Multi-source data integration and multi-scale modeling framework for progressive prediction of complex geological interfaces in tunneling

Abstract

A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel construction. However, regional models based on limited survey data represent macroscopic geological environments but not detailed internal geological characteristics, especially at tunnel portals with complex geological conditions. This paper presents a comprehensive methodological framework for refined modeling of the tunnel surrounding rock and subsequent mechanics analysis, with a particular focus on natural space distortion of hard-soft rock interfaces at tunnel portals. The progressive prediction of geological structures is developed considering multi-source data derived from the tunnel survey and excavation stages. To improve the accuracy of the models, a novel modeling method is proposed to integrate multi-source and multi-scale data based on data extraction and potential field interpolation. Finally, a regional-scale model and an engineering-scale model are built, providing a clear insight into geological phenomena and supporting numerical calculation. In addition, the proposed framework is applied to a case study, the Long-tou mountain tunnel project in Guangzhou, China, where the dominant rock type is granite. The results show that the data integration and modeling methods effectively improve model structure refinement. The improved model's calculation deviation is reduced by about 10% to 20% in the mechanical analysis. This study contributes to revealing the complex geological environment with singular interfaces and promoting the safety and performance of mountain tunneling.

Keywords

Mountain tunnel / Geological modeling / Multi-source data / Progressive prediction / Tunnel portals

CRediT authorship contribution statement

Jingxiao Wang: Conceptualization, Methodology, Data curation, Formal analysis, Visualization, Validation, Writing - original draft. Peinan Li: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Funding acquisition. Xiaoying Zhuang: Visualization, Writing - review & editing. Xiaojun Li: Writing - review & editing. Xi Jiang: Writing - review & editing. Jun Wu: Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was supported by the National Natural Science Foundation of China, China (Grant No. 41827807), the “Social Development Project of Science and Technology Commission of Shanghai Municipality, China (Grant No. 21DZ1201105)”, “The Fundamental Research Funds for the Central Universities, China (Grant No. 21D111320)”, and the “Systematic Project of Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, China (Grant No. 2022ZDK018)”.

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