基于Stacking集成算法的岩爆等级预测研究

doi:10.13722/j.cnki.jrme.2021.0831

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Abstract Rockburst is a huge disaster faced by underground engineering，and rockburst prediction can reduce the harm caused by rockburst. Machine learning is the research hotspot and development direction of rockburst prediction methods，However，at present，each machine learning algorithm performs differently，and works independently of each other. Without fusion，they cannot complement each other?s advantages，resulting in low accuracy，generalization and stability of all machine learning algorithms. In this paper，eight machine learning algorithms(four ensemble algorithms and four basic algorithms) which are widely used at present are integrated by the Stacking integration algorithm，so as to give full play to the advantages of each algorithm and realize the complementary advantages. In order to ensure the diversity of new feature information，combined with the principles of various machine learning algorithms and the characteristics of rockburst sample library，three sets of Stacking integration algorithms with multiple rockburst prediction indices were proposed，each of which had different base models and multiple meta-models. The difficulty of accepting feature information and selecting meta-models in traditional Stacking integration algorithm is solved. The accuracy，accuracy，recall，and F1 values of each Stacking algorithm and independent algorithm were compared and analyzed，the results show that the Stacking algorithm can effectively integrate all the machine learning algorithms，and the prediction performance is significantly improved. Among the three sets of Stacking algorithms，the base-model of Stacking algorithm 2 is composed of Random Forest Classifier，Extra Trees Classifier，Gradient Boosting Classifier，and LGBMClassifier integration algorithms with high prediction accuracy，and the precision of new feature information provided by the base-model is the highest，and the prediction improvement of each meta-model is the most significant. Among the three sets of Stacking algorithms，the accuracy of the SVC algorithm and the LGBMClassifier algorithm in the meta-model of Stacking algorithm 2 is the highest in the three sets of meta-models. The Stacking ensemble algorithm can learn more feature information by using different groups of meta-models with good performance to vote for rockburst prediction. The accuracy of the test set is finally stable at 94.12 %，which is better than the prediction performance of independent machine learning and common theoretical criteria(the highest accuracy is 91.18 % and 53.9 %，respectively). Finally，the rock burst prediction of Zhongnanshan tunnel shaft is carried out by the established Stacking integration algorithm，and the prediction results are consistent with the actual situation.

Key words： rock mechanics rockburst prediction Stacking integration algorithm algorithm fusion machine learning

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	Articles by authors
	LIU Dejun1
	2
	DAI Qingqing1
	ZUO Jianping1
	2
	SHANG Qi1
	CHEN Guoliang1
	GUO Yihao1

Cite this article:

LIU Dejun1,2,DAI Qingqing1, et al. Research on rockburst grade prediction based on stacking integrated algorithm[J]. , 2022, 41(S1): 2915-2926.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2021.0831 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/IS1/2915

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