关键词: 岩体精细化描述, 围岩分类, 力学参数, Monte Carlo法, 概率分布特征

Abstract: According to the drawbacks that the evaluation results by different rock mass classification systems are not consistent and the rock mass mechanical parameters between indoor tests and current situation have a great gap, a method named rock mass fine description system is recommended to describe surrounding rock mass structures quantitatively. Firstly, these evaluation indexes of the common rock mass classification systems(RMR、Q、RMi、GSI、BQ、HC) are summarized as some groups, and basic indexes are selected from these groups by the in-detail comparative analysis. Then, some sections of powerhouse in Danggangshan hydropower station are chosen as the research objects; we use the method combining the in-situ rock mass fine geological sketches with post-data mining and fitting, and based on the association relationship between the ranking parameters and the basic ones, these probabilistical distribution models and their relevant parameters of evaluation indexes are able to be required; thus the surrounding rock mass elaborate descriptions of the section can be obtained. Based on the rock mass fine description, lots of random numbers that meeting the probabilistical distribution models and relevant parameters of these evaluation indexes can be produced by the Monte Carlo simulation; and then according to the evaluation idea and rating process of each rock mass classification system, plenty of corresponding random rating-values of these evaluation indexes are also gained; the surrounding rock mass evaluation results and these probabilistical distribution characteristics by six rock mass classification systems can be attained easily by the inductive statistics. At last, these probabilistical distribution characteristics and their relevant parameters of these rock mass mechanical parameters can be decided by the correlation formulas between them and the rock mass classification indexes. This research can provide some references to estimate accurately the rock mass classification and mechanical parameters in some similar projects, and offer the essential primary data for supporting limit state design to the surrounding rock mass.

Key words: rock mass fine description system, rock mass classification, mechanical parameters, Monte Carlo simulation, probabilistical distribution characteristics