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Penetration grouting technology is an important technical means to improve the mechanical properties of gravel soil layers, and the time-varying characteristics of Newtonian fluid viscosity have an important influence on the morphology and effect of penetration grouting. However, these time-varying properties are not considered in the current research on the mechanism of Newtonian fluid penetration grouting. In this paper, by studying the basic rheological equation of Newtonian fluids and its dynamic viscosity time-varying law, the penetration motion equation of viscosity time-varying Newtonian fluids is discussed, by means of theoretical analysis and experimental research. Based on this, the time-varying viscosity Newtonian fluid columnar penetration grouting diffusion mechanism (TVNCPGDM) equation is derived, the application scope of the equation is analyzed and a grouting experiment is designed to verify it. The results show that the theoretical value of the grouting diffusion radius calculated by the TVNCPGDM equation, is closer to the experimental value than that obtained by the equation of columnar penetration grouting without considering the viscosity time-varying Newtonian fluid, with a 12.9% improvement in accuracy. This shows that the TVNCPGDM equation derived in this paper, can better reflect the diffusion law and diffusion morphology of column penetration grouting of Newtonian fluid, which changes with time in the injected medium; and the diffusion radius obtained for penetration grouting is more in line with the actual grouting engineering demands. The research results can provide some theoretical guidance for the actual grouting of loose gravel soil layers.

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To gain the equivalent mechanical parameter of rock in underground engineering and to improve the precision of numerical simulation of excavation, a new multiinformation intelligent recognition method of rock mechanical parameter is put forward. By coupling artificial neural network and genetic algorithm as a whole algorithm and by building an associated fitness function to absorb multiinformation, a combinatorial intelligent optimization method is available for parameter recognition. The method realizes the global optimization efficiently and improves the uniqueness of searched parameter as far as possible. After recognizing the equivalent mechanical parameters of rock in Jinping II underground caverns by above method, the performance of surround rock in construction is calculated by 3D numerical simulation with the characters of 1168 thousand elements and 234 gridpoints. The practical application indicates that the intelligent method is reliable to analyze and predict the safety of underground engineering.