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Estimation of rock mass deformation modulus using indirect information from multiple sources

机译:使用来自多个来源的间接信息估算岩体变形模量

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Deformation modulus (E-m) of rock masses is an important design parameter in many rock engineering projects such as tunnels excavation, slopes, and foundations. As direct (field) measurements of Em are time consuming and sometimes impossible to perform, data of E-m is often limited or not available in many rock engineering projects. When there is limited or non-availability of E-m data from rock engineering projects, various indirect information from multiple sources (e.g., different rock mass classifications) often available during preliminary stages of rock engineering projects can be combined to estimate E-m. How to combine various information from multiple sources to estimate values of E-m remains a difficult task. This study aims to address this challenge by developing a Bayesian sequential updating approach, which uses information from multiple sources to estimate E-m. The proposed approach is formulated to combine information from tunneling quality index (Q) and rock mass rating (RMR) obtained during rock mass classifications to estimate E-m, in terms of its statistics and probability distribution. The proposed approach is illustrated using real Q and RMR data as inputs, and it is shown to satisfactorily estimate E-m values. The approach can also be used in big data analytics, by using multiple sources of information to uncover patterns and trends of rock properties and other useful information, especially during investigation into rock engineering projects such as underground excavations.
机译:岩体的变形模量(E-m)是许多岩石工程项目(例如,隧道开挖,边坡和地基)中的重要设计参数。由于对Em的直接(现场)测量非常耗时且有时无法执行,因此在许多岩石工程项目中,E-m的数据通常受到限制或无法获得。当来自岩石工程项目的E-m数据有限或不可用时,可以将在岩石工程项目的初始阶段通常可从多个来源获得的各种间接信息(例如,不同的岩体分类)进行组合,以估算E-m。如何组合来自多个来源的各种信息以估计E-m值仍然是一项艰巨的任务。这项研究旨在通过开发一种贝叶斯顺序更新方法来应对这一挑战,该方法使用来自多个来源的信息来估计E-m。拟议的方法旨在结合隧道质量指数(Q)和岩体质量分类期间获得的岩体额定值(RMR)的信息来估计E-m,包括其统计量和概率分布。使用实际的Q和RMR数据作为输入来说明所提出的方法,并且该方法可以令人满意地估计E-m值。通过使用多种信息源来揭示岩石特性和其他有用信息的模式和趋势,特别是在对地下工程等岩石工程项目进行调查期间,该方法还可用于大数据分析。

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