High geotemperatures are encountered during tunnel construction in areas with complex geological structures, which can seriously affect personnel and equipment in the process of tunnel construction and operation. The Nige tunnel, a deep-buried extra-long tunnel, was selected to monitor the geotemperature during construction. The air, rock, and water temperatures during the tunnel construction were measured at the tunnel face, and the actual temperatures of the rock or water body at the tunnel face were measured by advanced drilling. The variation trends of the water temperature, air temperature, and flow of water with the tunnel mileage were analyzed. The differences in three measured rock temperatures in three advanced drillings were revealed. The results showed that the Nige tunnel had a maximum water temperature of 63.4 °C, maximum rock temperature (Rock T) of 88.8 °C, and maximum air temperature (Air T) of 56.4 °C. Increasing trends of the air, rock, and water temperatures with the tunnel’s horizontal distance and the buried depth (vertical depth) were obvious, and the geotemperature gradient was approximately 7.6 °C per 100 m. Additionally, the variation laws of the construction ambient temperature with time in a complete construction cycle showed four stage characteristics, and each stage presented different mathematical relationships. These findings will provide guidance for the construction of high geotemperature tunnels in future.

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.