Authors: Yu Wang Adeyemi Emman Aladejare
Publish Date: 2016/05/09
Volume: 49, Issue: 9, Pages: 3559-3573
Abstract
Geological Strength Index GSI is an important parameter for estimating rock mass properties GSI can be estimated from quantitative GSI chart as an alternative to the direct observational method which requires vast geological experience of rock GSI chart was developed from past observations and engineering experience with either empiricism or some theoretical simplifications The GSI chart thereby contains model uncertainty which arises from its development The presence of such model uncertainty affects the GSI estimated from GSI chart at a specific site it is therefore imperative to quantify and incorporate the model uncertainty during GSI estimation from the GSI chart A major challenge for quantifying the GSI chart model uncertainty is a lack of the original datasets that have been used to develop the GSI chart since the GSI chart was developed from past experience without referring to specific datasets This paper intends to tackle this problem by developing a Bayesian approach for quantifying the model uncertainty in GSI chart when using it to estimate GSI at a specific site The model uncertainty in the GSI chart and the inherent spatial variability in GSI are modeled explicitly in the Bayesian approach The Bayesian approach generates equivalent samples of GSI from the integrated knowledge of GSI chart prior knowledge and observation data available from site investigation Equations are derived for the Bayesian approach and the proposed approach is illustrated using data from a drill and blast tunnel project The proposed approach effectively tackles the problem of how to quantify the model uncertainty that arises from using GSI chart for characterization of sitespecific GSI in a transparent mannerThe authors would like to thank the two anonymous reviewers for their constructive and valuable comments which helped to improve the quality of the paper The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region China Project No 9042172 CityU 11200115 and Project No 8730035 CityU8/CRF/13G and a Strategic Research Grant from City University of Hong Kong Project No 7004178 The financial supports are gratefully acknowledged
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