Machine learning approaches to rock fracture mechanics problems: Mode-I fracture toughness determination

The cracked chevron notched Brazilian disc (CCNBD) specimen is a suggested testing method to measure Mode-I fracture toughness of rocks by ISRM, which is widely adopted in the laboratory experiments. However, sizes of CCNBD rock specimens are uncertain in the laboratory experiments, which leads to be inaccurate in measurement of Mode-I fracture toughness of rocks in tests. In this work, four machine learning approaches, including decision regression tree, random regression forest, extra regression tree and fully-connected neural networks (FCNNs) are developed and their feasibility and value are demonstrated through the analysis and predictions of Mode-I fracture toughness of rocks. It can be found that solutions based on the four machine learning approaches can provide the accurate results for predicting Mode-I fracture toughness of rock by in ISRM-suggested CCNBD rock specimens. The random regression forest is more suitable to predict Mode-I fracture toughness of rocks in ISRM-suggested CCNBD rock tests than others. The reliable functionality and rapid development of machine learning solutions are demonstrated that it is a major improvement over the previous analytical and empirical solutions by this example. When analytical and empirical solutions are not available, machine learning approaches overcome the associated limitations, which provides a substantially way to solve rock engineering problems. • Machine learning provides an alternative way to predict complex physical phenomena. • Four machine learning methods are applied to predict K I of rocks. • Machine learning approaches are able to accelerate data interpolation in measurements of K I of rocks. • Four machine learning solutions of rock K I are compared.