数据模型知识协同驱动的隧道围岩高精度数字孪生建模方法

doi:10.11947/j.AGCS.2025.20250170

测绘学报 ›› 2025, Vol. 54 ›› Issue (10): 1893-1906.doi: 10.11947/j.AGCS.2025.20250170

数据模型知识协同驱动的隧道围岩高精度数字孪生建模方法

吴浩宇¹(), 朱庆¹(), 丁雨淋¹, 鲍榴¹, 刘利¹^,²

^1.西南交通大学地球科学与工程学院，四川　成都　611756
^2.中铁十八局集团有限公司，天津　300222

收稿日期:2025-04-17 修回日期:2025-10-15 出版日期:2025-11-14 发布日期:2025-11-14
通讯作者: 朱庆 E-mail:haoyu.wu@my.swjtu.edu.cn;zhuqing@swjtu.edu.cn
作者简介:吴浩宇（1998—），男，博士生，研究方向为数字孪生建模。　E-mail：haoyu.wu@my.swjtu.edu.cn
基金资助:
国家自然科学基金(42371436)

High-precision digital twin modeling of tunnel surrounding rock driven by data model knowledge collaboration

Haoyu WU¹(), Qing ZHU¹(), Yulin DING¹, Liu BAO¹, Li LIU¹^,²

^1.Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
^2.China Railway 18th Bureau Group Co., Ltd., Tianjin 300222, China

Received:2025-04-17 Revised:2025-10-15 Online:2025-11-14 Published:2025-11-14
Contact: Qing ZHU E-mail:haoyu.wu@my.swjtu.edu.cn;zhuqing@swjtu.edu.cn
About author:WU Haoyu (1998—), male, PhD candidate, majors in digital twin modelling.　E-mail: haoyu.wu@my.swjtu.edu.cn
Supported by:
The National Natural Science Foundation of China(42371436)

摘要/Abstract

摘要：

隧道围岩高精度数字孪生模型是工程优化设计与安全质量效益多目标精准管控的关键基础。围岩孪生建模主要融合多源异质感知数据对围岩属性和结构进行动态三维建模，由于数据在模态、语义及空间分布等方面存在显著差异，现有建模方法难以自动化、智能化融合处理。为此本文提出数据模型知识协同驱动的建模方法，利用体素结构集成多源数据以统一模态及空间分布，联合地球物理、岩土力学等多模型求解围岩物理力学属性，引入分级规则消除与实测指标的语义差异，动态接入探测数据优化机理模型参数，通过知识图谱引导数据模型知识协同驱动的自动化更新建模，共覆盖16种关键要素。选取典型铁路隧道进行验证，构建了随施工动态更新的0.5 m分辨率围岩体素模型，质量评价指标分类准确率高于85%，相比现有方法提升约10%。

关键词: 数字孪生, 隧道围岩, 知识图谱, 融合建模, 多模态数据

Abstract:

The digital twin (DT) model of tunnel surrounding rock provides a critical foundational support for optimizing engineering design and achieving precise management of multi-objective of safety, quality, and efficiency. DT modeling primarily integrates multi-source heterogeneous sensing data to perform dynamic three-dimensional modeling of surrounding rock properties and structures. Due to significant differences in data modalities, semantics, and spatial distributions, existing methods struggle to achieve automated and intelligent fusion modeling. To address this challenge, a data model knowledge co-driven modeling approach is proposed. This method integrates multi-source data using voxel models to unify modalities and spatial distributions. By combining geophysical and geotechnical models to solve physical and mechanical properties of surrounding rock, classification rules are introduced to eliminate semantic discrepancies with on-site measured indicators. Dynamically integrating data to optimize the parameters of mechanistic models, knowledge graph guides data model knowledge co-drives automatic update modeling, covering 16 key elements. A typical railway tunnel was selected for verification, a 0.5 m resolution surrounding rock voxel model was constructed and dynamically updated with construction. The classification accuracy of quality evaluation indicators exceeded 85%, which is about 10% higher than the existing method.

Key words: digital twin, tunnel surrounding rock, knowledge graph, fusion modelling, multi-modal data

中图分类号:

P208

吴浩宇, 朱庆, 丁雨淋, 鲍榴, 刘利. 数据模型知识协同驱动的隧道围岩高精度数字孪生建模方法[J]. 测绘学报, 2025, 54(10): 1893-1906.

Haoyu WU, Qing ZHU, Yulin DING, Liu BAO, Li LIU. High-precision digital twin modeling of tunnel surrounding rock driven by data model knowledge collaboration[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(10): 1893-1906.

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分类模型	完整程度		坚硬程度		地下水状态
分类模型	训练F₁值	测试F₁值	训练F₁值	测试F₁值	训练F₁值	测试F₁值
支持向量机	0.933 2	0.761 6	0.945 5	0.804 8	0.913 7	0.784 2
朴素贝叶斯	0.938 1	0.780 5	0.950 9	0.799 2	0.925 5	0.768 3
随机森林	0.942 1	0.798 1	0.965 0	0.819 6	0.922 4	0.793 5
本文方法	0.925 4	0.882 9	0.954 8	0.907 6	0.906 4	0.857 1

数据模型知识协同驱动的隧道围岩高精度数字孪生建模方法

High-precision digital twin modeling of tunnel surrounding rock driven by data model knowledge collaboration

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