Back analysis method of slope displacement based on safety monitoring

Abstract

The invention discloses a back analysis method of slope displacement based on safety monitoring. In the method, displacement monitoring data is fully utilized to perform optimized inversion and determine the rock mechanical parameter, the minimum error function between the displacement value of numerical calculation and the monitored displacement value is used as a target function, the modified particle swarm intelligent algorithm is utilized to optimize the target function, the numerical calculation is combined with the modified particle swarm intelligent optimization inversion method, and repeated iteration is carried out to fast converge the target function at a globally optimal solution, therefore, the optimal value of the specific parameter is determined, and a corresponding relationship between the displacement monitoring value and the rock mechanical parameter is established. In a practical project, the displacement value predicted by using the mechanical parameter of inversion perfectly matches with the actually measured displacement value and has little error, which indicates that the inversion method of the invention has relatively good practical application value in the back analysis of rock displacement.

Description

technical field [0001] The invention belongs to the research field of high slope safety monitoring, in particular to a high slope displacement back analysis method based on safety monitoring. Background technique [0002] In slope stability analysis, the accuracy of mechanical parameter selection directly affects the credibility of the analysis results. Using monitoring information to optimize inversion is an effective method to determine material parameters. The minimum value of the calculated displacement value and the monitored displacement value The error function is used as the objective function to approach the optimal value of the undetermined parameters through repeated iterations. [0003] However, related research on back analysis shows that the optimization objective function is a highly complex nonlinear multi-peak function, so it is an ideal way to use global optimization algorithms such as evolutionary algorithms. At present, some scholars have done research i...

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Problems solved by technology

At present, some scholars have done research in this area, and proposed methods of optimizing anti-analysis such as genetic algorithm anti-analysis and simulated annealing anti-analysis, but these methods are either easy to fall into a local minimum in the objective function, or the algorithm is quite complicated, and there are too many optimization parameters. less robust

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