Creep Parameter Inversion for High CFRDs Based on Improved BP Neural Network Response Surface Method

Abstract

The creep parameters of rockfill materials obtained from engineering analogy method or indoor tests often cannot accurately reflect the long-term deformation of high Concrete Faced Rockfill Dams (CFRDs). This paper introduces an optimized inversion method based on Multi-population Genetic Algorithm improved BP Neural Network and Response Surface Method (MPGA-BPNN RSM). The parameters used for inversion are determined by parameter sensitivity analysis based on the statistical orthogonal test method. MPGA-BPNN RSM, validated by Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and squared correlation coefficient (R2), etc., completely reflects the response between the creep parameters and the settlement calculation values obtained by Finite Element Method (FEM). MPGA optimized the objective function to obtain the optimal creep parameters. The results show that the settlement values of Xujixia CFRD calculated by FEM using the inversion parameters has great consistency with the monitored values both in size and in distribution, suggesting that the model parameters obtained by the introduced creep parameter inversion method are feasible and effective. The introduced method can improve the inversion efficiency and the prediction accuracy in FEM applications.

Current study on PID control method with optimized BP neural network based on particle swarm optimization

“…The value of 𝛿 can control the weight error in the value and make the inertia weight evolve in favor of the expected weight. Additionally, the maximum value 𝜇 𝑚𝑎𝑥 is set to 0.8 and the minimum value 𝜇 𝑚𝑖𝑛 is set to 0.3 [10]. This method proposes to refine the inertia weight of the algorithm from linear decreasing to random weight.…”