Uncertainty Quantification in Predicting UCS Using Fully Bayesian Gaussian Process Regression with Consideration of Model Class Selection

The uniaxial compressive strength (UCS) of rocks is used widely in tunneling, rock, and mining engineering. Direct methods for testing UCS of rocks often require well-prepared samples of high quality and therefore are often relatively time-consuming and expensive. In this case, indirect approaches such as empirical equations and machine learning methods are proposed for UCS prediction. Due to the data-driven and non-parametric characteristics of machine learning methods, UCS data of rocks are often estimated through these methods. In this study, one of the machine learning methods, entitled a fully Bayesian Gaussian process regression (fB-GPR), is applied to predict UCS as well as uncertainty quantification associated with the prediction. In the meanwhile, the optimal model for predicting UCS is also determined and examined through a systematical manner. Results show that the fB-GPR method can be well used for accurately predicting UCS with reasonably quantified uncertainty through developing the optimal model.