基于机器学习的含充填裂隙岩样断裂行为预测方法

doi:10.13722/j.cnki.jrme.2022.0284

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Abstract Fracture behaviors of rock samples with discontinuities is difficult to quantify and predict. In this study，machine learning method is used to predict mode I fracture toughness and crack propagation mode of rock samples with an infilled fracture. Firstly，the feasibility of the method was verified by comparison of results of notched semi-circle bend test and numerical simulation based on discrete element method，and the dataset which filtered the outliers was generated by a lot of random numerical simulations. Multiple techniques were used in the preprocessing of dataset. Then four classical machine learning models were established. The optimal parameters of the models were obtained by grid search and five-fold cross-validation method. Multiple indicators and graphs were used to verify the prediction performances of the models. Comparison between the four models shows that Multilayer Perceptron(MLP) is the optimal machine learning model. The sensitivity of the MLP model was analyzed and the stability is good. The MLP model was applied to the test samples，which has high prediction accuracy. And a user interface program was developed. The data driven model of rock mechanics based on the machine learning method reduces time cost compared with the traditional test and numerical simulation，which provides a new method and thought for solving traditional rock engineering problems.

Key words： rock mechanics fracture toughness machine learning discrete element method discontinuities

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	Articles by authors
	CHEN Jinfan1
	SHANG Delei1
	2
	3
	ZHAO Zhihong1
	CHEN Zhaowei4

Cite this article:

CHEN Jinfan1,SHANG Delei1,2, et al. Prediction method of fracture behaviors of rock samples with an infilled fracture based on machine learning[J]. , 2023, 42(S1): 3458-3472.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2022.0284 OR https://rockmech.whrsm.ac.cn/EN/Y2023/V42/IS1/3458

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