With the acceleration of urbanization, the demand for construction land has increased sharply. High and steep slope excavation has become an important way to provide construction sites, but its stability is directly related to engineering safety and the surrounding environment. Therefore, the difficulty of how to maintain slope stability during excavation is the focus of our current research. In this paper, based on the high and steep rock slope of a special building in a city in southern China, the PSO-BP (particle swarm optimization-back propagation neural network) algorithm is used to predict the support parameters of the slope, whose safety factor does not meet the requirements. The numerical simulation results show that the safety factor after optimized support is 21.1% higher than that before support, the slope dissipation energy is reduced by 53.1%, the displacement in the direction of zz and xx is reduced by 99.3% and 98. 7%, and the fitting degree between the support parameters predicted by the algorithm and the actual support parameters is good, which shows that the algorithm can provide a scientific and reliable basis for optimizing the slope reinforcement parameters, shortening the time of slope reinforcement design and improving the slope stability.

Sliding failure is a common form of instability for reverse rock slopes. The determination of the failure plane for such unstable slopes is currently a key and challenging issue in research. To pinpoint the position of the failure surface with higher accuracy, this paper proposes a key equilibrium model based on the symmetric bidirectional evolutionary structural optimization (BESO) method. This model is based on the basic parameters of the slope, the BESO method, and the limit equilibrium theory. It uses its own algorithm program to search and determine the key failure plane of the slope. At the same time, two rock slope model tests were conducted to verify the effectiveness of this method in slope stability analysis. The calculated results exhibit a good consistency with the experimental outcomes, which confirms the feasibility of using the key equilibrium-bidirectional evolutionary structural optimization (CE-BESO) method for stability evaluation of this type of slopes.

In porous media grouting, highly viscous non-Newtonian fluids often trigger viscous fingering (Saffman–Taylor instability) due to adverse mobility ratios, creating fractal-like preferential channels that severely weaken the reinforcement volume. To overcome this, a novel swirl-driven grouting method is proposed. A 2.5D swirl seepage model is established to derive the analytical solution for tangential velocity spatial decay. Integrating the Capillary Bundle and Herschel–Bulkley models elucidates the nonlinear coupling mechanism of centrifugal force and shear-thinning. The swirl flow creates an in situ centrifugal pump, reshaping pressure gradients and triggering a sudden viscosity plunge via extremely high comprehensive shear rates. Furthermore, a modified Saffman–Taylor dispersion relation is constructed and validated via indoor sandbox experiments. Theoretical analysis yields a conditional critical Swirl Number threshold of approximately 0.6 under the tested parameters to suppress the fractal-like evolution of fingering. Experiments demonstrate that exceeding this threshold transitions the grout from preferential seepage to uniform isotropic diffusion. The measured isotropic index is closely enveloped within the 10% theoretical error band. Consequently, the effective projected area experiences a substantial leap of 83.3% due to synergistically enhanced driving forces and reduced medium resistance. This mechanism fundamentally overcomes viscous fingering and suppresses the fractal-like growth tendency of the displacement front, providing a solid theoretical basis for controlling grout diffusion morphology in underground engineering.