基于岩体分类指数的变形模量统计特征的识别

Identification of Statistical Characteristics of Deformation Modulus Parameters Based on Rock Mass Classification Index

变形模量参数的统计特征是水电站库岸边坡风险评估中的重要设计参数。在施工期库岸边坡的灾害预警中,现场直接测量岩体的变形模量耗时且有时无法实施。当来自现场直接测量的数据有限或不可用时,通常在工程初设阶段可获得各种间接信息来估计变形模量,如岩体分类指数。对此,提出一种贝叶斯更新框架可结合多种间接信息来估计变形模量的统计特征。通过在岩体分类过程中获得三种指数来更新变形模量参数的平均值、标准差和波动范围。对比现场直接测量值及随机场统计特征的变化,使用多种岩体分类指数的贝叶斯更新方案,显示出对参数更有效的识别。

The statistical characteristics of deformation modulus parameters are important design parameters in the risk assessment of reservoir bank slopes in hydropower stations.In early warning of hazards on bank slopes during construction period,direct measurements of the deformation modulus on site are time-consuming and sometimes it is impossible to be implemented.When the data from direct in-situ measurements are limited or unavailable,various indirect information is usually available in the preliminary stages of engineering to estimate deformation moduli,such as rock mass classification indices.This paper proposes a Bayesian updating framework that can combine various indirect information to estimate the statistical characteristics of deformation modulus.The mean,standard deviation and fluctuation range of deformation modulus parameters are updated by three rock mass classification indices.Comparing direct measurements,as well as changes in random field statistical characteristics,a Bayesian update scheme using various rock mass classification indices shows a more efficient identification of the parameters.