ABSTRACT: Rock mechanical properties (e.g., uniaxial compressive strength or UCS, Young’s modulus, and Poisson’s ratio) are important input parameters for geotechnical assessment and excavation designs. Two common methods used to obtain these parameters are laboratory testing and geophysical logging. The former delivers probably the most reliable results, but can be costly and time-consuming and for a lot of the time it is challenging to source sufficient samples. Alternative ways to better predict rock mechanical properties are needed.
In this case study, the XGBoost machine learning algorithm was applied to correlate laboratory and geophysical logging data with the three mechanaical properties of UCS, Young’s modulus, and Poisson’s ratio. The proposed machine learning approach better predicted UCS values with a smaller Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and a larger R2. Similarly, better results were obtained for the Young’s modulus prediction using the XGBoost algorithm. However, poor correlations existed between the inputs of geophysical and Poisson’s ratio, most likely due to the uncertainties associated with the acquisition of Poisson’s ratio data and the nature of this parameter. This study concluded that a machine learning approach has the potential to predict rock mechanical properties more reliably than the conventional methods, and further study is underway to have more quantitative and detailed analysis with more data inputs and other machina learning models.