Applications and challenges of digital twin in intelligent construction of transportation infrastructure
Article Text (Baidu Translation)
-
摘要: 为更深入了解数字孪生在交通基础设施智能建造的应用现状，阐明了数字孪生定义与参考框架，归纳了新型数据采集方法与传输技术，分析了数字孪生在交通基础设施智能建造中的应用价值与典型案例，探讨了实施过程中面临的挑战。分析结果表明：数字孪生通过构建多源数据融合与实时交互的虚拟环境，显著提升了交通基础设施智能建造的协同管理与智能决策能力；其交互反馈与自适应演化特性有助于实现人机协同、资源优化、施工安全与运维智能化，但目前数字孪生在基础设施领域仍处于初步发展阶段，存在开发技术不成熟，系统集成缺乏整体规划、标准不统一等问题，需经历长期优化以实现高效部署和稳定运行；数字孪生整体参考架构能促进资源整合与多模式兼容，促进各方协调合作，但目前具备服务功能的架构较少，且缺乏完善的标准与统一的评价体系，制约了技术的广泛应用与跨行业适配性；新型传感技术显著提升了数据采集的精准度与时效性，但初期成本较高；数据质量方面普遍缺乏战略性数据分析，容易形成数据冗余，且多源数据在安全评估与隐私保护方面也面临严峻挑战；未来数字孪生的构建应从系统层面进行统筹规划，推动数据标准化、增强系统互操作性，并优化数据管理与安全防护机制，确保技术体系的可靠性与可拓展性，同时，应通过产学研协同创新、跨领域平台建设与政策引导，加速技术突破与产业化进程。Abstract: To comprehensively understand the current application of digital twin in the intelligent construction of transportation infrastructure, the definition and reference frameworks of digital twin were elucidated. The emerging data acquisition methods and transmission technologies were summarized. The application value and representative cases of digital twin in the intelligent construction of transportation infrastructure were analyzed, and the key challenges encountered during implementation were discussed. Analysis results show that, by establishing virtual environments capable of multi-source data integration and real-time interaction, the digital twin significantly enhances collaborative management and intelligent decision-making capabilities in the intelligent construction of transportation infrastructure. Its interactive feedback and self-evolving features facilitate human-machine collaboration, resource optimization, construction safety, and intelligent operation and maintenance. Nevertheless, the current digital twin remains in its early developmental stage in the field of infrastructure. Issues exist including immature core technologies, fragmented system integration, and non-uniform standards. Therefore, sustained optimization is needed to achieve efficient deployment and stable operation. The overall reference architecture of digital twin promotes resource integration and multi-modal compatibility, thereby supporting inter-organizational coordination. However, few existing frameworks offer service functionalities. Furthermore, the lack of complete standards and unified evaluation systems constrains the wide application and cross-sector adaptability of the technology. The novel sensing technologies have markedly improved data acquisition in accuracy and timeliness, though with a high initial cost. In terms of data quality, the absence of strategic data analysis tends to result in data redundancy. In addition, multi-source data integration poses significant risks in security assessment and privacy protection. Future construction of digital twin should focus on overall planning at the system level. Data standardization should be advanced, and interoperability enhanced. Data management and safety mechanisms should also be optimized to ensure the reliability and extensibility of the technology system. Moreover, technological advancement and industrialization should be accelerated by collaborative innovation among academia, research institutions, and industry, along with cross-disciplinary platform construction and policy guidance.
-
图 20 政府-高校-企业协同关系
Figure 20. Collaborative relationship of government-university-enterprise
表 1 数字孪生的定义
Table 1. Definitions of DT
参考文献 定义 行业 Glaessgen等[4, 23-24] 对飞行器或系统进行集成的多物理场、多尺度、概率仿真，使用现有最佳物理模型、传感器更新和机队历史等，反映其飞行孪生体的生命。数字孪生是超现实的，可以考虑一个或多个重要且相互依赖的飞行系统。 航空航天 Grieves等[3] 一组虚拟信息结构，从微观原子层面到宏观几何层面全面描述潜在或实际的物理制造产品。在最佳情况下，通过检查物理制造产品可以获得的任何信息都可从其的数字孪生中获得。 复杂系统 陶飞等[25] 以数字化方式创建物理实体的虚拟模型，借助数据模拟物理实体在现实环境中的行为，通过虚实交互反馈、数据融合分析、决策迭代优化等手段，为物理实体增加或扩展新的能力。 制造业 刘大同等[26] 数字孪生指在信息化平台内建立、模拟一个物理实体、流程或者系统。借助于数字孪生，可以在信息化平台上了解物理实体的状态，并对物理实体里面预定义的接口元件进行控制。 复杂工业系统和复杂装备 Bruynseels等[27] 数字孪生代表一种特定的工程范式，其中单个物理工件与动态反映这些工件状态的数字模型配对。 医疗 
下载: 导出CSV
对比维度 BIM DT 模型构建 数据 数据和算法 数据状态 静态数据 静态和动态数据 联网状态 无法联网 可联网 更新方式 手动干预更新 持续链接到物理对象自动更新 目标 不能实时操作监控 实时操作监控
下载: 导出CSV
表 3 数字孪生框架比较
Table 3. Comparison of DT framework
框架 优点 缺点 适用性 局限性 未来发展方向 基本框架[10] 简单易实现，适用于低复杂度应用 缺乏灵活性，缺乏应对复杂系统的能力 适用于小规模、低复杂度应用 无法处理高动态、多维度的复杂需求 强化数据采集，提升标准化，集成人工智能实现自动化维护 Rǎvileanu等[37]的双向数据流框架 数据收集和分析层次模块化，灵活性高，适应不同的算法和优化技术 复杂性较高，实施和维护成本较大，依赖高质量数据 适合用于复杂的多阶段生产流程，适合大规模生产 技术门槛高，对数据和网络依赖大，集成复杂 优化实时数据处理，拓展智能预测，降低集成成本 Borangiu等[38]的4层框架 提供全面的实时监控和预测能力，支持离线优化和实时调整 实施成本高，系统集成复杂，数据依赖性强 适用于具有连续流动特征但不是稳态的半连续生产过程 对高质量数据依赖大，技术人员要求高，依赖计算资源 降低计算资源需求，增强适应性，优化智能调度 Tao等[39]的5维框架 强调物理与虚拟车间的实时同步，提供了全面的智能互联和反馈机制，优化生产调度与资源配置 实施过程中面临技术挑战，实施难度较大 适用于智能制造，能够实时优化生产调度 对高质量传感器数据的依赖较强，对数据流的稳定性和实时性要求高 实现智能调度，引入AR/VR，推动开放架构，提高系统兼容性与可扩展性 Aheleroff等[40]的工业4.0框架 提供灵活的服务架构，适应性强，支持大规模定制化应用，支持云平台的集成，提供全生命周期的管理与优化解决方案 技术要求较高，实施成本高，高度依赖云计算和大数据 适用于各类工业领域，特别是在需要灵活扩展和大规模定制的应用中 对云计算平台和网络环境依赖较大，数据传输和处理受云基础设施的限制 提高云计算资源优化能力，拓展跨行业应用，增强数据安全性与隐私保护 Redelinghuys等[42]的6层框架 支持多任务和多用户管理，支持与传统制造系统的平滑集成，支持与不同系统之间的兼容性 架构复杂，技术门槛高，实施成本较高，管理难度大 适用于传统和现代生产系统的融合 在网络不稳定或计算资源有限的情况下，系统性能可能受到影响 精简架构，提高兼容性，增强自动化运维与智能维护
下载: 导出CSV
表 4 适用于数字孪生视觉传感器
Table 4. Vision-based sensors for DT
视觉方法 范围/m 精度 采集与处理时长 应用 相机 0.1~1 000 1.0×10-4~1.0×10-2 m(子像素级) 5 s~10 min 运动识别、人物识别、材料识别 LiDAR 1~100 1.0×10-4~1.0×10-2 m(随距离变化) 15 min 几何造型 双目摄像头 0.1~100 1.0×10-4~1.0×10-2 m(子像素级) 10 min 掌面造型，内部结构改造 全站仪 1~5 000 1.0×10-3+1.0×10-6s(s为距离) 5 min 一般的测定 无人机摄影 1~10 000 1.0×10-2~1.0×10-1 m(随距离变化) 30 min 城市地区、地点建模 DIC < 1 1.0×10-6 m(对应微应变) 1 min 应变场测量 视频 0.1~1 000 1.0×10-4~1.0×10-2 m(子像素级) 5 s~10 min 动态特征 红外相机 1~10 0.1 ℃ 1 s 材料识别、温度测量、缺陷检查
下载: 导出CSV
表 5 无线通信技术特点
Table 5. Wireless communication technology characteristics
技术类型 优点 缺点 应用场景 无线电频率识别 自动化程度高，实时数据采集 信号易受干扰，存在安全隐患 物流跟踪、门禁管理 低功耗蓝牙 能耗低，可连接多设备(可达7个)，电池寿命长 数据速率较低，范围有限 工人定位、照明、空调等设施管理 近场通信 安全性高，操作简单 工作距离非常短(厘米级) 工人身份验证、门禁系统 超声波 精度高(厘米级)，低成本 易受环境噪声影响，距离限制在10~15 m 室内资产追踪(如建筑工地材料和工人跟踪) 红外线 低成本，简单易于实现，不需要频谱许可 受限于直线传播，受光照影响大 遥控器、自动门、无障碍入侵检测 Zigbee 网络结构灵活，适合大规模部署，低功耗 数据传输速率低，传输距离有限，需要网关才能连接到因特网 监测施工现场的温度、湿度、噪音和气体浓度等环境因素，无线传感器网络 Wi-Fi 数据传输速率高(可达几百Mbps)，覆盖广，支持多设备连接 功耗较高，易受干扰 现场通信、监测设备连接到中心管理系统、实时监控 超宽带 定位精度极高，强抗干扰能力 成本高，实现技术复杂 高精度定位、实时监控安全区域，如检测人员的接近，及时发出警报，以防止事故发生 蜂窝通信 高速连接，覆盖广 基础设施成本高，信号不稳定 实时通信、移动安全监测、应急响应与事故处理、智能设备与IoT集成 低功耗远距离通信技术 覆盖范围广，功耗低 数据传输速率较低(通常为0.3~50 kB·s-1)，延迟较高，不适合实时大数据传输 环境监测、监测安全区域 卫星通信 全球覆盖，无需地面基础设施 延迟高，成本昂贵 全球定位系统、气象监测
下载: 导出CSV
表 6 IoT在基础设施建造中的应用
Table 6. Applications of IoT in infrastructure
参考文献 应用案例 IoT传感器输入 IoT传感器输出 应用效果 Hou等[80] 无锡西桥 传感器采集桥梁的位移数据 无线传感器通过窄带物联网将数据传输至云服务器，进行数据存储和可视化 有效降低了桥梁位移监测成本，提高了数据传输效率和可靠性 Xie等[77] 南宁地铁1号线隧道 实时采集盾构隧道中的沉降数据 通过实时平台将数据传输给相关利益者，以便控制施工过程中的沉降 提升了隧道施工过程中的沉降控制精度，减少了施工风险 Sarkar等[81] Corridor Ahmedabad地铁 通过RFID传感器采集资产信息，实时跟踪设备和资产的位置 数据上传至云端，通过平台界面供项目利益相关方查看和管理 改善了地铁高架项目的资产管理，减少了管理错误，提高了生产效率 Danish等[82] 钢筋混凝土梁 位移传感器和加速度计监测结构在加载下的动态变化 数据实时传输至云端，进行结构健康评估 通过实时监测，及时发现混凝土结构中的损伤，提升了结构健康评估的准确性 Jin等[83] 温州银行大楼施工现场 智能安全帽和便携式RFID触发器采集工地人员位置信息 数据实时传输至云端，向管理平台报警 有效提升了工地入侵检测的安全管理水平，减少了事故发生的可能性 Sakena Benazer等[84] 铁路轨道中裂纹的检测 发光二极管-光敏电阻组件用于铁路裂缝检测 通过射频模块传输裂缝检测信息至控制室 利用低成本技术实现铁路裂缝检测，降低了设备安装和维护成本 Zhang等[85] 梁结构裂缝识别 传感器采集桥梁裂缝图像，通过卷积神经网络进行分类 通过卷积神经网络输出桥梁裂缝分类结果，数据传输到监控平台 提高了桥梁裂缝检测的效率和精度，增强了桥梁结构的健康监控能力，减少了结构故障风险 Zrelli等[86] 突尼斯的Radés-La-Goulette桥 无线传感器网络用于桥梁参数(应变、温度、湿度等)的监测 实时传输桥梁损伤数据至监控系统，实现远程监测 提高桥梁健康监测的精度和效率，及时发现结构问题 Wang等[87] 高拱坝 红外传感器和相机传感器用于监测振动深度并捕捉混凝土表面图像 通过物联网框架实时传输振动深度和混凝土表面图像数据 提高混凝土振动质量监测的精度，确保混凝土结构的长期稳定性 Iyer等[88] 铁轨 超声波传感器和摄像头用于检测铁路轨道的裂缝和锈蚀 故障检测信息通过云存储和全球移动通信系统传输至中央位置 提高铁路轨道故障检测的自动化程度，减少人工巡检成本 Alfarraj[89] 道路裂缝 智能移动传感器用于收集道路图像并检测裂缝 利用生物启发的深度学习网络分析图像，输出道路裂缝分类结果 有效检测道路裂缝，减少交通事故的风险 Feng等[90] 岷江水电站的溢洪道隧道 无人机系统收集泄洪隧道的图像数据，传感器检测钢筋暴露缺陷 深度学习网络输出缺陷检测结果，数据用于结构安全评估 提高泄洪隧道缺陷检测的准确性，提升结构安全评估能力 Zhou等[91] 武汉地铁4号线长江穿越地铁隧道施工现场 RFID、超声波、红外传感器用于监控地下施工现场的危险能量 通过物联网平台实时发出安全警报，避免事故发生 提高施工现场安全性，减少事故发生率 Yang等[92] 湖南省的某高速公路施工现场 传感器监测混凝土表面的湿度和温度 自动调整喷雾间隔时间并记录湿度和温度数据 提高混凝土早期养护效果，减少裂缝，提升混凝土质量
下载: 导出CSV
表 7 决策算法在基础设施建造中的应用
Table 7. Applications of decision algorithms in infrastructure construction
参考文献 决策算法 应用于基础设施建造项的环节 应用效果 Erharter等[116] 强化学习算法 隧道挖掘策略优化 开发了新的隧道挖掘策略，提高了挖掘过程中的安全性和效率 Kedir等[117] 强化学习算法和基于代理模型 施工规划与调度优化 提升了建筑项目活动排序和资源分配的决策能力，优化项目规划决策 Zhang等[118] 模糊贝叶斯网络 钻爆法隧道坍塌的概率评估 准确识别了隧道坍塌的关键风险因素，改善了风险管理，所提出的方法可以处理多状态的模糊性和不确定性 Zhou等[119] 支持向量机+鲸鱼优化算法 预测隧道挤压的严重程度 提高了识别准确性，最高的准确性约0.956 5 Almahameed等[120] 机器学习+粒子群优化 建筑项目管理预测建模和成本优化 提高成本估算精度、确定影响项目成本的关键因素以及实施旨在降低成本的策略 Lin等[121] 启发式粒子群优化+自适应粒子群优化 智能建筑项目中的资源调度 显著提高了调度的准确性和稳定性，在资源受限条件下缩短了工期 Long等[122] 遗传算法 优化重复施工项目的调度 增强资源工作连续性，减少了项目的工期和成本 Jiang等[123] 深度强化学习 对建筑项目中的资源和现金流进行持续自适应最优控制 优化了资源分配并改善了现金流管理 Asghari等[124] 强化学习 优化施工调度、资源分配和风险管理 提高了决策质量和效率，减少了项目延误和成本超支 Yao等[125] 深度强化学习 自动化施工调度，涉及任务分配、时间管理和资源调配 优化了调度流程，减少了施工时间，提高施工效率和降低成本 Liang等[126] 深度学习 桥梁橡胶支座的状态进行检测和分类 提高了对桥梁橡胶支座的检测准确性，减少了潜在的安全隐患 Lee等[127] 深度强化学习 机器人施工任务分配 提高了任务分配的灵活性和效率 Zhang等[128] 人工神经网络+粒子群优化 隧道与地下空间的稳定性评估 提高了稳定性预测的准确性，增强了施工安全性 Gondia等[129] 机器学习 施工项目延误风险预测 准确预测项目延误，帮助管理决策。 Zhou等[130] 支持向量机 深基坑施工安全风险预测 有效预测深基坑施工中的安全风险
下载: 导出CSV
表 8 监测点部署
Table 8. Deployment of monitoring points
序号 监测项目 样本数量 部署方式 监测仪器 1 纵向沉降 基准点 118个点 每个地铁站配备6个基准点 徕卡光学水平NA2+GPM3带因瓦合金棒 测量点 1 502个点 测量点位于轨道床上，每个点之间的间距约为20~30 m 2 径向收敛 点测距 737个截面 测量点安装在隧道中段上方和下方约50 cm处的2组反射器中，每组反射器之间的横截面间距约为120 m 徕卡全站仪TC1201带精密目标反射器 3 径向收敛 全截面扫描 28 827个部分 整个地铁隧道逐环扫描 移动式三维激光测量系
下载: 导出CSV
表 9 数据整合与共享策略概览
Table 9. Overview of data integration and sharing strategies
策略类别 具体策略 策略说明 数据质量 数据预处理与清洗 通过自动化工具去除数据冗余、噪声，确保数据一致性和准确性 标准化数据格式 建立统一的数据采集标准和格式，消除跨系统数据格式差异 数据质量监控 引入动态监控系统，实时检测数据缺陷并校正 数据可访问性 建设开放的应用程序编程(Application Programming Interface, API)接口 通过标准化API实现数据提取、访问和格式转换，提升跨平台流通效率 数据仓库与数据湖建设 整合结构化与非结构化数据，实现统一存储和高效查询 增强跨平台数据共享机制 利用中间件和数据交换平台，实现多源数据无缝对接 数据安全性 数据加密与脱敏 对敏感数据加密存储和传输，通过脱敏技术降低隐私泄露风险 身份认证与访问控制 实施双因素认证和细粒度权限管理，限制数据访问范围 定期安全审计与漏洞修复 使用自动化工具定期扫描安全风险，及时修复漏洞，保障数据安全
下载: 导出CSV
-
[1] AGARWAL R, CHANDRASEKARAN S, SRIDHAR M. Imagining construction's digital future[R]. New York: McKinsey & Company, 2018. [2] LI T, LI X J, RUI Y, et al. Digital twin for intelligent tunnel construction[J]. Automation in Construction, 2024, 158: 105210. [3] GRIEVES M, VICKERS J. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems[M]//Transdisciplinary Perspectives on Complex Systems. Cham: Springer International Publishing, 2016: 85-113. [4] GLAESSGEN E, STARGEL D. The digital twin paradigm for future NASA and U.S. air force vehicles[C]//AIAA. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston: AIAA, 2012: 1818. [5] 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. [6] SCHLEICH B, ANWER N, MATHIEU L, et al. Shaping the digital twin for design and production engineering[J]. CIRP Annals, 2017, 66(1): 141-144. [7] TAO F, CHENG J F, QI Q L, et al. Digital twin-driven product design, manufacturing and service with big data[J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(9): 3563-3576. [8] GRATIUS N, HOU Y, BERGÉS M, et al. Lessons learned on the implementation of probabilistic graphical model-based digital twins: a space habitat study[J]. Journal of Space Safety Engineering, 2023, 10(2): 172-181. [9] LIU C, LE ROUX L, KÖRNER C, et al. Digital twin-enabled collaborative data management for metal additive manufacturing systems[J]. Journal of Manufacturing Systems, 2022, 62: 857-874. [10] PREGNOLATO M, GUNNER S, VOYAGAKI E, et al. Towards Civil Engineering 4.0: concept, workflow and application of digital twins for existing infrastructure[J]. Automation in Construction, 2022, 141: 104421. [11] NADERI H, SHOJAEI A. Digital twinning of civil infrastructures: current state of model architectures, interoperability solutions, and future prospects[J]. Automation in Construction, 2023, 149: 104785. [12] OPOKU D G J, PERERA S, OSEI-KYEI R, et al. Digital twin application in the construction industry: a literature review[J]. Journal of Building Engineering, 2021, 40: 102726. [13] SHIM C S, KANG H R, DANG N S. Digital twin models for maintenance of cable-supported bridges[C]//ICSIC. International Conference on Smart Infrastructure and Construction 2019. Cambridge: ICE Publishing, 2019: 737-742. [14] LU Q C, XIE X, PARLIKAD A K, et al. Digital twin- enabled anomaly detection for built asset monitoring in operation and maintenance[J]. Automation in Construction, 2020, 118: 103277. [15] QI Q L, TAO F, HU T L, et al. Enabling technologies and tools for digital twin[J]. Journal of Manufacturing Systems, 2021, 58: 3-21. [16] CHEN C, LU Y, WU B, et al. Digital twin-based and knowledge graph-enhanced emergency response in urban infrastructure construction[J]. Applied Sciences, 2025, 15(11): 6009. [17] KUŠIĆ K, SCHUMANN R, IVANJKO E. A digital twin in transportation: real-time synergy of traffic data streams and simulation for virtualizing motorway dynamics[J]. Advanced Engineering Informatics, 2023, 55: 101858. [18] ZHANG H, LIU Q, CHEN X, et al. A digital twin-based approach for designing and multi-objective optimization of hollow glass production line[J]. IEEE Access, 2017, 5: 26901-26911. [19] LU Q C, PARLIKAD A K, WOODALL P, et al. Developing a digital twin at building and city levels: case study of west Cambridge campus[J]. Journal of Management in Engineering, 2020, 36(3): 05020004. [20] LENG J W, LIU Q, YE S D, et al. Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model[J]. Robotics and Computer-Integrated Manufacturing, 2020, 63: 101895. [21] TAO F, ZHANG H, LIU A, et al. Digital twin in industry: state-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2405-2415. [22] TUHAISE V V, TAH J H M, ABANDA F H. Technologies for digital twin applications in construction[J]. Automation in Construction, 2023, 152: 104931. [23] SHAFTO M, CONROY M, DOYLE R, et al. Modeling, simulation, information technology and processing roadmap[R]. Washington DC: National Aeronautics and Space Administration, 2010. [24] KNAPP G L, MUKHERJEE T, ZUBACK J S, et al. Building blocks for a digital twin of additive manufacturing[J]. Acta Materialia, 2017, 135: 390-399. [25] 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18.TAO Fei, LIU Wei-ran, LIU Jian-hua, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18. [26] 刘大同, 郭凯, 王本宽, 等. 数字孪生技术综述与展望[J]. 仪器仪表学报, 2018, 39(11): 1-10.LIU Da-tong, GUO Kai, WANG Ben-kuan, et al. Summary and perspective survey on digital twin technology[J]. Chinese Journal of Scientific Instrument, 2018, 39(11): 1-10. [27] BRUYNSEELS K, SANTONI DE SIO F, VAN DEN HOVEN J. Digital twins in health care: ethical implications of an emerging engineering paradigm[J]. Frontiers in Genetics, 2018, 9: 31. [28] GVRDVR BROO D, BRAVO-HARO M, SCHOOLING J. Design and implementation of a smart infrastructure digital twin[J]. Automation in Construction, 2022, 136: 104171. [29] BOLTON R N, MCCOLL-KENNEDY J R, CHEUNG L, et al. Customer experience challenges: bringing together digital, physical and social realms[J]. Journal of Service Management, 2018, 29(5): 776-808. [30] BATTY M. Digital twins[J]. Environment and Planning B: Urban Analytics and City Science, 2018, 45(5): 817-820. [31] TCHANA Y, DUCELLIER G, REMY S. Designing a unique digital twin for linear infrastructures lifecycle management[J]. Procedia CIRP, 2019, 84: 545-549. [32] COSTIN A, ADIBFAR A, HU H J, et al. Building information modeling (BIM) for transportation infrastructure-literature review, applications, challenges, and recommendations[J]. Automation in Construction, 2018, 94: 257-281. [33] SACKS R, BRILAKIS I, PIKAS E, et al. Construction with digital twin information systems[J]. Data-Centric Engineering, 2020, 1: e14. [34] MAHMOODIAN M, SHAHRIVAR F, SETUNGE S, et al. Development of digital twin for intelligent maintenance of civil infrastructure[J]. Sustainability, 2022, 14(14): 8664. [35] XIONG Z M, GUO J T, XIA Y P, et al. A 3D Multi-scale geology modeling method for tunnel engineering risk assessment[J]. Tunnelling and Underground Space Technology, 2018, 73: 71-81. [36] BOSCHERT S, ROSEN R. Digital Twin: the simulation aspect[M]. Berlin: Springer International Publishing, 2016. [37] RǎILEANU S, BORANGIU T, IVǎNESCU N, et al. Integrating the digital twin of a shop floor conveyor in the manufacturing control system[C]//Springer. Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, Proceedings of SOHOMA. Berlin: Springer, 2020: 134-145. [38] BORANGIU T, OLTEAN E, RǎILEANU S, et al. Embedded digital twin for ARTI-type control of semi-continuous production processes[C]//Springer. Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, Proceedings of SOHOMA. Berlin: Springer, 2020: 113-133. [39] TAO F, ZHANG M. Digital twin shop-floor: A new shop- floor paradigm towards smart manufacturing[J]. IEEE Access, 2017, 5: 20418-20427. [40] AHELEROFF S, XU X, ZHONG R Y, et al. Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model[J]. Advanced Engineering Informatics, 2021, 47: 101225. [41] KRITZINGER W, KARNER M, TRAAR G, et al. Digital twin in manufacturing: a categorical literature review and classification[J]. IFAC-Papers OnLine, 2018, 51(11): 1016-1022. [42] REDELINGHUYS A J H, BASSON A H, KRUGER K. A six-layer architecture for the digital twin: a manufacturing case study implementation[J]. Journal of Intelligent Manufacturing, 2020, 31(6): 1383-1402. [43] CATBAS N, AVCI O. A review of latest trends in bridge health monitoring[J]. Proceedings of the Institution of Civil Engineers-Bridge Engineering, 2023, 176(2): 76-91. [44] MACPHERSON W N, SILVA-LOPEZ M, BARTON J S, et al. Tunnel monitoring using multicore fibre displacement sensor[J]. Measurement Science and Technology, 2006, 17(5): 1180-1185. [45] KISSINGER T, CHEHURA E, STAINES S E, et al. Dynamic fiber-optic shape sensing using fiber segment interferometry[J]. Journal of Lightwave Technology, 2018, 36(4): 917-925. [46] BANG H J, KIM H I, LEE K S. Measurement of strain and bending deflection of a wind turbine tower using arrayed FBG sensors[J]. International Journal of Precision Engineering and Manufacturing, 2012, 13(12): 2121-2126. [47] YE X W, SU Y H, XI P S. Statistical analysis of stress signals from bridge monitoring by FBG system[J]. Sensors, 2018, 18(2): 491. [48] CHEN P W, CHUNG D D L. Carbon fiber reinforced concrete for smart structures capable of non-destructive flaw detection[J]. Smart Materials and Structures, 1993, 2(1): 22. [49] MOSALAM K M, TAKHIROV S M, PARK S. Applications of laser scanning to structures in laboratory tests and field surveys[J]. Structural Control and Health Monitoring, 2014, 21(1): 115-134. [50] CABO C, ORDÓÑEZ C, ARGVELLES-FRAGA R. An algorithm for optimizing terrestrial laser scanning in tunnels[J]. Automation in Construction, 2017, 83: 163-168. [51] WANG Q, CHENG J C P, SOHN H. Automated estimation of reinforced precast concrete rebar positions using colored laser scan data[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(9): 787-802. [52] XIONG X H, ADAN A, AKINCI B, et al. Automatic creation of semantically rich 3D building models from laser scanner data[J]. Automation in Construction, 2013, 31: 325-337. [53] TANG P B, HUBER D, AKINCI B, et al. Automatic reconstruction of as-built building information models from laser-scanned point clouds: a review of related techniques[J]. Automation in Construction, 2010, 19(7): 829-843. [54] PĂTRĂUCEAN V, ARMENI I, NAHANGI M, et al. State of research in automatic as-built modelling[J]. Advanced Engineering Informatics, 2015, 29(2): 162-171. [55] XU F Y, ZHANG Z A, MENG X M, et al. Axial compressive performance of cruciform timber-encased steel composite columns: experimental investigation and buckling analysis through 3D laser scanning[J]. Thin-Walled Structures, 2024, 201: 111968. [56] NUTTENS T, STAL C, DE BACKER H, et al. Methodology for the ovalization monitoring of newly built circular train tunnels based on laser scanning: Liefkenshoek Rail Link (Belgium)[J]. Automation in Construction, 2014, 43: 1-9. [57] XU X Y, YANG H, NEUMANN I. A feature extraction method for deformation analysis of large-scale composite structures based on TLS measurement[J]. Composite Structures, 2018, 184: 591-596. [58] SÁNCHEZ-RODRÍGUEZ A, RIVEIRO B, SOILÁN M, et al. Automated detection and decomposition of railway tunnels from mobile laser scanning datasets[J]. Automation in Construction, 2018, 96: 171-179. [59] 刘学增, 叶康. 隧道衬砌裂缝的远距离图像测量技术[J]. 同济大学学报(自然科学版), 2012, 40(6): 829-836.LIU Xue-zeng, YE Kang. A long-distance image measuring technique for crack on tunnel lining[J]. Journal of Tongji University (Natural Science), 2012, 40(6): 829-836. [60] RIBEIRO D, MONTENEGRO P A, ANDERSSON A, et al. Digital Railway Infrastructure[M]. Berlin: Springer International Publishing, 2015. [61] KHALOO A, LATTANZI D, CUNNINGHAM K, et al. Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling[J]. Structure and Infrastructure Engineering, 2018, 14(1): 124-136. [62] FENG D M, FENG M Q. Vision-based multipoint displacement measurement for structural health monitoring[J]. Structural Control and Health Monitoring, 2016, 23(5): 876-890. [63] PAN B, TIAN L, SONG X L. Real-time, non-contact and targetless measurement of vertical deflection of bridges using off-axis digital image correlation[J]. NDT & E International, 2016, 79: 73-80. [64] FENG D M, FENG M Q. Computer vision for SHM of civil infrastructure: from dynamic response measurement to damage detection-A review[J]. Engineering Structures, 2018, 156: 105-117. [65] RIBEIRO D, CALÇADA R, FERREIRA J, et al. Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system[J]. Engineering Structures, 2014, 75: 164-180. [66] LU Y Q, LIU C, WANG K I, et al. Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues[J]. Robotics and Computer-Integrated Manufacturing, 2020, 61: 101837. [67] WANG Y S, ZHENG G P, WANG X. Development and application of a goaf-safety monitoring system using multi-sensor information fusion[J]. Tunnelling and Underground Space Technology, 2019, 94: 103112. [68] RIAZ Z, ARSLAN M, KIANI A K, et al. CoSMoS: a BIM and wireless sensor based integrated solution for worker safety in confined spaces[J]. Automation in Construction, 2014, 45: 96-106. [69] QIAN H T. Design of tunnel automatic monitoring system based on BIM and IoT[J]. Journal of Physics: Conference Series, 2021, 1982(1): 012073. [70] WANG C, TAVARES A, FONSECA J, et al. Real-time condition assessment of a painted megalithic cave using Wireless Sensor Network[J]. Tunnelling and Underground Space Technology, 2022, 120: 104270. [71] RAO A S, RADANOVIC M, LIU Y G, et al. Real-time monitoring of construction sites: sensors, methods, and applications[J]. Automation in Construction, 2022, 136: 104099. [72] BANFI F, BRUMANA R, SALVALAI G, et al. Digital twin and cloud BIM-XR platform development: from scan-to-BIM-to-DT process to a 4D multi-user live app to improve building comfort, efficiency and costs[J]. Energies, 2022, 15(12): 4497. [73] LIN Y C, CHEUNG W F. Developing WSN/BIM-based environmental monitoring management system for parking garages in smart cities[J]. Journal of Management in Engineering, 2020, 36(3): 04020012. [74] SOFI A, JANE REGITA J, RANE B, et al. Structural health monitoring using wireless smart sensor network-An overview[J]. Mechanical Systems and Signal Processing, 2022, 163: 108113. [75] MISHRA M, LOURENÇO P B, RAMANA G V. Structural health monitoring of civil engineering structures by using the Internet of Things: a review[J]. Journal of Building Engineering, 2022, 48: 103954. [76] ZUO Z B, HUANG Y L, PAN X, et al. Experimental research on remote real-time monitoring of concrete strength for highrise building machine during construction[J]. Measurement, 2021, 178: 109430. [77] XIE X Y, WANG Q, SHAHROUR I, et al. A real-time interaction platform for settlement control during shield tunnelling construction[J]. Automation in Construction, 2018, 94: 154-167. [78] WANG J, FU Y F, YANG X K. An integrated system for building structural health monitoring and early warning based on an Internet of Things approach[J]. International Journal of Distributed Sensor Networks, 2017, 13(1): 155014771668910. [79] VILLA V, NATICCHIA B, BRUNO G, et al. IoT open- source architecture for the maintenance of building facilities[J]. Applied Sciences, 2021, 11(12): 5374. [80] HOU S T, WU G. A low-cost IoT-based wireless sensor system for bridge displacement monitoring[J]. Smart Materials and Structures, 2019, 28(8): 085047. [81] SARKAR D, PATEL H, DAVE B. Development of integrated cloud-based Internet of Things (IoT) platform for asset management of elevated metro rail projects[J]. International Journal of Construction Management, 2022, 22(10): 1993-2002. [82] DANISH A, TAYYAB F, SALIM M U. Health assessment based on dynamic characteristics of reinforced concrete beam using realtime wireless structural health monitoring sensor[J]. Journal of Structural Integrity and Maintenance, 2020, 5(3): 204-210. [83] JIN R, ZHANG H, LIU D H, et al. IoT-based detecting, locating and alarming of unauthorized intrusion on construction sites[J]. Automation in Construction, 2020, 118: 103278. [84] SAKENA BENAZER S, SHEIK DAWOOD M, KARTHICK RAMANATHAN S, et al. Efficient model for IoT based railway crack detection system[J]. Materials Today: Proceedings, 2021, 45: 2789-2792. [85] ZHANG L Y, ZHOU G C, HAN Y, et al. Application of Internet of Things technology and convolutional neural network model in bridge crack detection[J]. IEEE Access, 2018, 6: 39442-39451. [86] ZRELLI A, KHLAIFI H, EZZEDINE T. Application of damage detection for bridge health monitoring[C]//IEEE. 2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC). New York: IEEE, 2017: 42-46. [87] WANG D, REN B Y, CUI B, et al. Real-time monitoring for vibration quality of fresh concrete using convolutional neural networks and IoT technology[J]. Automation in Construction, 2021, 123: 103510. [88] IYER S, VELMURUGAN T, GANDOMI A H, et al. Structural health monitoring of railway tracks using IoT-based multi-robot system[J]. Neural Computing and Applications, 2021, 33(11): 5897-5915. [89] ALFARRAJ O. Internet of Things with bio-inspired co- evolutionary deep-convolution neural-network approach for detecting road cracks in smart transportation[J]. Neural Computing and Applications, 2020: 1-16. [90] FENG C C, ZHANG H, LI Y L, et al. Efficient real-time defect detection for spillway tunnel using deep learning[J]. Journal of Real-Time Image Processing, 2021, 18(6): 2377-2387. [91] ZHOU C, DING L Y. Safety barrier warning system for underground construction sites using Internet-of-Things technologies[J]. Automation in Construction, 2017, 83: 372-389. [92] YANG J, FAN J, KONG B, et al. Theory and application of new automated concrete curing system[J]. Journal of Building Engineering, 2018, 17: 125-134. [93] PITT J, BOURAZERI A, NOWAK A, et al. Transforming big data into collective awareness[J]. Computer, 2013, 46(6): 40-45. [94] KIM J, KIM H, HAM Y. Mapping local vulnerabilities into a 3D city model through social sensing and the CAVE system toward digital twin city[C]//ASCE. International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience. Reston: ASCE, 2019: 451-458. [95] FAN C, JIANG Y C, MOSTAFAVI A. Social sensing in disaster city digital twin: integrated textual-visual-geo framework for situational awareness during built environment disruptions[J]. Journal of Management in Engineering, 2020, 36(3): 04020002. [96] HAM Y, KIM J. Participatory sensing and digital twin city: updating virtual city models for enhanced risk-informed decision-making[J]. Journal of Management in Engineering, 2020, 36(3): 04020005. [97] AHN C, HAM Y, KIM J, et al. A digital twin city model for age-friendly communities: Capturing environmental distress from multimodal sensory data[C]//IEEE. Proceedings of the 53rd Hawaii International Conference on System Sciences. New York: IEEE. 2020: 1675-1684. [98] FAN C, ZHANG C, YAHJA A, et al. Disaster City Digital Twin: a vision for integrating artificial and human intelligence for disaster management[J]. International Journal of Information Management, 2021, 56: 102049. [99] PAN Y, ZHANG L M. A BIM-data mining integrated digital twin framework for advanced project management[J]. Automation in Construction, 2021, 124: 103564. [100] DANG H V, TATIPAMULA M, NGUYEN H X. Cloud-based digital twinning for structural health monitoring using deep learning[J]. IEEE Transactions on Industrial Informatics, 2022, 18(6): 3820-3830. [101] SEPASGOZAR S M E. Digital twin and web-based virtual gaming technologies for online education: a case of construction management and engineering[J]. Applied Sciences, 2020, 10(13): 4678. [102] AKANMU A A, OLAYIWOLA J, OGUNSEIJU O, et al. Cyber-physical postural training system for construction workers[J]. Automation in Construction, 2020, 117: 103272. [103] ARIF F. Application of virtual reality for infrastructure management education in civil engineering[J]. Education and Information Technologies, 2021, 26(4): 3607-3627. [104] KANG J H, YADAV N, RAMADOSS S, et al. Reliability of distance estimation in virtual reality space: a quantitative approach for construction management[J]. Computers in Human Behavior, 2023, 145: 107773. [105] YITMEN I, ALIZADEHSALEHI S, AKINER İ, et al. An adapted model of cognitive digital twins for building lifecycle management[J]. Applied Sciences, 2021, 11(9): 4276. [106] TORZONI M, TEZZELE M, MARIANI S, et al. A digital twin framework for civil engineering structures[J]. Computer Methods in Applied Mechanics and Engineering, 2024, 418: 116584. [107] KOLLER D, FRIEDMAN N. Probabilistic Graphical Models: Principles and Techniques[M]. Cambridge: MIT Press, 2009. [108] CHA Y J, ALI R, LEWIS J, et al. Deep learning-based structural health monitoring[J]. Automation in Construction, 2024, 161: 105328. [109] ZHAO H T, SUN S Y, JIN B. Sequential fault diagnosis based on LSTM neural network[J]. IEEE Access, 2018, 6: 12929-12939. [110] CHA Y J, CHOI W, BVYVKÖZTVRK O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361-378. [111] CHEN Z K, CHEN D S, ZHANG X, et al. Learning graph structures with transformer for multivariate time-series anomaly detection in IoT[J]. IEEE Internet of Things Journal, 2022, 9(12): 9179-9189. [112] FRANCISCO A, MOHAMMADI N, TAYLOR J E. Smart city digital twin-enabled energy management: toward real-time urban building energy benchmarking[J]. Journal of Management in Engineering, 2020, 36(2): 04019045. [113] KAEWUNRUEN S, PENG S J, PHIL-EBOSIE O. Digital twin aided sustainability and vulnerability audit for subway stations[J]. Sustainability, 2020, 12(19): 7873. [114] YU G, WANG Y, MAO Z Y, et al. A digital twin-based decision analysis framework for operation and maintenance of tunnels[J]. Tunnelling and Underground Space Technology, 2021, 116: 104125. [115] 陶飞, 张贺, 戚庆林, 等. 数字孪生模型构建理论及应用[J]. 计算机集成制造系统, 2021, 27(1): 1-15.TAO Fei, ZHANG He, QI Qing-lin, et al. Theory of digital twin modeling and its application[J]. Computer Integrated Manufacturing Systems, 2021, 27(1): 1-15. [116] ERHARTER G H, HANSEN T F, LIU Z Q, et al. Reinforcement learning based process optimization and strategy development in conventional tunneling[J]. Automation in Construction, 2021, 127: 103701. [117] KEDIR N S, SOMI S, FAYEK A R, et al. Hybridization of reinforcement learning and agent-based modeling to optimize construction planning and scheduling[J]. Automation in Construction, 2022, 142: 104498. [118] ZHANG G H, CHEN W, JIAO Y Y, et al. A failure probability evaluation method for collapse of drill-and-blast tunnels based on multistate fuzzy Bayesian network[J]. Engineering Geology, 2020, 276: 105752. [119] ZHOU J, ZHU S L, QIU Y G, et al. Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm[J]. Acta Geotechnica, 2022, 17(4): 1343-1366. [120] ALMAHAMEED B A, BISHARAH M. Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management[J]. Asian Journal of Civil Engineering, 2024, 25(2): 1281-1294. [121] LIN J C, LV Q, YU D H, et al. Optimized scheduling of resource-constraints in projects for smart construction[J]. Information Processing & Management, 2022, 59(5): 103005. [122] LONG L D, OHSATO A. A genetic algorithm-based method for scheduling repetitive construction projects[J]. Automation in Construction, 2009, 18(4): 499-511. [123] JIANG C, LI X, LIN J R, et al. Adaptive control of resource flow to optimize construction work and cash flow via online deep reinforcement learning[J]. Automation in Construction, 2023, 150: 104817. [124] ASGHARI V, WANG Y Y, BIGLARI A J, et al. Reinforcement learning in construction engineering and management: a review[J]. Journal of Construction Engineering and Management, 2022, 148(11): 03122009. [125] YAO Y, TAM V W Y, WANG J, et al. Automated construction scheduling using deep reinforcement learning with valid action sampling[J]. Automation in Construction, 2024, 166: 105622. [126] LIANG D, ZHANG S J, HUANG H B, et al. Deep learning-based detection and condition classification of bridge elastomeric bearings[J]. Automation in Construction, 2024, 166: 105680. [127] LEE D M, LEE S, MASOUD N, et al. Digital twin-driven deep reinforcement learning for adaptive task allocation in robotic construction[J]. Advanced Engineering Informatics, 2022, 53: 101710. [128] ZHANG X L, NGUYEN H, BUI X N, et al. Evaluating and predicting the stability of roadways in tunnelling and underground space using artificial neural network-based particle swarm optimization[J]. Tunnelling and Underground Space Technology, 2020, 103: 103517. [129] GONDIA A, SIAM A, EL- DAKHAKHNI W, et al. Machine learning algorithms for construction projects delay risk prediction[J]. Journal of Construction Engineering and Management, 2020, 146(1): 04019085. [130] ZHOU Y, SU W J, DING L Y, et al. Predicting safety risks in deep foundation pits in subway infrastructure projects: support vector machine approach[J]. Journal of Computing in Civil Engineering, 2017, 31(5): 04017052. [131] ALIBRANDI U. Risk-informed digital twin of buildings and infrastructures for sustainable and resilient urban communities[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2022, 8(3): 04022032. [132] MANNINO A, DEJACO M C, RE CECCONI F. Building information modelling and Internet of Things integration for facility management: literature review and future needs[J]. Applied Sciences, 2021, 11(7): 3062. [133] CHEUNG W F, LIN T H, LIN Y C. A real-time construction safety monitoring system for hazardous gas integrating wireless sensor network and building information modeling technologies[J]. Sensors, 2018, 18(2): 436. [134] YUAN X, ANUMBA C J, PARFITT M K. Cyber-physical systems for temporary structure monitoring[J]. Automation in Construction, 2016, 66: 1-14. [135] ZOU Y, KIVINIEMI A, JONES S W, et al. Risk information management for bridges by integrating risk breakdown structure into 3D/4D BIM[J]. KSCE Journal of Civil Engineering, 2019, 23(2): 467-480. [136] KAEWUNRUEN S, SRESAKOOLCHAI J, MA W T, et al. Digital twin aided vulnerability assessment and risk-based maintenance planning of bridge infrastructures exposed to extreme conditions[J]. Sustainability, 2021, 13(4): 2051. [137] LIU Z S, BAI W Y, DU X L, et al. Digital twin-based safety evaluation of prestressed steel structure[J]. Advances in Civil Engineering, 2020, 2020(1): 8888876. [138] LIU Z S, MENG X T, XING Z Z, et al. Digital twin-based intelligent safety risks prediction of prefabricated construction hoisting[J]. Sustainability, 2022, 14(9): 5179. [139] BOSCHERT S, HEINRICH C, ROSEN R. Next generation digital twin[C]//HORVÁTH I, SUÁREZ RIVERO J P, HERNÁNDEZ CASTELLANO P M. Proceedings of TMCE 2018. Las Palmas de Gran Delft: TMCE Organizing Committee, 2018: 209-218. [140] HOSAMO H H, SVENNEVIG P R, SVIDT K, et al. A digital twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics[J]. Energy and Buildings, 2022, 261: 111988. [141] HODAVAND F, RAMAJI I J, SADEGHI N. Digital twin for fault detection and diagnosis of building operations: a systematic review[J]. Buildings, 2023, 13(6): 1426. [142] LI T T, ZHAO Y, ZHANG C B, et al. A knowledge-guided and data-driven method for building HVAC systems fault diagnosis[J]. Building and Environment, 2021, 198: 107850. [143] PENG Y, LIN J R, ZHANG J P, et al. A hybrid data mining approach on BIM-based building operation and maintenance[J]. Building and Environment, 2017, 126: 483-495. [144] JIANG F, MA L, BROYD T, et al. Digital twin and its implementations in the civil engineering sector[J]. Automation in Construction, 2021, 130: 103838. [145] ZHAO Y H, WANG N Q, LIU Z S, et al. Construction theory for a building intelligent operation and maintenance system based on digital twins and machine learning[J]. Buildings, 2022, 12(2): 87. [146] CHENG J C P, CHEN W W, CHEN K Y, et al. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms[J]. Automation in Construction, 2020, 112: 103087. [147] SHIM C S, DANG N S, LON S, et al. Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model[J]. Structure and Infrastructure Engineering, 2019, 15(10): 1319-1332. [148] LIU Z S, JIANG A T, ZHANG A S, et al. Intelligent prediction method for operation and maintenance safety of prestressed steel structure based on digital twin technology[J]. Advances in Civil Engineering, 2021, 2021(1): 6640198. [149] KANG J S, CHUNG K, HONG E J. Multimedia knowledge-based bridge health monitoring using digital twin[J]. Multimedia Tools and Applications, 2021, 80(26): 34609-34624. [150] WU X Q, ZHANG X N, JIANG Y S, et al. An intelligent tunnel firefighting system and small-scale demonstration[J]. Tunnelling and Underground Space Technology, 2022, 120: 104301. [151] FANG W L, CHEN W Y, LOVE P E D, et al. A status digital twin approach for physically monitoring over-and-under excavation in large tunnels[J]. Advanced Engineering Informatics, 2024, 62: 102648. [152] WU H Y, ZHU Q, GUO Y X, et al. Multi-level voxel representations for digital twin models of tunnel geological environment[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 102887. [153] LI Y Y, XIAO Z H, LI J T, et al. Integrating vision and laser point cloud data for shield tunnel digital twin modeling[J]. Automation in Construction, 2024, 157: 105180. [154] JIANG H R, JIANG A N. Monitoring system for the construction of arch cover method subway station based on DT and IoT[J]. Journal of Sensors, 2022, 2022(1): 1875196. [155] YE Z J, YE Y, ZHANG C P, et al. A digital twin approach for tunnel construction safety early warning and management[J]. Computers in Industry, 2023, 144: 103783. [156] HU M, LIU Y R, SUGUMARAN V, et al. Automated structural defects diagnosis in underground transportation tunnels using semantic technologies[J]. Automation in Construction, 2019, 107: 102929. [157] WU Z H, CHANG Y, LI Q, et al. A novel method for tunnel digital twin construction and virtual-real fusion application[J]. Electronics, 2022, 11(9): 1413. [158] ZHOU C, QIN W B, LUO H B, et al. Digital twin for smart metro service platform: evaluating long-term tunnel structural performance[J]. Automation in Construction, 2024, 167: 105713. [159] WU H N, SHEN S L, CHEN R P, et al. Three-dimensional numerical modelling on localised leakage in segmental lining of shield tunnels[J]. Computers and Geotechnics, 2020, 122: 103549. [160] YAO J F, YANG Y, WANG X C, et al. Systematic review of digital twin technology and applications[J]. Visual Computing for Industry, Biomedicine, and Art, 2023, 6(1): 10. [161] LEE D M, LEE S H, MASOUD N, et al. Integrated digital twin and blockchain framework to support accountable information sharing in construction projects[J]. Automation in Construction, 2021, 127: 103688. [162] WANG S H, XIE X, LI Y, et al. Research on the spatial data intelligent foundation model[J]. arXiv, 2024, DOI: 10.48550/arXiv.2405.19730. [163] LINDMAN J, HORKOFF J, HAMMOUDA I, et al. Emerging perspectives of application programming interface strategy: a framework to respond to business concerns[J]. IEEE Software, 2020, 37(2): 52-59. [164] ZHAO K, LIU Y, ZHOU Y, et al. A hierarchical and self-evolving digital twin (HSE-DT) method for multi-faceted battery situation awareness realisation[J]. Machines, 2025, 13(3): 175. [165] 朱军, 朱庆, 祝兵, 等. 数字孪生驱动的桥梁智能建造方法[J]. 遥感学报, 2024, 28(5): 1340-1349.ZHU Jun, ZHU Qing, ZHU Bing, et al. Intelligent bridge construction method driven by digital twin[J]. National Remote Sensing Bulletin, 2024, 28(5): 1340-1349. [166] 吴明先. 交通基础设施智能建造技术探索与实践[J]. 中国公路, 2021(23): 28-33.WU Ming-xian. Exploration and practice of intelligent construction technology of transportation infrastructure[J]. China Highway, 2021(23): 28-33. [167] 杨静, 李大鹏, 岳清瑞, 等. 建筑与基础设施全寿命周期智能化的研究现状及关键科学问题[J]. 中国科学基金, 2021, 35(4): 620-626.YANG Jing, LI Da-peng, YUE Qing-rui, et al. Key scientific issues and state-of-art in whole-lifecycle intelligentization of buildings and infrastructures[J]. Bulletin of National Natural Science Foundation of China, 2021, 35(4): 620-626. -
图(20) / 表(9)
计量
- 文章访问数: 1782
- HTML全文浏览量: 288
- PDF下载量: 153
- 被引次数: 0
