岩体分类
可靠性(半导体)
地质强度指标
蒙特卡罗方法
岩土工程
理论(学习稳定性)
结构工程
极限状态设计
概率逻辑
计算机科学
数学
地质学
工程类
统计
物理
量子力学
机器学习
功率(物理)
作者
Hui Lu,Marte Gutierrez,Eunhye Kim
标识
DOI:10.1016/j.undsp.2022.01.001
摘要
The critical strain concept has been widely used in analytical or numerical approaches to evaluate the stability of underground excavations. Analytical, empirical, and numerical procedures are usually used to determine the critical strain values. This paper presents a reliability assessment procedure for evaluating excavation stability using the empirical approach based on the rock mass classification Q and the first order reliability method (FORM). In contrast to deterministic critical strain values, a probabilistic critical strain, which considers uncertainties in rock mass parameters, was incorporated in a limit state function for reliability analysis. Using the rock mass classification Q, the empirically estimated tunnel stain was included in the limit state function. The critical strain and estimated tunnel strain were probabilistically characterized based on the rock mass classification Q-derived rock mass properties. Monte Carlo simulations were also conducted for comparing the reliability analysis results with those derived from the FORM algorithm. A highway tunnel case study was used to demonstrate the reliability assessment procedure. The effects of the input ground parameter correlations, probability distributions, and coefficients of variation on tunnel reliability were investigated. Results show that uncorrelated and normally distributed input parameters (intact rock strength and elastic modulus) have generated more conservative reliability. The reliability analysis results also show that the tunnel had relatively high reliability (reliability index of 2.78 and probability of failure of 0.27%), indicating the tunnel is not expected to experience instability after excavation. The tunnel excavation stability was assessed using analytical and numerical approaches for comparison. The results were consistent with the reliability analysis using the FORM algorithm's Q-based empirical method.
科研通智能强力驱动
Strongly Powered by AbleSci AI