Machine learning approach to predicting the macro-mechanical properties of rock from the meso-mechanical parameters

The determination of fundamental rock mechanical properties, uniaxial compression strength (UCS) and elastic modulus (E), constitutes a pivotal facet of rock engineering design. However, deriving these properties directly from standard laboratory tests on rock core samples can be challenging, especially when dealing with deep highstress rock formations and weak fractured strata. Thus, it is crucial to establish a cost-effective and practical approach for predicting the macro-mechanical properties of rocks in situ. In this study, a machine learning approach was proposed to predict UCS and E by upscaling meso-mechanical parameters at particle scale in lowporosity crystalline rocks. To expand the correlation database of rock meso-macro mechanical properties, the meso-mechanical parameters, including the fracture toughness, tensile strength of the rock crystal interface, and the elastic modulus of rock crystal, were accurately measured, using a newly designed mechanical apparatus and a nanoindentation device. The grain-based models implemented in the combined finite discrete element method (FDEM-GBM) were developed based on these experimental results, and their reliability was validated though standard tests. Subsequently, a database, including 225 groups of data, was established using the numerical method. Five machine learning algorithms were applied to develop prediction models for UCS and E through data training in the database. Excellent performance improvement was achieved through the application of the grid-search method. The results indicate the optimized kernel ridge regression (KRR) and gaussian process regression (GPR) models demonstrated excellent performance with relative average errors of 4.9% and 1.1% in predicting UCS and E, respectively. Finally, the predicted values of UCS and E were compared with the experimental results, validating the feasibility of the optimized KRR and GPR models.

Automated summary

This study proposed a machine learning approach to predict the uniaxial compression strength (UCS) and elastic modulus (E) of rocks. By measuring meso-mechanical parameters and developing grain-based models, a database with 225 groups of data was established for prediction models. The optimized kernel ridge regression (KRR) and gaussian process regression (GPR) models achieved excellent performance in predicting UCS and E.