Mingqiang Wang

As a support material for mine roadways, shotcrete (SC) exhibits performance limitations in extreme deep-mining environments characterized by high stress and water seepage. Polyoxymethylene (POM) fiber, with its properties of high strength, high modulus, and corrosion resistance, holds potential for application in surrounding rock support of deep roadways. To investigate the effect of POM fiber on the flexural performance of shotcrete, four-point bending tests were conducted on fiber-reinforced concrete specimens with different fiber lengths and dosages. Combined with ABAQUS numerical simulation, damage simulation analysis was performed on each group of specimens, and the stress propagation state of the fibers was tracked. The results show that the flexural strength of polyoxymethylene fiber shotcrete (PFS) increases with the increase in fiber length and dosage, and the influence of fiber dosage is more significant. POM fiber can effectively inhibit the crack development of shotcrete, enhancing its crack resistance and residual strength. The load-deflection curves indicate that PFS exhibits excellent fracture toughness, with the P9L42 group showing the highest flexural strength improvement, reaching an increase of 94%. The numerical simulation results are in good agreement with the experimental conditions, accurately reflecting the damage state and load-deflection response of each group of concrete specimens. Based on the above research, POM fiber is more conducive to meeting the stability requirements of roadway surrounding rock support, providing a scientific basis for the application of PFS in mine roadway surrounding rock support.

Tailings ponds can recycle water resources through the water recirculation system by clarifying and purifying the wastewater discharged from the mining production process. Due to factors such as flooding and heavy rainfall, once a tailings dams burst, the spread of heavy metals in the tailings causes underground and surface water pollution, endangering the lives and properties of people downstream. To effectively assess the potential impact of tailings dams bursting, many problems such as the difficulty of taking values in predicting the volume of silt penetration through empirical formulae, model testing, and numerical simulation need to be solved. In this study, 65 engineering cases were collected to develop a sample dataset containing dam height and storage capacity. The Support Vector Machine (SVM) algorithm was used to develop a nonlinear regression model for tailings discharge volume after tailings dam failure. In addition, the model penalty parameter C and kernel function g were optimized using the powerful global search capability of the Slime Mold Algorithm (SMA) to develop an SMA–SVM prediction model for tailings discharge volume. The results indicate that the volume of tailings discharged increases nonlinearly with increasing dam height and tailings storage capacity. The SMA-SVM model showed higher prediction accuracy compared to the predictions made by the Random Forest (RF), Radial Basis Function (RBF), and Least Squares SVM (LS-SVM) algorithms. The average absolute error in tailings discharge volume compared to actual values was 30,000 m³, with an average relative error of less than 25%. This is very close to practical engineering scenarios. The ability of the SMA-SVM optimization algorithm to produce predictions with minimal error relative to actual values was further confirmed by the combination of numerical simulations. In addition, the numerical simulations revealed the flow characteristics and inundation area of the discharged sediment during tailings dam failure, and the research results can provide reference for water resource protection and downstream safety prevention and control of tailings ponds.