对近年来我国隧道工程数字孪生技术的发展现状进行了系统研究，首先对数字孪生技术的概念、要素、优势进行系统阐述，再针对我国隧道工程数字孪生技术的发展与研究现状进行分析。数据获取及处理、数字孪生建模和连接交互技术作为隧道工程数字孪生关键技术被详细介绍。总结了数字孪生技术在隧道工程设计、施工和运维三阶段中的一些应用场景，并结合近年来一些智慧隧道工程应用数字孪生技术的案例进行了归纳，如深中通道隧道，江苏省太湖隧道、贵州大娄山隧道等。此外，还对隧道全生命周期数字孪生模型的数据特征与数字孪生模型的标准提出了一些见解。目前，隧道行业数字孪生研究还处于起步阶段。尽管隧道工程数字孪生关键技术体系日趋完善，全生命周期应用范围不断扩展，但还面临着数据获取及融合难度大、模型构建及演化复杂、仿真分析与决策支持能力有待提高、缺乏统一应用规范等挑战。针对这些挑战，新型感知与计算技术、智能化技术融合等是今后进一步研究的方向。
The authors conduct a
systematic study on the development status of digital twin technology in China′s tunnel engineering in recent years. First, the concept, elements,
and advantages of digital twin technology are systematically expounded. Then,
the development and research status of digital twin technology in China′s tunnel engineering is analyzed. The key technologies of digital
twin in tunnel engineering, data acquisition and processing, digital twin
modeling, and connection and interaction technologies, are elaborated in
detail. Some application scenarios of digital twin technology in the three
stages of tunnel engineering design, construction operation, and maintenance
are summarized, and based on the cases of some smart tunnel engineering
applications of digital twin technology in recent years, such as the Shenzhen-Zhongshan tunnel, the Taihu tunnel in Jiangsu, and the Daloushan
tunnel in Guizhou, are generalized. Furthermore, some insights are proposed on
the data characteristics of the digital twin model throughout the life cycle of
tunnels and the standards of the digital twin model. Currently, the research on
digital twins in the tunnel industry is still in its infancy. Although the key
technology system for digital twins in tunnel engineering is becoming more and
more mature, and the application scope throughout the life cycle is
continuously expanding, it still faces challenges such as the difficulty of
data acquisition and integration, the complexity of model construction and
evolution, the need to improve simulation analysis and decision support
capabilities, and the lack of unified application standards. In response to
these challenges, the integration of emerging sensing and computing
technologies, as well as intelligent technologies, will be the direction for
future in-depth research.
