Pervasive, unavoidable and uncontrollable creep failure generated in soft-rock engineering occasionally happens and therefore attracts extensive attention recently. However, due to soft rock’s multi-factor creep mechanism, it is still difficult to simulate the full-stage creep with the Distinct Element Method (DEM). In this study, we proposed an improved simulation method based on the classical Burger’s model and the Parallel Bonded model in Particle Flow Code (PFC). We apply the abovementioned models together to simulate the full-stage creep process in soft rock. The proposed process has considered the mesoscopic mechanical characteristics of DEM carefully and finally resulted in a parallel physical model, which is called Burger’s Bonded model in this paper. The DEM simulation test using Burger’s Bonded model was designed to compare with experiments. The experiments include a normal creep test and a uniaxial loading test with prefabricated cracks. In contrast to experimental results, the numerical results show that the average error during the whole creep process is less than 3%; the stress–strain curves and crack development process show great agreement. It is also found that the wing crack coalescence in soft rock is independent of the prefabricated crack angle, propagating with a fixed dip angle. The results show that the numerical method proposed in this paper can simulate the multi-factor-caused full stage (attenuated, steady, accelerated) creep process of soft rock in DEM, which provides new insights for theoretical research and engineering design.

The internal damage of red-bed soft rock induced by water is pervasive. The accumulation, growth, and localization of damage is a multi-scale process that can lead to significant strength loss in red-bed soft rock. Yet, research on the critical state of deterioration process considering multi-scale failure is limited due to high degree of system freedom. Renormalization group theory is an effective approach to find critical point of phase transition in a disordered system. To apply renormalization group theory in red-bed soft rocks, this article firstly analyzed their microstructures. Then, the granular unit model and stripy unit model are proposed to describe the self-similar characteristics of red-bed soft rocks. The calculation results based on renormalization group theory are consistent with the experimental results. The critical reductions of strength induced by water are 60% in light-yellow silty mudstone and 80% in grey silty mudstone. In addition, the critical state of damage propagation caused by stress is also studied and the analytical solution is derived. Results show that the renormalization group theory can effectively couple the micro damage and strength deterioration which provides guidance to the engineering.

Granite residual soil is a common engineering material, and its mechanical properties are of great importance to engineering safety. This kind of soil presents obvious structural characteristics, and many researchers have emphasized the significance of its structural features. According to previous experiments, from a macroscopic perspective, many researchers have investigated the structural relationship between undisturbed and remolded soils, but few studies have considered it in the mesoscopic aspect. Adopting DEM (a mesomechanical simulation method), we can study how the structure affects the mechanical process between undisturbed and remolded soil. Therefore, this paper combines DEM with laboratory tests to study the structural characteristic correlation between undisturbed and remolded soil. The results indicate that a weak cohesion effect exists in undisturbed soil, and the damage of weak cohesion elements accompanies the failure process. Weak cohesion elements in undisturbed soil cause inhomogeneities in deformation, stress state, and damage accumulation, which ultimately causes differences in strength curves. This paper explains the mechanism of the structural effect on mechanical evolution from a mesomechanical perspective. The DEM simulation method proposed in this paper can be applied to structured soils and better guide engineering practice.

The mechanical deterioration of soft rocks under freeze–thaw cycles is caused by the accumulation of mesoscopic damage. However, the current freeze–thaw deterioration model for soft rocks does not adequately consider the multiscale correlations, which makes the strength calculation results differ greatly from the test results and cannot fully reveal the damage mechanism of soft rocks under freeze–thaw cycling conditions. In this paper, the bond damage and pore ice expansion laws are considered from the soft-rock mesoscopic bond unit and a multiscale strength deterioration model is proposed. The freeze–thaw deterioration model is extended to intact and cracked soft rocks by the Discrete Element Method (DEM). The results are validated by laboratory tests. The peak strengths of intact soft rocks are calculated within 10% error for different numbers of freeze–thaw cycles, and the macroscopic crack development simulation results are consistent with the laboratory tests. The joints have a significant effect on the damage evolution: the freeze–thaw-induced mesoscopic damage in cracked rocks accumulates at a uniform rate, while the damage in intact soft rocks grows exponentially; the freeze–thaw cracks in cracked soft rocks are distributed between 60 and 90°, with a tensile–shear damage ratio of 1:2; the freeze–thaw cracks in intact soft rocks are distributed around 90°, with a tensile–shear damage ratio of 1:3. The deterioration model proposed in this paper can fully consider the multiscale damage correlations, which renders it easy to promote the application in the freeze–thaw hazard problem of soft rock engineering.

The self-balanced pressure arch theory is an important basis for excavation support in karst caves, but it is difficult to quantify the empirical theory in coastal areas. In addition, the rheological effects of karst strata could pose a hazard to engineering. Therefore, this study investigated the rheological mechanism under the self-balanced pressure arch effect, and we proposed a mesoscopic unit rheological model for clay minerals in a water-rich environment. With the discrete element method (DEM), we realized the numerical modeling of the rheological model. Then, the proposed model is validated by a case study of foundation excavation in the coastal karst area of China. The mesoscopic mechanical characteristics of caves considering the influence of pressure arch are analyzed. The results show that the self-balance of the caverns mainly lies in the mesoscopic mineral strength and local stress. With the rheological controlled model, the final predicted convergent deformation of the foundation has an error of less than 10% compared with the field monitoring data. In this study, we can quantitively describe the self-balance effect of the pressure arch surrounding the caves and reveal the rheological mechanism. The proposed model can be applied to similar engineering with careful calibration and provide safety guidance.

The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal–mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal–mechanical coupling performance of the substation panel under fire.