Integrated analysis and dynamic assessment of uncertainty in geological modeling for tunnel engineering
WANG Jingxiao1, LI Peinan1, 2*, ZHU Hehua1, 3, LI Xiaojun1,3, YAN Zhiguo1, 3, WU Wei1, 3, RUI Yi1, 3, ZHANG Zhongjie4
(1. College of Civil Engineering, Tongji University, Shanghai 200092, China; 2. College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China; 3. State Key Laboratory of Disaster Reduction in Civil Engineering,
Tongji University, Shanghai 200092, China; 4. Shanghai Urban Construction Design and Research
Institute (Group) Co., Ltd., Shanghai 200125, China)
Abstract：Three-dimensional geological modeling serves as a fundamental basis for tunnel design and construction decision-making. However, the complexity and limited observability of geological bodies inevitably expose geological models to various uncertainties. Additionally, tunneling exhibits distinct stages and a sequential nature, wherein geological understanding deepens progressively as excavation advances, necessitating more advanced approaches to characterize and update these uncertainties. Consequently, a methodological framework for the integrated analysis and dynamic assessment of multi-source geological uncertainties in tunnel engineering has been developed. To systematically represent uncertainty in the modeling process, it is categorized into data uncertainty and methodological uncertainty based on its source. The former employs the quasi-Monte Carlo (QMC) method to perform random sampling of input data, generating multiple sample sets that capture the fluctuation features and uncertainty range of the original data. The latter applies the sequential Gaussian simulation (SGS) technique to stochastically interpolate geological variables, producing various possible spatial distributions to reflect the propagation of estimation errors and spatial variability. As excavation progresses, new geological information is continuously incorporated into the framework to revise and update prior knowledge. In this process, the data uncertainty in regional and tunnel face information is integrated using Bayesian Inference (BI) to update the probabilistic distribution of geological interfaces. The methodological uncertainty is addressed using the Minimum cross-entropy (MCE) principle, which optimizes the distribution while preserving global probabilistic characteristics and ensuring consistency with new information. Furthermore, the updated uncertainty analysis results are integrated into rock mass quality evaluation, leading to the development of an improved G-RMR classification system that incorporates a geological local variability index. To verify the engineering applicability and effectiveness of the proposed method, a case study was conducted on the Longtou Mountain Tunnel in Guangzhou, China. The results show that the regional multi-source uncertainty analysis reveals the spatial variability and fluctuation intensity of the geological structure. Following the update, uncertainty regarding the stratigraphic interface position within the local area is significantly reduced, with variance generally decreasing by approximately 40%–70%, while remaining robust under complex geological conditions. The G-RMR classification results are consistent with on-site adjustments and exhibit heightened sensitivity to variations in surrounding rock, providing a reliable reference for risk identification and supporting optimization during the construction stage.
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2026, Vol. 45 
