Abstract
Abstract
Data insufficiency of input rock properties is a major issue to analyze the stability of slopes via traditional deterministic and reliability approaches. This data insufficiency in the properties arises due to complexities associated with in-situ and lab testing of rocks. The traditional Bayesian approach overcomes this issue by considering uncertainties in model parameters by combining available prior information neglecting the uncertainty associated with the distribution type/probability model. This study proposes a novel Bayesian multimodel inference approach to incorporate the uncertainties associated with probability models/distribution types along with model parameters for rock properties. The approach first identifies a set of candidate probability models and then employs the Bayesian framework to incorporate the parameter uncertainties for each model. The approach is demonstrated for a rock slope case with the potential of structurally controlled planar failure. It is concluded that the approach effectively treats the statistical uncertainties associated with probability model types and parameters with limited data and provides a more realistic stability assessment than the traditional Bayesian approach. Results show that the uncertainty in probability model parameters affects the stability of rock slope much more significantly than model types.