Low-porosity tight rocks are widely used as building and engineering materials. The freeze–thaw cycle is a common weathering effect that damages building materials in cold climates. Tight rocks are generally supposed to be highly frost-resistant; thus, studies on frost damage in tight sandstone are rare. In this study, we investigated the deterioration in mechanical properties and changes in P-wave velocity with freeze–thaw cycles in a tight sandstone. We also studied changes to its pore structure using nuclear magnetic resonance (NMR) technology. The results demonstrate that, with increasing freeze–thaw cycles, (1) the mechanical strength (uniaxial compressive, tensile, shear strengths) exhibits a similar decreasing trend, while (2) the P-wave velocity and total pore volume do not obviously increase or decrease. (3) Nanopores account for >70% of the pores in tight sandstone but do not change greatly with freeze–thaw cycles; however, the micropore volume has a continuously increasing trend that corresponds to the decay in mechanical properties. We calculated the pressure-dependent freezing points in pores of different diameters, finding that water in nanopores (diameter 5.9 nm control the evolution of frost damage in tight sandstone. We suggest that pore ice grows from larger pores into smaller ones, generating excess pressure that causes frost damage in micropores and then nanopores, which is manifested in the decrease in mechanical properties.

Soil thermal conductivity is a dominant parameter of an unsteady heat-transfer process, which further influences the stability and sustainability of engineering applications in permafrost regions. In this work, a laboratory test for massive specimens is performed to reveal the distribution characteristics and the parameter-influencing mechanisms of soil thermal conductivity along the Qinghai–Tibet Engineering Corridor (QTEC). Based on the measurement data of 638 unfrozen and 860 frozen soil specimens, binary fitting, radial basis function (RBF) neural network and ternary fitting (for frozen soils) prediction models of soil thermal conductivity have been developed and compared. The results demonstrate that, (1) particle size and intrinsic heat-conducting capacity of the soil skeleton have a significant influence on the soil thermal conductivity, and the typical specimens in the QTEC can be classified as three clusters according to their thermal conductivity probability distribution and water-holding capacity; (2) dry density as well as water content sometimes does not have a strong positive correlation with thermal conductivity of natural soil samples, especially for multiple soil types and complex compositions; (3) both the RBF neural network method and ternary fitting method have favorable prediction accuracy and a wide application range. The maximum determination coefficient (R²) and quantitative proportion of relative error within ±10% ( $P_{\pm 10\%}$ ) of each prediction model reaches up to 0.82, 0.88, 81.4% and 74.5%, respectively. Furthermore, because the ternary fitting method can only be used for frozen soils, the RBF neural network method is considered the optimal approach among all three prediction methods. This study can contribute to the construction and maintenance of engineering applications in permafrost regions.