 胡明远, 陈旻, 张帆, 游兰, 陈宇婷. 2020. 从地理信息系统到虚拟地理环境的认知转变. 地球信息科学学报, 22(04): 662-672[DOI: 10.12082/dqxxkx.2020.200048http://dx.doi.org/10.12082/dqxxkx.2020.200048]
Lu C F, Ban X L. 2021. Health management of railway bridge based on testing technology. China Railway, 9: 11-17.
卢春房, 班新林. 2021. 以检测技术为基础 做好铁路桥梁健康管理.中国铁路, 9: 11-17[DOI: 10.19549/j.issn.1001-683x.2021.09.011http://dx.doi.org/10.19549/j.issn.1001-683x.2021.09.011]
Lu C F. 2018. Challenges facing transport infrastructure. China Highway, 15: 22-23.
卢春房. 2018. 交通基础设施面对的挑战. 中国公路, 15: 22-23[DOI: 10.13468/j.cnki.chw.2018.15.004http://dx.doi.org/10.13468/j.cnki.chw.2018.15.004]
Ma Y F, Ouyang Q B, Wang G D, Wang L and Zhang J R. 2020. Fatigue life prediction for suspender based on equivalent initial flaw size. Journal of Transport Science and Engineering, 36(02): 52-57.
马亚飞, 欧阳清波, 汪国栋, 王磊, 张建仁. 2020. 基于等效初始裂纹尺寸的吊杆疲劳寿命预测. 交通科学与工程, 36(02): 52-57[DOI: : 10.16544/j.cnki.cn43-1494/u.2020.02.009http://dx.doi.org/10.16544/j.cnki.cn43-1494/u.2020.02.009]
Pan Y J, Liu X G, Cai D G and Zhao X X. 2020. Research on correlation of railway bridge BIM model and full life cycle information oriented to operation and maintenance. China Railway, 5: 73-80.
潘永杰, 刘晓光,蔡德钩, 赵欣欣. 2020. 面向运维的铁路桥梁BIM模型及全生命周期信息关联研究. 中国铁路, 5: 73-80[DOI: 10.19549/j.issn.1001-683x.2020.05.073http://dx.doi.org/10.19549/j.issn.1001-683x.2020.05.073]
Qi C L, Kang Y G, Wang Y, Song S F and Xu H Q. 2022. Intelligent design and construction system scheme of Hangzhou bay bridge. Railway Technical Innovation, 3: 30-35.
齐成龙, 康银庚, 王永, 宋树峰, 徐洪权.2022. 杭州湾跨海大桥智能设计与建造体系方案. 铁路技术创新, 3: 30-35[DOI: 10.19550/j.issn.1672-061x.2022.01.29.001http://dx.doi.org/10.19550/j.issn.1672-061x.2022.01.29.001]
Song H, Han G H. 2022. Intelligent span arrangement of railway bridges based on BIM and AI technology. Railway Computer Application, 31(07): 32-36.
宋浩, 韩广晖. 基于BIM和AI技术的铁路桥梁智能布跨研究. 2022. 铁路计算机应用, 31(07): 32-36[DOI: 10.3969/j.issn.1005-8451.2022.07.06http://dx.doi.org/10.3969/j.issn.1005-8451.2022.07.06]
Tan J R, Liu Z Y, Xu J H. 2018. Intelligent products and equipment led by new-generation artificial intelligence. Strategic Study of CAE, 20(04): 35-43.
谭建荣, 刘振宇, 徐敬华. 2018. 新一代人工智能引领下的智能产品与装备.中国工程科学, 20(04): 35-43[DOI: 10.15302/J-SSCAE-2018.04.007http://dx.doi.org/10.15302/J-SSCAE-2018.04.007]
Tao F, Liu W R, Zhang M, Hu T L, Qi Q L, Zhang H, Sui F Y, Wang T, Xu H, Huang Z G, Ma X, Zhang L C, Cheng J F, Yao L K, Yi W M, Zhu K Z, Zhang X S, Meng F J, Jin X H, Liu Z B, He L R, Cheng H, Zhou E Z, Li Y, Lv Q and Luo Y M. 2019. Five dimension digital twin model and its ten applications. Computer Integrated Manufacturing Systems, 25(01): 1-18.
陶飞, 刘蔚然, 张萌, 胡天亮, 戚庆林, 张贺, 隋芳媛, 王田, 徐慧, 黄祖广, 马昕, 张连超, 程江峰, 姚念奎, 易旺民, 朱恺真, 张新生, 孟凡军, 金小辉, 刘中兵, 何立荣, 程辉, 周二专, 李洋, 吕倩, 罗椅民. 数字孪生五维模型及十大领域应用. 2019. 计算机集成制造系统, 25(01): 1-18[DOI: 10.13196/j.cims.2019.01.001http://dx.doi.org/10.13196/j.cims.2019.01.001]
Wang C S, Wang Q, Duan L. 2021. Leading the development trend of steel bridge technology-design theory and intelligent construction of long-life steel bridges. China Highway,11: 48-49.
王春生, 王茜, 段兰. 2021. 引领钢桥科技发展趋势——长寿命钢桥设计理论与智能建造.中国公路, 11: 48-49[DOI: 10.3969/j.issn.1006-3897.2021.11.018http://dx.doi.org/10.3969/j.issn.1006-3897.2021.11.018]
Wang H K, Liu J J, Zhang Y G, Guan G Y and Wu T T. 2020. BIM intelligent management system for maintenance and reconstruction of Nanjing Yangtze River Bridge. Highway, 65(11): 361-365.
王宏坤, 刘锦军, 张义桂, 关高勇, 吴涛洮. 2020. 南京长江大桥维修改造BIM智能管理系统. 公路, 65(11): 361-365
Wang T J. 2021. Research on key technologies for intelligent construction of railway bridge. China Railway, 9: 1-10.
王同军. 2021.铁路桥梁智能建造关键技术研究. 中国铁路,9: 1-10[DOI: 10.19549/j.issn.1001-683x.2021.09.001http://dx.doi.org/10.19549/j.issn.1001-683x.2021.09.001]
Xia Y, Lei X M, Wang P, Liu G M, Sun L M.2021. Regional bridge information integration and data mining for network-level assessment. Journal of Harbin Institute of Technology, 53(03): 66-74.
夏烨, 雷晓鸣, 王鹏, 刘国明, 孙利民. 2021. 针对网级评估的区域桥梁信息集成与数据挖掘. 哈尔滨工业大学学报, 53(03): 66-74[DOI: 10.11918/201908024http://dx.doi.org/10.11918/201908024]
Yang Y, Wang L, Zhang Y F. 2020. Development and challenging issues of bridge detection technology using unmanned aerial vehicles. Modern Transportation Technology, 17(04): 27-32.
杨扬, 王连发, 张宇峰. 2020. 无人机桥梁检测技术进展与瓶颈问题分析. 现代交通技术, 17(04): 27-32[DOI: CNKI: SUN: JTJZ.0.2020-04-006http://dx.doi.org/CNKI:SUN:JTJZ.0.2020-04-006]
Zhang X C, Li S Y, Zhou Q M and Sun Y. 2021. The rationale and innovative thinking of building Digital Twin City. Science of Surveying and Mapping, 46(03): 147-152+168.
张新长, 李少英, 周启鸣, 孙颖. 2021. 建设数字孪生城市的逻辑与创新思考.测绘科学, 46(03): 147-152+168[DOI: 10.16251/j.cnki.1009-2307.2021.03.022http://dx.doi.org/10.16251/j.cnki.1009-2307.2021.03.022]
Zhang X G, Liu G, Ma J H, Wu H B, Fu B Y and Gao Y. 2017. Current situation and prospect of bridge technology in China. China Highway, 5: 40-45.
张喜刚, 刘高, 马军海, 吴宏波, 付佰勇, 高原. 2017. 中国桥梁技术的现状与展望. 中国公路, 5: 40-45[DOI: 10.1360/N972015-00912http://dx.doi.org/10.1360/N972015-00912]
Zhang X, Lian F, Wang T M and Ma P. 2020.BIM+ Internet application in construction of Bianyuzhou Yangtze river bridge. Railway Technical Innovation, 3: 70-75.
张翔, 连飞,王同民, 马攀. 2020. BIM+互联网技术在鳊鱼洲长江大桥施工中的应用. 铁路技术创新,3: 70-75[DOI: 10.19550/j.issn.1672-061x.2020.03.070http://dx.doi.org/10.19550/j.issn.1672-061x.2020.03.070]
Zhao T Q, Gou H Y, Chen X Y, Li W H, Liang H, Chen Z H and Zhou S Q. 2021. State-of-the-art review of bridge informatization and intelligent bridge in 2020.Journal of Civil and Environmental Engineering, 43(S1): 268-279.
赵天祺, 勾红叶, 陈萱颖, 李文昊, 梁浩, 陈子豪, 周思清. 2021. 桥梁信息化及智能桥梁2020年度研究进展. 土木与环境工程学报(中英文), 43(S1): 268-279[DOI: 10.11835/j.issn.2096-6717.2020.103http://dx.doi.org/10.11835/j.issn.2096-6717.2020.103]
Zhao Y N, Wang H, Gao H, Zhu Q X, Wang F Q and Xie Y S. 2020. Design and application of stress monitoring scheme for high-speed railway continuous girder bridge construction based on BIM. Railway Standard Design, 64(11): 68-73.
赵亚宁, 王浩, 郜辉,祝青鑫, 王飞球, 谢以顺. 2020. 基于BIM的高铁连续梁施工应力监控方案设计及应用.铁道标准设计, 64(11): 68-73[DOI: 10.13238/j.issn.1004-2954.201910070002http://dx.doi.org/10.13238/j.issn.1004-2954.201910070002]
Zhou X H, Liu J P, Chen G Z, Li D C, Huang T, Liang J H, Liu H and Liu Y X. Intelligent virtual trial assembly of large and complex steel arch bridges based on point cloud data. China Journal of Highway and Transport, 34(11): 1-9.
周绪红, 刘界鹏, 程国忠, 李东声, 黄涛, 梁俊海, 刘虎, 刘雨鑫.2021. 基于点云数据的大型复杂钢拱桥智能虚拟预拼装方法.中国公路学报, 34(11): 1-9.[DOI: 10.19721/j.cnki.1001-7372.2021.11.001http://dx.doi.org/10.19721/j.cnki.1001-7372.2021.11.001]
Zhu Q, Zhang L G, Ding Y L, Hu H, Ge X M, Liu M W and Wang W. 2022. From real 3D modeling to digital twin modeling. Acta Geodaetica et Cartographica Sinica, 51(06): 1040-1049.
朱庆, 张利国, 丁雨淋, 胡翰, 葛旭明, 刘铭崴, 王玮. 2022. 从实景三维建模到数字孪生建模.测绘学报, 51(06):1040-1049[DOI: 10.11947/ j.AGCS.2022.20210640http://dx.doi.org/10.11947/j.AGCS.2022.20210640]
Zhu Q, Zhu J, Huang H P, Wang W and Zhang L G. 2020. Real 3D spatial information platform and digital twin Sichuan-Tibet railway. High Speed Railway Technology, 11(02): 46-53.
朱庆, 朱军, 黄华平, 王玮, 张利国. 2020. 实景三维空间信息平台与数字孪生川藏铁路. 高速铁路技术, 11(02): 46-53[DOI: CNKI: SUN: GSTL.0.2020-02-009http://dx.doi.org/CNKI:SUN:GSTL.0.2020-02-009]
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State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences
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Faculty of Geographical Science, Beijing Normal University
Department of Information and Communication, Academy of Army Armored Forces
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