The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

Freezing–thawing actions can affect the mechanical features of soil greatly, which is vital for the stability of soil slope in cold regions. Firstly, triaxial compression tests on sand samples under undrained conditions were performed to investigate the influences of freezing–thawing cycles, which shows that the freezing–thawing actions can weaken their strength and stiffness, and with the increasing freezing–thawing cycles, both the deviatoric stress and pore water pressure decrease gradually. Then, the double hardening constitutive model was revised to model the influences of freezing–thawing cycles in consideration of the influences of freezing–thawing actions, and the model was also validated by the test results. Finally, the proposed constitutive model was incorporated into a finite element code to numerically simulate the distribution of displacement and pore water pressure of sand slope subjected to freezing–thawing cycles, which shows that the freezing–thawing actions accelerate the dissipation of the pore water pressure and enlarge the displacement of the slope. The study here can provide a help in designing and construction of civil engineering in cold regions.