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Article

Multi-Source-Data-Fusion-Based Susceptibility Assessment of Tunnel Geothermal Hazards: A Case Study of the Nige Tunnel

1
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
2
Power China Guiyang Engineering Corporation Limited, Guiyang 550081, China
3
Power China Chengdu Engineering Corporation Limited, Chengdu 610072, China
4
School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2026, 16(14), 7151; https://doi.org/10.3390/app16147151 (registering DOI)
Submission received: 16 June 2026 / Revised: 12 July 2026 / Accepted: 14 July 2026 / Published: 16 July 2026

Abstract

Tunnel construction in tectonically active mountainous regions is frequently hampered by elevated geothermal conditions, threatening construction safety and long-term infrastructure performance. In the Yunnan–Guizhou Plateau, characterized by complex fault systems and intense hydrothermal activity, rigorous assessment of geothermal hazard susceptibility along tunnel corridors is of critical engineering importance. However, the sparsity of geothermal observational data renders conventional assessment approaches insufficient, as they fail to quantify predictive uncertainty, which is essential for reliable risk decision-making. To address this gap, this study proposes a three-stage framework that integrates multi-source data fusion with Monte Carlo-based uncertainty quantification, using the Nige Tunnel as a case study. Ten conditioning factors were incorporated, with temperature-weighted positive samples constructed from field-surveyed hot springs. Gaussian noise injection and fractal buffer randomization were applied across 500 Monte Carlo iterations of an L2-regularized logistic regression model, evaluated by leave-one-out cross-validation. The three-stage assessment framework achieved robust predictive performance (mean leave-one-out cross-validation area under the curve (LOO-AUC) = 0.824) under sparse-sample conditions. High and Very High susceptibility zones account for 14.2% of the study area, concentrated along fault traces and collocated with hydrothermal discharge locations, with the Nige Tunnel traversing predominantly High to Very High susceptibility zones. Fault distance emerges as the dominant predictive factor, surpassing heat flow and Moho depth, indicating that structural permeability is the rate-limiting control on geothermal fluid enrichment in fault-dominated systems. The findings offer scientific support and methodological insights for risk zoning and hazard mitigation design in tunnel engineering projects in comparable geological settings.
Keywords: geothermal hazard susceptibility; tunnel engineering; Monte Carlo simulation; uncertainty quantification; logistic regression; geothermal hazard simulation geothermal hazard susceptibility; tunnel engineering; Monte Carlo simulation; uncertainty quantification; logistic regression; geothermal hazard simulation

Share and Cite

MDPI and ACS Style

Hu, Z.; Liu, J.; Wang, Z.; Che, W.; Xia, Y.; Zhang, B.; Wu, S.; Zheng, K.; Huang, F.; Zhang, B. Multi-Source-Data-Fusion-Based Susceptibility Assessment of Tunnel Geothermal Hazards: A Case Study of the Nige Tunnel. Appl. Sci. 2026, 16, 7151. https://doi.org/10.3390/app16147151

AMA Style

Hu Z, Liu J, Wang Z, Che W, Xia Y, Zhang B, Wu S, Zheng K, Huang F, Zhang B. Multi-Source-Data-Fusion-Based Susceptibility Assessment of Tunnel Geothermal Hazards: A Case Study of the Nige Tunnel. Applied Sciences. 2026; 16(14):7151. https://doi.org/10.3390/app16147151

Chicago/Turabian Style

Hu, Zheng, Jin Liu, Zhengjie Wang, Wenyue Che, Yong Xia, Bing Zhang, Shuyu Wu, Kexun Zheng, Feng Huang, and Bo Zhang. 2026. "Multi-Source-Data-Fusion-Based Susceptibility Assessment of Tunnel Geothermal Hazards: A Case Study of the Nige Tunnel" Applied Sciences 16, no. 14: 7151. https://doi.org/10.3390/app16147151

APA Style

Hu, Z., Liu, J., Wang, Z., Che, W., Xia, Y., Zhang, B., Wu, S., Zheng, K., Huang, F., & Zhang, B. (2026). Multi-Source-Data-Fusion-Based Susceptibility Assessment of Tunnel Geothermal Hazards: A Case Study of the Nige Tunnel. Applied Sciences, 16(14), 7151. https://doi.org/10.3390/app16147151

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