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

Abstract

Deformation modulus (Em) 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 Em is often limited or not available in many rock engineering projects. When there is limited or non-availability of Em 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 Em. How to combine various information from multiple sources to estimate values of Em 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 Em. 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 Em, 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 Em 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.