Rock mass mechanical parameter joint solving method and device

Abstract

The invention provides a rock mass mechanical parameter joint solving method and device based on a self-adaptive particle swarm algorithm, and the method and device can achieve the accurate obtaining of multi-region rock mass parameters and the timely evaluation of the operation state of a project. The method comprises the following steps: S1, acquiring monitoring data; s2, adaptive particle swarm algorithm solving: constructing a deformation model based on finite element data and the acquired monitoring data; initializing a particle swarm algorithm; the deformation model serves as a target function, the position information of each particle is input into the target function to obtain a fitness value, the smaller the fitness value is, the higher the closeness degree with the actual deformation and seepage of the rock mass is, and the closer the rock mass parameter is the true value; according to the positions of the particles in the search space, dividing sub-populations according to relative distances, and then determining the number of the sub-populations; carrying out iterative calculation of a particle swarm algorithm; s3, rock mass parameters are obtained, wherein the particles with the minimum fitness value in the whole population are taken out, and the positions of the particles are all solved rock mass mechanical parameters.

Description

technical field [0001] The invention belongs to the technical field of geotechnical engineering, and in particular relates to a joint solution method and device for rock mechanics parameters based on an improved self-adaptive particle swarm algorithm. Background technique [0002] The seepage, deformation and stress state of rock mass are the key contents of geotechnical engineering analysis, and accurate rock mass mechanical parameters are the basis for physical test and numerical simulation analysis. Due to the inhomogeneity of the rock mass, the parameters obtained by indoor or field tests have great discreteness and uncertainty. At the same time, it is impossible to carry out physical tests to determine the parameters of the internal rock mass under normal operating conditions, and it is impossible to capture the parameters that vary with time. Information about changes over time. This problem can be effectively solved by back-analyzing the monitoring data of rock mass ...

AI Technical Summary

Problems solved by technology

[0003] The back analysis algorithm currently used in geotechnical engineering generally needs to manually adjust the parameters repeatedly. In addition, due to the high degree of nonlinearity of the constitutive model and the large number of parameters to be solved, the algorithm is easy to fall into a local optimal solution, and generally can only Solve a small number of parameters, and cannot consider the deformation coordination of multiple areas of the project

These anti-analysis algorithms do not perform well in the case of too much human intervention. Not only is it time-consuming and laborious, but also the results of physical tests and numerical simulation calculations using these parameters are quite different from the monitoring data, and engineering stress, deformation and seepage cannot be accurately obtained. situation, and it is impossible to make engineering predictions

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