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.
Bibliographical noteKAUST Repository Item: Exported on 2021-08-06
Acknowledgements: This work was supported by the open research funds by State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology, China (Grant No. SICGM202101), the Visiting Researcher Fund Program of State Key Laboratory of Water Resources and Hydropower Engineering Science, China (Grant No. 2020SGG06), and National Natural Science Foundation of China (Grant No. 51779021). Y.T. Wang also wish to acknowledge the finance support from the Otto Pregl Foundation of Fundamental Geotechnical Research in Vienna.
ASJC Scopus subject areas
- Mechanics of Materials
- General Materials Science
- Mechanical Engineering