Prediction model of rockburst intensity classification based on RF-AHP-Cloud model

doi:10.16265/j.cnki.issn1003-3033.2020.07.025

Abstract

Abstract: In order to accurately and reliably predict rockburst disasters, AHP was optimized based on importance of analysis index of RF, and an RF-AHP weight calculation method was constructed. Then, a prediction model of rockburst intensity classification based on RF-AHP-Cloud model was established. Finally, through literature survey, a database containing 301 groups of engineering instances was established as sample data for rockburst prediction, and prediction results of 25 sets of samples were analyzed. The results show that the proposed model has an prediction accuracy of more than 88%, and it can determine rockburst intensity grade of samples. And rationality of RF-AHP index weight calculation method as core of prediction model is also verified.

Key words: random forest(RF), analytic hierarchy process(AHP), cloud model, rockburst intensity, prediction model

CLC Number:

X936

TIAN Rui, MENG Haidong, CHEN Shijiang, WANG Chuangye, SHI Lei. Prediction model of rockburst intensity classification based on RF-AHP-Cloud model[J]. China Safety Science Journal, 2020, 30(7): 166-172.

References

[1] 冯夏庭,肖亚勋,丰光亮,等.岩爆孕育过程研究[J].岩石力学与工程学报,2019,38(4):649-673.
FENG Xiating, XIAO Yaxun, FENG Guangliang, et al. Study on the development process of rockbursts[J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(4): 649-673.
[2] 黄建,夏元友,吝曼卿.基于改进组合赋权的岩爆多维云模型预测研究[J].中国安全科学学报,2019,29(7):26-32.
HUANG Jian, XIA Yuanyou, LIN Manqing. Study on prediction of rock burst by multi-dimensional cloud model based on improved combined weight[J]. China Safety Science Journal, 2019, 29(7): 26-32.
[3] 周鑫隆,章光,李俊哲,等.灰靶决策理论在岩爆烈度等级评价中的应用[J].中国安全科学学报,2019,29(5):19-24.
ZHOU Xinlong, ZHANG Guang, LI Junzhe, et al. Application of grey target decision-making theory in evaluation of rock burst intensity[J]. China Safety Science Journal, 2019, 29(5): 19-24.
[4] WANG Chunlai, WU Aixiang, LU Hui, et al. Predicting rockburst tendency based on fuzzy matter-element model[J]. International Journal of Rock Mechanics & Mining Sciences, 2015,75: 224-232.
[5] 徐琛,刘晓丽,王恩志,等.基于组合权重-理想点法的应变型岩爆五因素预测分级[J].岩土工程学报,2017,39(12):2 245-2 252.
XU Chen, LIU Xiaoli, WANG Enzhi, et al. Strain mode rockburst prediction and classification based on five-factors criterion and combined weight-ideal point method[J]. Chinese Journal of Geotechnical Engineering, 2017, 39(12): 2 245-2 252.
[6] PU Yuanyuan, DEREK B. A, XU Huawei. Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier[J]. Tunnelling and Underground Space Technology, 2019, 90: 12-18.
[7] 宫凤强,李夕兵.岩爆发生和烈度分级预测的距离判别方法及应用[J].岩石力学与工程学报,2007,26(5):1 012-1 018.
GONG Fengqiang, LI Xibing. A distance discriminant analysis method for prediction of possibility and classification of rockburst and its application[J]. Chinese Journal of Rock Mechanics and Engineering, 2007, 26(5): 1 012-1 018.
[8] 高玮,张飞君.深部地下工程岩爆预测的筛选蚁群聚类算法[J].爆炸与冲击,2012,32(6):568-572.
GAO Wei, ZHANG Feijun. Forecasting of rockburst in deep underground engineering based on abstraction ant colony clustering algorithm[J]. Explosion and Shock Waves, 2012, 32(6): 568-572.
[9] 王迎超,靖洪文,张强,等.基于正态云模型的深埋地下工程岩爆烈度分级预测研究[J].岩土力学,2015,36(4):1 189-1 194.
WANG Yingchao, JING Hongwen, ZHANG Qiang, et al. A normal cloud model-based study of grading prediction of rockburst intensity in deep underground engineering[J]. Rock and Soil Mechanics, 2015, 36(4): 1 189-1 194.
[10] 郝杰,侍克斌,王显丽,等.基于模糊C-均值算法粗糙集理论的云模型在岩爆等级评价中的应用[J].岩土力学,2016,37(3):859-866.
HAO Jie, SHI Kebin, WANG Xianli, et al. Application of cloud model to rating of rockburst based on rough set of FCM algorithm[J]. Rock and Soil Mechanics, 2016, 37(3): 859-866.
[11] ZHOU Keping, LIN Yun, DENG Hongwei, et al. Prediction of rock burst classification using cloud model with entropy weight[J]. Transactions of Nonferrous Metals Society of China, 2016, 26(7): 1 995-2 002.
[12] 过江,张为星,赵岩.岩爆预测的多维云模型综合评判方法[J].岩石力学与工程学报,2018,37(5):1199-1206.
GUO Jiang, ZHANG Weixing, ZHAO Yan. A multidimensional cloud model for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 2018,37(5): 1 199-1 206.
[13] 李绍红,王少阳,朱建东,等.基于权重融合和云模型的岩爆倾向性预测研究[J].岩土工程学报,2018,40(6):1 075-1 083.
LI Shaohong, WANG Shaoyang, ZHU Jiandong, et al. Prediction of rockburst tendency based on weighted fusion and improved cloud model[J].Chinese Journal of Geotechnical Engineering, 2018, 40(6): 1 075-1 083.
[14] 王元汉,李卧东,李启光,等.岩爆预测的模糊数学综合评判方法[J].岩石力学与工程学报,1998,17(5):493-501.
WANG Yuanhan, LI Wodong, LI Qiguang, et al. Method of fuzzy comprehensive evaluations for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 1998, 17(5): 493-501.
[15] 张俊峰.大相岭隧道岩爆灾害分阶段预测与控制技术研究[D].成都:西南交通大学,2010.
ZHANG Junfeng. Study on prediction by stages and control technology of rockburst hazard of Daxiangling highway tunnel[D]. Chengdou: Southwest Jiaotong University, 2010.
[16] 孙臣生.基于改进Matlab-BP神经网络算法的隧道岩爆预测模型[J].重庆交通大学:自然科学版,2019,38(10):41-49.
SUN Chensheng. A prediction model of rockburst in tunnel based on the improved Matlab-BP neural network[J]. Journal of Chongqing Jiaotong University: Natural Science, 2019, 38(10): 41-49.
[17] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[18] 鲁月.基于随机森林因素筛选的国产电影票房组合预测模型研究[D].南京:南京航天航空大学,2019.
LU Yue. Research on factors screening based on random forest and domestic movie box-office combined prediction model[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019.
[19] 李德毅,杜鹢.不确定性人工智能[M].北京:国防工业出版社,2014:44-51.
[20] 黄海生,王汝传.基于隶属云理论的主观信任评估模型研究[J].通信学报,2008,29(4):13-19.
HUANG Haisheng, WANG Ruchuan. Subjective trust evaluation model based on membership cloud theory[J]. Journal on Communications, 2008, 29(4): 13-19.
[21] 谢立军,朱志强,孙磊,等.基于隶属度理论的云服务行为信任评估模型研究[J].计算机应用研究,2013,30(4):1 051-1 054.
XIE Lijun, ZHU Zhiiang, SUN Lei, et al. Research on behavior trust evaluation model of cloud service based on membership theory[J]. Application Research of Computers, 2013, 30(4): 1 051-1 054.
[22] 田睿,孟海东,陈世江,等.基于深度神经网络的岩爆烈度分级预测研究[J].煤炭学报,2020:1-12.
TIAN Rui, MENG Haidong, CHEN Shijiang, et al. Prediction of intensity classification of rockburst based on deep neural network[J].. Journal of China Coal Society, 2020:1-12.