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首页 > 过刊浏览>2021年第43卷第S1期 >268-279. DOI:10.11835/j.issn.2096-6717.2021.230
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桥梁信息化及智能桥梁2020年度研究进展
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国家自然科学基金(51878563)


State-of-the-art review of bridge informatization and intelligent bridge in 2020
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    摘要:

    以信息化、智能化为特征的数字化时代的到来推动了桥梁工程技术的发展与创新，有必要将云计算、大数据、人工智能、3D打印、机器人等战略性新兴产业技术与桥梁工程相融合，从智能设计、智能施工、智能运维等多个维度，推进桥梁工业化、数字化、智能化升级。本文从桥梁信息化、智能检测与安全运维、智能防灾减灾、智能材料等方面，综述了2020年该领域前沿技术和重要成果，总结了研究热点与前景展望。分析表明：BIM技术可以提升桥梁正向设计精细化水平、施工过程控制和管理准确化程度；无人机、机器人等智能检测技术与机器学习、卷积神经网络等人工智能技术提高了桥梁检测的精度和效率；高性能智能材料的应用促进了桥梁结构的自感知性、自适应性、自调节性和自诊断性；基于人工智能的自然灾害监测与预警为桥梁智能防灾减灾提供了新的发展思路。未来应将人工智能技术深度融合桥梁设计、建造和养护的全生命周期，顺应信息化、智能化的发展趋势，实现桥梁强国梦。

    Abstract:

    With the advent of digital era characterized by informatization and intelligence, the development and innovation of bridge engineering technology are promoted. It is necessary to integrate cloud computing, big data, artificial intelligence, 3D printing, robot and other strategic emerging industrial technologies with bridge engineering, and promote the industrialization, digitization and intelligent upgrading of bridges from multiple dimensions such as intelligent design, construction, operation and maintenance. This paper reviews related frontier technologies and important achievements worldwide in 2020, with regard to bridge informatization, intelligent inspection, safety operation and maintenance, intelligent disaster prevention/mitigation, intelligent materials, and summarizes the research hotspots and prospects. According to the review, BIM technologies can improve the refinement of bridge forward design, the accuracy of construction process control and management. Intelligent inspection technologies (e.g. UAV and robots)and artificial intelligence technologies (e.g. machine learning and convolution neural network)improve the accuracy and efficiency of bridge inspection and monitoring. Applications of high-performance intelligent materials promotes the self-perception, self-adaptability, self-adjustment and self-diagnosis of bridge. Natural disaster monitoring and early warning based on artificial intelligence (AI)provides new development idea for bridge intelligent disaster prevention. To conform to the development trend of informatization and intelligence, future research should deeply integrate artificial intelligence technology into the whole life cycle of bridge design, construction and maintenance to realize the dream of bridge power.

    参考文献
    [1] 傅战工, 张金涛, 张锐. 基于Inventor的常泰长江大桥主塔BIM正向设计[J]. 铁道标准设计, 2020, 64(Sup1):190-194.
    [2] 陈洪春, 黄武, 陈航. BIM技术在宁淮铁路桥梁设计中的应用[J]. 铁道标准设计, 2020, 64(Sup1):187-190.
    [3] 赵月悦, 彭灿, 张超超, 等. 福厦高铁桥梁BIM技术应用[J]. 世界桥梁, 2020, 48(Sup1):106-112.
    [4] 李红豫, 李恒, 吴悦, 等. 基于BIM的东洲湘江大桥参数化设计应用研究[J]. 公路, 2020, 65(11):173-178.
    [5] 张鹏飞, 杨福瑞, 雷晓燕, 等. 基于Revit平台的高速铁路大跨斜拉桥工程算量系统研发[J]. 铁道标准设计, 2021, 65(8):72-76.
    [6] 周应华, 瞿浩, 景磊. 基于精细化BIM模型的钢结构桥梁工程量自动统计技术研究[J]. 公路, 2020, 65(7):109-112.
    [7] 黄成岑, 李洋溢, 袁通. BIM技术在桥隧相接部位方案设计中的应用[J]. 中外公路, 2020, 40(5):236-239.
    [8] 戴林发宝, 薛光桥, 苑俊杰. 基于多源数据的铁路工程BIM协同设计平台研究[J]. 铁道标准设计, 2021, 65(8):96-100.
    [9] 张翔, 连飞, 王同民, 等. BIM+互联网技术在鳊鱼洲长江大桥施工中的应用[J]. 铁路技术创新, 2020(3):70-75.
    [10] 王宏坤, 刘锦军, 张义桂, 等. 南京长江大桥维修改造BIM智能管理系统[J]. 公路, 2020, 65(11):361-365.
    [11] 赵亚宁, 王浩, 郜辉, 等. 基于BIM的高铁连续梁施工应力监控方案设计及应用[J]. 铁道标准设计, 2020, 64(11):68-73.
    [12] 袁宇, 李坤, 王银广. 基于BIM移动端的桥梁预防性养护研究[J]. 智能城市, 2020, 6(21):66-67.
    [13] 王一, 李坤, 王银广, 等. 基于BIM的桥梁养护成本控制[J]. 智能城市, 2020, 6(21):80-81.
    [14] 潘永杰, 刘晓光, 蔡德钩, 等. 面向运维的铁路桥梁BIM模型及全生命周期信息关联研究[J]. 中国铁路, 2020(5):73-80.
    [15] FLEISCHHACKER A, GHONIMA O, SCHUMACHER T. Bayesian survival analysis for US concrete highway bridge decks[J]. Journal of Infrastructure Systems, 2020, 26(1):04020001.
    [16] ISAILOVIĆ D, STOJANOVIC V, TRAPP M, et al. Bridge damage:Detection, IFC-based semantic enrichment and visualization[J]. Automation in Construction, 2020, 112:103088.
    [17] 胡兴意, 陈波, 刘国强, 等. 融合BIM与影像建模技术的桥梁检测方法及其应用研究[J]. 中外公路, 2020, 40(1):107-112.
    [18] CAPRANI C C, DE MARIA J. Long-span bridges:analysis of trends using a global database[J]. Structure and Infrastructure Engineering, 2020, 16(1):219-231.
    [19] 杨扬, 王连发, 张宇峰. 无人机桥梁检测技术进展与瓶颈问题分析[J]. 现代交通技术, 2020, 17(4):27-32.
    [20] KARIM M M, DAGLI C H, QIN R W. Modeling and simulation of a robotic bridge inspection system[J]. Procedia Computer Science, 2020, 168:177-185.
    [21] PERRY B J, GUO Y L, ATADERO R, et al. Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs)and machine learning[J]. Measurement, 2020, 164:108048.
    [22] SALEEM M R, PARK J W, LEE J H, et al. Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network[J]. Structural Health Monitoring, 2020:147592172093238.
    [23] ZOLLINI S, ALICANDRO M, DOMINICI D, et al. UAV photogrammetry for concrete bridge inspection using object-based image analysis (OBIA)[J]. Remote Sensing, 2020, 12(19):3180.
    [24] BOLOURIAN N, HAMMAD A. LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection[J]. Automation in Construction, 2020, 117:103250.
    [25] BISCARINI C, CATAPANO I, CAVALAGLI N, et al. UAV photogrammetry, infrared thermography and GPR for enhancing structural and material degradation evaluation of the Roman masonry bridge of Ponte Lucano in Italy[J]. NDT & E International, 2020, 115:102287.
    [26] JUNG S, CHOI D, SONG S, et al. Bridge inspection using unmanned aerial vehicle based on HG-SLAM:hierarchical graph-based SLAM[J]. Remote Sensing, 2020, 12(18):3022.
    [27] JANG K, AN Y K, KIM B, et al. Automated crack evaluation of a high-rise bridge pier using a ring-type climbing robot[J]. Computer-Aided Civil and Infrastructure Engineering, 2021, 36(1):14-29.
    [28] BOOMERI V, TOURAJIZADEH H. Design, modeling, and control of a new manipulating climbing robot through infrastructures using adaptive force control method[J]. Robotica, 2020, 38(11):2039-2059.
    [29] CHEN Y X, HU B B, ZOU J K, et al. Design and fabrication of a multi-motion mode soft crawling robot[J]. Journal of Bionic Engineering, 2020, 17(5):932-943.
    [30] BAEK H, JUN B H, NOH M D. The application of sector-scanning sonar:Strategy for efficient and precise sector-scanning using freedom of underwater walking robot in shallow water[J]. Sensors, 2020, 20(13):3654.
    [31] DONG Z X, MAI Z H, YIN S Q, et al. A weld line detection robot based on structure light for automatic NDT[J]. The International Journal of Advanced Manufacturing Technology, 2020, 111(7/8):1831-1845.
    [32] 王海波, 勾红叶, 韩冰, 等. 用于正交异性面板疲劳裂纹检测的装置及其使用方法:201911190522.1[P]. 2020-11-27.
    [33] D'AMICO F, GAGLIARDI V, BIANCHINI CIAMPOLI L, et al. Integration of InSAR and GPR techniques for monitoring transition areas in railway bridges[J]. NDT & E International, 2020, 115:102291.
    [34] ALANI A M, TOSTI F, CIAMPOLI L B, et al. An integrated investigative approach in health monitoring of masonry arch bridges using GPR and InSAR technologies[J]. NDT & E International, 2020, 115:102288.
    [35] ASADI P, GINDY M, ALVAREZ M, et al. A computervision based rebar detection chain for automatic processing of concrete bridge deck GPR data[J]. Automation in Construction, 2020, 112:103106.
    [36] 何志文, 赵新铭, 叶宇霄, 等. 基于BOTDA的混凝土管双裂缝监测试验研究[J]. 混凝土与水泥制品, 2020(7):43-46.
    [37] ZHANG L X, QIU G Y, CHEN Z S. Structural health monitoring methods of cables in cable-stayed bridge:A review[J]. Measurement, 2021, 168:108343.
    [38] SHAO S, ZHOU Z X, DENG G J, et al. Experiment of structural geometric morphology monitoring for bridges using holographic visual sensor[J]. Sensors, 2020, 20(4):1187.
    [39] 刘妙燕, 陆俊, 明攀, 等. 静载和疲劳荷载作用下橡胶混凝土声发射特性研究[J]. 水利与建筑工程学报, 2020, 18(5):149-155.
    [40] LIU S M, WU C X, ZHOU J T, et al. Relation between the shear stress distribution and the resulting acoustic emission variation in concrete beams[J]. Structural Control and Health Monitoring, 2020, 27(6):e2528.
    [41] 汪兴源, 周建庭, 刘腾飞, 等. 声发射信号分布与RC梁截面应力变化的相关性[J]. 土木工程与管理学报, 2020, 37(1):134-138, 145.
    [42] 段兰, 王春生, 翟慕赛, 等. 基于声发射技术的钢桥面板疲劳损伤监测与评估[J]. 交通运输工程学报, 2020, 20(1):60-73.
    [43] 陈鑫, 朱劲松, 钱骥, 等. 基于导波多尺度能量熵的钢绞线张拉力识别[J]. 振动.测试与诊断, 2020, 40(4):745-750, 826.
    [44] LIU L, ZHENG D, ZHOU J T, et al. Parameters that influence corrosion detection in reinforced concrete based on eddy current thermography[J]. Advances in Civil Engineering, 2020, 2020:1-9.
    [45] WANG D L, ZHANG Y Q, PAN Y, et al. An automated inspection method for the steel box girder bottom of long-span bridges based on deep learning[J]. IEEE Access, 2020, 8:94010-94023.
    [46] 许国平, 林超. 基于激光扫描技术的无砟轨道离缝智能检测小车研发[J]. 中国铁路, 2020(7):95-100.
    [47] SUN L M, SHANG Z Q, XIA Y, et al. Review of bridge structural health monitoring aided by big data and artificial intelligence:From condition assessment to damage detection[J]. Journal of Structural Engineering, 2020, 146(5):04020073.
    [48] MOHAMMED ABDELKADER E, MARZOUK M, ZAYED T. A self-adaptive exhaustive search optimization-based method for restoration of bridge defects images[J]. International Journal of Machine Learning and Cybernetics, 2020, 11(8):1659-1716.
    [49] ASJODI A H, DAEIZADEH M J, HAMIDIA M, et al. Arc Length method for extracting crack pattern characteristics[J]. Structural Control and Health Monitoring, 2021, 28(1):e2653.
    [50] LI G, LIU Q W, ZHAO S M, et al. Automatic crack recognition for concrete bridges using a fully convolutional neural network and naive Bayes data fusion based on a visual detection system[J]. Measurement Science and Technology, 2020, 31(7):075403.
    [51] PHAM H C, TA Q B, KIM J T, et al. Bolt-loosening monitoring framework using an image-based deep learning and graphical model[J]. Sensors (Basel, Switzerland), 2020, 20(12):3382.
    [52] 刘宇飞, 樊健生, 孔思宇, 等. 多视角几何三维重建法识别工程结构缺损与变形[J]. 工程力学, 2020, 37(9):103-111.
    [53] 晏班夫, 李得睿, 徐观亚, 等. 基于快速DIC与正则化平滑技术的结构形变测试[J]. 中国公路学报, 2020, 33(9):193-205.
    [54] 杨杰文, 章光, 陈西江, 等. 基于深度学习的较复杂背景下桥梁裂缝检测[J]. 铁道科学与工程学报, 2020, 17(11):2722-2728.
    [55] KIM H, YOON J, SIM S H. Automated bridge component recognition from point clouds using deep learning[J]. Structural Control and Health Monitoring,2020, 27(9). https://doi.org/10.1002/stc.2591.
    [56] XU S Y, WANG J, SHOU W C, et al. Computer vision techniques in construction:a critical review[J]. Archives of Computational Methods in Engineering, 2021, 28(5): 3383-3397.
    [57] KALFARISI R, WU Z Y, SOH K. Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and integrated visualization[J]. Journal of Computing in Civil Engineering, 2020, 34(3): 04020010.
    [58] NARAZAKI Y, HOSKERE V, HOANG T A, et al. Vision-based automated bridge component recognition with high-level scene consistency[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(5): 465-482.
    [59] BAE H, JANG K, AN Y K. Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges[J]. Structural Health Monitoring, 2020: 147592172091722.
    [60] 周建庭, 李晓庆, 辛景舟, 等. 基于Kalman-GARCH模型的结构损伤识别[J]. 振动与冲击, 2020, 39(6): 1-7, 21.
    [61] ZHANG J, QU C X., YI T H, et al. Damage detection for decks of concrete girder bridges using the frequency obtained from an actively excited vehicle[J]. Smart Structures and Systems, 2021, 27 (1): 101-114.
    [62] CHEN Z, ZHANG R Y, ZHENG J W, et al. Sparse Bayesian learning for structural damage identification[J]. Mechanical Systems and Signal Processing, 2020, 140: 106689.
    [63] 饶勇平, 黄金山, 邱昊, 等. 基于部分观测剪切框架结构绝对响应量的地震激励的广义识别方法[J]. 厦门大学学报(自然科学版), 2020, 59(4): 578-582.
    [64] 林阳, 封周权, 华旭刚, 等. 基于自由振动响应识别桥梁断面颤振导数的人工蜂群算法[J]. 工程力学, 2020, 37(2): 192-200.
    [65] LIU Y F, NIE X, FAN J S, et al. Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(5): 511-529.
    [66] ZHAN J W, ZHANG F, SIAHKOUHI M, et al. A damage identification method for connections of adjacent box-beam bridges using vehicle-bridge interaction analysis and model updating[J]. Engineering Structures, 2021, 228: 111551.
    [67] 勾红叶, 杨彪, 刘雨, 等. 复杂条件下车-轨-桥变形映射关系及行车安全评价[J]. 中国公路学报, 2021, 34(4): 162-173.
    [68] GOU H Y, LIU C, HUA H, et al. Mapping relationship between dynamic responses of high-speed trains and additional bridge deformations[J]. Journal of Vibration and Control, 2021, 27(9/10):1051-1062.
    [69] GOU H Y, XIE R, LIU C, et al. Analytical study on high-speed railway track deformation under long-term bridge deformations and interlayer degradation[J]. Structures, 2021, 29:1005-1015.
    [70] ZHOU Y, PEI Y L, LI Z W, et al. Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms[J]. Measurement, 2020, 159:107801.
    [71] DI J, RUAN X Z, ZHOU X H, et al. Fatigue assessment of orthotropic steel bridge decks based on strain monitoring data[J]. Engineering Structures, 2021, 228:111437.
    [72] 马亚飞, 欧阳清波, 汪国栋, 等. 基于等效初始裂纹尺寸的吊杆疲劳寿命预测[J]. 交通科学与工程, 2020, 36(2):52-57.
    [73] CUI C, XU Y L, ZHANG Q H, et al. Vehicle-induced fatigue damage prognosis of orthotropic steel decks of cable-stayed bridges[J]. Engineering Structures, 2020, 212:110509.
    [74] 管德清, 丁湘泽, 潘嫣然. 考虑残余应力影响的焊接结构疲劳总寿命预测方法[J]. 长沙理工大学学报(自然科学版), 2020, 17(3):15-21.
    [75] TANG Z Y, BAO Y Q, LI H. Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring[J]. Structural Health Monitoring, 2020:147592172093174.
    [76] 冯德成, 吴刚. 混凝土结构基本性能的可解释机器学习建模[J/OL]. 建筑结构学报, https://doi.org/10.14006/j.jzjgxb.2020.0491.
    [77] 夏烨, 雷晓鸣, 王鹏, 等. 针对网级评估的区域桥梁信息集成与数据挖掘[J]. 哈尔滨工业大学学报, 2021, 53(3):66-74.
    [78] WU G M, YANG D H, YI T H, et al. Sliding life prediction of sliding bearings using dynamic monitoring data of bridges[J]. Structural Control and Health Monitoring, 2020, 27(5):e2515.
    [79] ZHAO H W, DING Y L, LI A Q, et al. Digital modeling on the nonlinear mapping between multi-source monitoring data of in-service bridges[J]. Structural Control and Health Monitoring, 2020, 27(11):e2618.
    [80] NI Y Q, WANG Y W, ZHANG C.A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data[J]. Engineering Structures, 2020, 212:110520.
    [81] ZHANG Y M, WANG H, MAO J X, et al. Probabilistic framework with Bayesian optimization for predicting typhoon-induced dynamic responses of a long-span bridge[J]. Journal of Structural Engineering, 2021, 147(1):04020297.
    [82] WEI S Y, BAO Y Q, LI H. Optimal policy for structure maintenance:A deep reinforcement learning framework[J]. Structural Safety, 2020, 83:101906.
    [83] 齐锡晶, 唐梁, 康伟鑫, 等. 高速公路桥梁桥面板养护方案多目标决策方法[J]. 东北大学学报(自然科学版), 2020, 41(7):1033-1040.
    [84] 杨颖, 李坤, 王银广. 基于模拟退火算法的BP神经网络在桥梁智能养护中的应用[J]. 智能城市, 2020, 6(21):12-13.
    [85] 段晓晨, 喇海霞, 胡天明, 等. 桥梁工程运维成本三维非线性智能控制研究[J]. 铁道工程学报, 2020, 37(9):102-107.
    [86] MAO X H, JIANG X D, YUAN C W, et al. Modeling the optimal maintenance scheduling strategy for bridge networks[J]. Applied Sciences, 2020, 10(2):498.
    [87] NILI M H, TAGHADDOS H, ZAHRAIE B. Integrating discrete event simulation and genetic algorithm optimization for bridge maintenance planning[J]. Automation in Construction, 2021, 122:103513.
    [88] 张令心, 钟江荣, 林旭川, 等. 区域与城市地震风险评估与监测技术研究项目及研究进展[J]. 地震科学进展, 2020, 50(3):1-19.
    [89] 刘汉龙, 马彦彬, 仉文岗, 文海家. 大数据技术在地质灾害防治中的应用综述[J/OL]. 防灾减灾工程学报, https://doi.org/10.13409/j.cnki.jdpme.2021.04.002.
    [90] 刘纪平, 刘猛猛, 徐胜华, 等. 大数据时代下的一体化综合减灾技术综述[J]. 武汉大学学报·信息科学版, 2020, 45(8):1107-1116.
    [91] 张锦, 徐君翔, 郭静妮, 等. 智能川藏铁路系统总体架构设计与研究[J]. 综合运输, 2020, 42(1):100-107.
    [92] 朱庆, 李函侃, 曾浩炜, 等. 面向数字孪生川藏铁路的实体要素分类与编码研究[J]. 武汉大学学报·信息科学版, 2020, 45(9):1319-1327.
    [93] BANIHABIB M E, JURIK L, KAZEMI M S, et al. A hybrid intelligence model for the prediction of the peak flow of debris floods[J]. Water, 2020, 12(8):2246.
    [94] 滕飞, 刘鉴竹, 祝锦烨, 等. 高铁大风预警模式挖掘[J]. 国防科技大学学报, 2020, 42(2):55-63.
    [95] 苏延文, 颜永逸, 曾永平, 等. 复杂山区铁路大跨桥梁施工大风监测预警技术[J]. 铁道标准设计, 2020, 64(Sup1):204-207.
    [96] 蒲黔辉, 杨长卫, 勾红叶, 等. 高速铁路防灾减灾——大数据智能化技术与应用[M]. 北京:科学出版社, 2020.
    [97] LIANG D, ZHENG Y, FANG C, et al. Shape memory alloy (SMA)-cable-controlled sliding bearings:Development, testing, and system behavior[J]. Smart Materials and Structures, 2020, 29(8):085006.
    [98] RUIZ-PINILLA J G, MONTOYA-CORONADO L A, RIBAS C, et al. Finite element modeling of RC beams externally strengthened with iron-based shape memory alloy (Fe-SMA) strips, including analytical stress-strain curves for Fe-SMA [J]. Engineering Structures, 2020, 223: 111152.
    [99] CAO S S, OZBULUT O E, WU S W, et al. Multi-level SMA/lead rubber bearing isolation system for seismic protection of bridges[J]. Smart Materials and Structures, 2020, 29(5): 055045.
    [100] SHAFEI E F, KIANOUSH R. Transient analysis of hybrid SMA-FRP reinforced concrete beams under sequential impacts[J]. Engineering Structures, 2020, 208: 109915.
    [101] KARIMIPOUR A, EDALATI M. Shear and flexural performance of low, normal and high-strength concrete beams reinforced with longitudinal SMA, GFRP and steel rebars[J]. Engineering Structures, 2020, 221: 111086.
    [102] SONG Q S, YAN G P, TANG G W, et al. Robust principal component analysis and support vector machine for detection of microcracks with distributed optical fiber sensors[J]. Mechanical Systems and Signal Processing, 2021, 146: 107019.
    [103] RUFAI O, CHANDARANA N, GAUTAM M, et al. Cure monitoring and structural health monitoring of composites using micro-braided distributed optical fibre[J]. Composite Structures, 2020, 254: 112861.
    [104] YAN K, YANG J C, ZHANG Y, et al. Safety performance monitoring of smart FBG-based FRP anchors[J]. Safety Science, 2020, 128: 104759.
    [105] HOU W Q, ZHENG Y, GUO W, et al. Piezoelectric vibration energy harvesting for rail transit bridge with steel-spring floating slab track system[J]. Journal of Cleaner Production, 2021, 291: 125283.
    [106] KAUR N, GOYAL S, ANAND K, et al. A cost-effective approach for assessment of pre-stressing force in bridges using piezoelectric transducers[J]. Measurement, 2021, 168: 108324.
    [107] ZHANG H, ZHOU Y H, QUAN L W. Identification of a moving mass on a beam bridge using piezoelectric sensor arrays[J]. Journal of Sound and Vibration, 2021, 491: 115754.
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赵天祺,勾红叶,陈萱颖,李文昊,梁浩,陈子豪,周思清.桥梁信息化及智能桥梁2020年度研究进展[J].土木与环境工程学报（中英文）,2021,43(S1):268-279. ZHAO Tianqi, GOU Hongye, CHEN Xuanying, LI Wenhao, LIANG Hao, CHEN Zihao, ZHOU Siqing. State-of-the-art review of bridge informatization and intelligent bridge in 2020[J]. JOURNAL OF CIVIL AND ENVIRONMENTAL ENGINEERING,2021,43(S1):268-279.10.11835/j. issn.2096-6717.2021.230

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  • 收稿日期:2021-07-06
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