Abstract
Estimation of rock mass deformation modulus is the subject of many studies in rock engineering research work. Although numerous predictive models have been developed for the estimation of the deformation modulus, they cannot be generalized for other sites because of inadequate accuracy. Furthermore, it is very valuable that the predictive models involve some accessible input parameters. The rock mass rating (RMR) is a well-known geomechanical parameter, which is usually determined to describe the quality of rock mass in rock engineering projects. In this study, five parameter ratings of the RMR classification system are used to predict the deformation modulus of rock mass in the abutment of the Gotvand earth dam. Statistical analysis and an artificial neural network are employed to present two new predictive models. Finally, probabilistic analysis is used to predict the rock mass deformation modulus, which overcomes the low accuracy caused by the inherent uncertainty in prediction. The results indicated that the parameter ratings used in the RMR classification system can predict the rock mass deformation modulus with a satisfactory correlation. However, the parameters don’t have the same influence on the rock mass deformability with the joint condition and the groundwater as the major and minor influencing parameters, respectively.
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Nejati, H.R., Ghazvinian, A., Moosavi, S.A. et al. On the use of the RMR system for estimation of rock mass deformation modulus. Bull Eng Geol Environ 73, 531–540 (2014). https://doi.org/10.1007/s10064-013-0522-3
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DOI: https://doi.org/10.1007/s10064-013-0522-3