Engineering rock mass in cold regions has obvious freeze–thaw damage as a result of extreme differences in temperature, rainfall, snow, and other factors, which is one of the main causes of frequent geological disasters. Therefore, it is important to investigate the deterioration mechanism and evolution laws of rock mass freeze–thaw damage. Considering a hydropower station’s left bank slope in a cold region, model testing and numerical testing of slope rock mass failure under freeze–thaw conditions are here carried out by developing a generalized model. The results reveal that heat is transmitted from the outside to the inside of the slope and that the rate of temperature change varies with depth; the frost-heave force causes tensile cracks in the rock mass, with crack propagation taking the form of a circular arc; the presence of an original structural plane influences the propagation direction of the frost-heave crack, whereas the freezing rate of the fissure water influences the amplitude and growth rate of the frost-heave force. Additionally, a novel method of measuring frost-heave force is proposed. The largest frost-heave force caused by water–ice phase change is 19.3 kPa, which is equivalent to 3.17 MPa of the actual slope.

For the simple bridge structure, the finite element model established by drawing and elastic mechanics method is accurate. However, when faced with large and complex long-span bridge structures, there are inevitable differences between the finite element model and the physical model, where the model has to be updated. It is problematic that the updating structural matrix cannot be fed back into the existing general finite element calculation software in the traditional structural matrix updating method. In this paper, a parameter-type updating method based on the “Kriging model + swarm intelligence” optimization is proposed. The Kriging model, based on Genetic Algorithm (GA), Bird Mating Optimizer (BMO), and Particle Swarm Optimization algorithm (PSO), is introduced into the finite element model, updating this to correct the design parameters of the finite element model. Firstly, a truss structure was used to verify the effectiveness of the proposed optimization method, and then a cable-stayed bridge was taken as an example. Three methods were used to update the finite element model of the bridge, and the results of the three optimization algorithms were compared and analyzed. The results show that, compared with the other two methods, the GA-based model updating method has the least time due to the small computation. The results of the BMO-based model were time consuming compared to the other two algorithms, and the parameter identification results were better than the GA algorithm. The PSO algorithm-based model updating method to solve the finite element model was repeated, which required a large amount of computation and was more time consuming; however, it had the highest parameter correction accuracy.