基于变形动态预测的岩质边坡力学参数反演方法

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

摘要:

岩体力学参数是岩质边坡稳定性综合评判的重要指标之一。现有参数反演方法主要基于稳定状态下的最终变形值,难以反映实际边坡变形过程中的非线性时变特征。为此,本文提出一种基于边坡变形动态预测的岩体力学参数反演方法。首先,引入乌鸦搜索算法(crow search algorithm,CSA)优化在线贯序极限学习机（online sequential extreme learning machine,OSELM）的权值和阈值参数,分别构建CSA-OSELM变形动态预测和参数反演模型；其次,采用分段3次Hermite插值和小波分解方法预处理实测变形数据,提取趋势项变形；再次,利用变形动态预测模型获取边坡的最终变形值,并代入反演模型输出力学参数；最后,以新疆京希巴拉克矿区南侧边坡工程为例开展验证分析。结果表明:CSA-OSELM模型在预测精度与稳定性方面均优于其他模型；将反演所得力学参数代入数值模型进行正演计算,计算值与实测变形值的平均误差为6.21%,进一步验证了本文方法的实用性和可靠性。研究成果可为实际工程中快速获取岩体力学参数提供一种新的技术途径。

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.