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Probabilistic Analysis of Tunnel Face Stability below River Using Bayesian Framework

机译:贝叶斯框架下河隧道面稳定性的概率分析

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摘要

A key issue in assessment on tunnel face stability is a reliable evaluation of required support pressure on the tunnel face and its variations during tunnel excavation. In this paper, a Bayesian framework involving Markov Chain Monte Carlo (MCMC) simulation is implemented to estimate the uncertainties of limit support pressure. The probabilistic analysis for the three-dimensional face stability of tunnel below river is presented. The friction angle and cohesion are considered as random variables. The uncertainties of friction angle and cohesion and their effects on tunnel face stability prediction are evaluated using the Bayesian method. The three-dimensional model of tunnel face stability below river is based on the limit equilibrium theory and is adopted for the probabilistic analysis. The results show that the posterior uncertainty bounds of friction angle and cohesion are much narrower than the prior ones, implying that the reduction of uncertainty in cohesion and friction significantly reduces the uncertainty of limit support pressure. The uncertainty encompassed in strength parameters are greatly reduced by the MCMC simulation. By conducting uncertainty analysis, MCMC simulation exhibits powerful capability for improving the reliability and accuracy of computational time and calculations.
机译:隧道面稳定性评估的关键问题是对隧道面上所需的支撑压力的可靠评估及其在隧道挖掘过程中的变化。本文实施了涉及马尔可夫链蒙特卡罗(MCMC)模拟的贝叶斯框架,以估计极限支撑压力的不确定性。呈现河流下隧道三维面稳定性的概率分析。摩擦角和内聚力被认为是随机变量。使用贝叶斯方法评估摩擦角和粘合的不确定性及其对隧道面稳定性预测的影响。河下隧道面稳定性的三维模型基于极限平衡理论,采用概率分析。结果表明,摩擦角和内聚力的后部不确定性范围比现有人的窄程度较窄,这意味着在内聚力和摩擦中降低不确定性显着降低了极限支持压力的不确定性。 MCMC仿真大大降低了强度参数所包围的不确定性。通过进行不确定性分析,MCMC仿真表现出强大的能力,可提高计算时间和计算的可靠性和准确性。

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