Thaw settlement (TS) in warm and ice-rich permafrost presents a challenge to highway subgrade stability in the Qinghai–Tibet Engineering Corridor (QTEC). To conduct a regional risk assessment, this study develops a framework coupling multi-source data fusion with Random Forest (RF) machine learning. By connecting site-specific thermo-mechanical simulations with corridor-scale remote sensing predictors, a 30 m resolution thaw settlement zoning map for 13 m wide separated subgrades was generated. The results indicate the following: (1) Thaw settlement exhibits significant spatial variability, with Level III settlement (20–30 cm) being the dominant category, accounting for 40.85% of the total area; Level IV and V settlements are mainly distributed in warm and ice-rich permafrost regions such as the Chumar River, Wuli, and Tuotuo River areas. (2) Mean annual ground temperature (MAGT) and ice content type (ICT) are key factors influencing the spatial settlement pattern, with differentiated dominant mechanisms: 50% of the zones are dominated by ICT, corresponding to higher settlement (26.76–43.31 cm); 35.71% are influenced by both MAGT and ICT; and 14.29% are dominated by MAGT, with lower settlement (16.23–24.19 cm). This suggests a distinct spatial pattern where “high-temperature zones are largely controlled by ice content, while low-temperature zones are controlled by temperature.” (3) Among multi-source remote sensing factors, land surface temperature (LST) and the thawing index (TI) show significant correlations with thaw settlement, confirming their applicability for hazard identification in high-altitude regions. This study provides a scientific reference and decision support for engineering maintenance and route selection on the Qinghai–Tibet Plateau.
Chen et al. (2026) studied this question.