Application Of Improve PSO Algorithm In Creep Model Parameters Back Analysis

To study the problem of parameter back analysis in the creep model,to solve the security problem of the construction and operation in actual engineering due to improper creep model selection and uncertainty model parameter,proposing the improved particle swarm algorithm based on chaotic mutation and disturbance strategy for studing study selection and parameters of creep model.In view of the improved particle swarm algorithm,using chaotic mutation even a wide range of variation for particle initial position,using the gaussian perturbation and cauchy disturbance particles can update in the global optimal position,using the gaussian disturbance and cauchy disturbance can make the particles updating in the global optimal position,putting forward a kind of CGC-PSO algorithm by chaos mutation and disturbance of gaussian and Cauchy.Through the chaos mutation to make initial population has a more uniform wide randomness,so to increase the population diversity and improve the optimization results a;by using the disturbance of gaussian cauchy,updating the global optimal particle,making it jump out of local optimal value in the late iterations and improved the convergence performance.In view of the problem of parameter back analysis in the creep model,analyzing the selection characteristics of the different creep model,using CGC-PSO algorithm choose different model and back analysis to experimental data,through the analysis of the results to choose the optimal model for solving the model parameter in each point location,and choosing the optimal parameters through error analysis for the model parameters,obtaining the expressions of creep model,in order to solving the problem of practical engineering safety during construction and operation.