Uncertainty Quantification in Predicting UCS Using Fully Bayesian Gaussian Process Regression with Consideration of Model Class Selection
Chao Song, Tengyuan Zhao · 2023
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.