Inversion method for mechanical parameters of rock slopes based on dynamic prediction of deformation

Abstract:

Mechanical parameters of rock mass are one of the important indicators for the comprehensive stability assessment of rock slopes. Existing parameter inversion methods are mainly based on the final deformation values under stable conditions, making it difficult to reflect the nonlinear and time-varying characteristics in the actual slope deformation process. To this end, this paper proposed an inversion method for the mechanical parameters of rock mass based on the dynamic prediction of slope deformation. Firstly, the crow search algorithm (CSA) was introduced to optimize the weight and threshold parameters of the online sequential extreme learning machine (OSELM), and the CSA-OSELM dynamic deformation prediction and parameter inversion models were constructed, respectively. Secondly, piecewise cubic Hermite interpolation and wavelet decomposition methods were adopted to preprocess the measured deformation data to extract the trend term deformation. Thirdly, the dynamic deformation prediction model was used to obtain the final deformation value of the slope, which was substituted into the inversion model to output the mechanical parameters. Finally, verification analysis was carried out by taking the southern slope project of the Jingxi-Barak mining area in Xinjiang as an example. The results show that the CSA-OSELM model outperforms other models in prediction accuracy and stability; by substituting the mechanical parameters obtained from the inversion into the numerical model for forward calculation, the average error between the calculated values and the measured deformation values is 6.21%, which further verifies the practicality and reliability of the method in this paper. The research results can provide a new technical approach for rapidly obtaining mechanical parameters of rock mass in practical engineering.