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

doi:10.11947/j.AGCS.2025.20250170

Abstract

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

CLC Number:

P208

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