A generalized Bayesian approach for prediction of strength and elastic properties of rock

Rock mass elastic and strength properties are needed for calculation of deformation and determination of stability of underground structures. Most available models for prediction of rock mass properties are site-specific, are deterministic, and cannot properly propagate uncertainty in reliability analysis of underground structures. A generalized Bayesian approach is used to develop probabilistic predictive models for the rock mass properties. A set of training and testing databases for rock mass deformation modulus, unconfined compressive strength, and Poisson's ratio based on point load strength index, water content, and geological strength index are also developed. Evaluation of the existing models using our databases show rather large mean absolute percentage errors. Our models, calibrated using training databases, (i) are probabilistic and include model parameter statistics needed for uncertainty propagation in reliability analysis, (ii) are rock-specific, (iii) show smaller prediction errors compared to existing models, and (iv) can be updated as new information become available.