Rockburst prediction based on oversampling and objective weighting method

TANG Zhili; WANG Xue; XU Qianjun

doi:10.16511/j.cnki.qhdxxb.2021.22.013

PDF(7941 KB)

Journal of Tsinghua University(Science and Technology) ›› 2021, Vol. 61 ›› Issue (6) : 543-555. DOI: 10.16511/j.cnki.qhdxxb.2021.22.013

DISASTER PREVENTION AND MITIGATION

Rockburst prediction based on oversampling and objective weighting method

{{article.zuoZhe_EN}}

Author information +

History +

Abstract

Machine learning rockburst predictions seek to solve the data imbalance problem and improve the prediction accuracy and model generalization. This work optimized a rockburst prediction algorithm using a dataset containing 246 rockburst cases and 6 rockburst indicators, B₁, MTS, SCF, UCS, UTS and W_et. Nine rockburst prediction models were developed based on machine learning algorithms to study the effects of 5 oversampling methods and 5 objective weighting methods on the model predictions. The results show that data oversampling improves the model accuracy by 11.8% to 52.3% and increases the macro average F₁ by 13.0% to 50.0%. Random oversampling gives the best improvements to resolve the data imbalance problem. After randomly oversampling the data set, objective date weighting only increases the accuracy and the macro average F₁ of the extreme gradient boosting algorithm (XGBoost) model by 1.1% and 1.2%, the random forest (RF) model by 2.1% and 2.3%, the decision tree (DT) model by 10.7% and 11.8%, and the extremely randomized trees (ET) model by 12.9% and 12.8%. The multi-layer perception (MLP) algorithm model based on random oversampling is the best rockburst prediction model with an accuracy of 0.917 and a macro average F₁ of 0.920.

Key words

rockburst prediction / machine learning / data imbalance / oversampling / objective weighting method

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

TANG Zhili, WANG Xue, XU Qianjun. Rockburst prediction based on oversampling and objective weighting method[J]. Journal of Tsinghua University(Science and Technology). 2021, 61(6): 543-555 https://doi.org/10.16511/j.cnki.qhdxxb.2021.22.013

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 X T, XIAO Y X, FENG G L, et al. Study on the development process of rockbursts[J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(4):649-673. (in Chinese)
[2] 徐林生, 王兰生, 李天斌. 国内外岩爆研究现状综述[J]. 长江科学院院报, 1999, 16(4):24-27. XU L S, WANG L S, LI T B. Present situation of rockburst research at home and abroad[J]. Journal of Yangtze River Scientific Research Institute, 1999, 16(4):24-27. (in Chinese)
[3] COOK N G W. The failure of rock[J]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1965, 2(4):389-403.
[4] ZHOU J, LI X B, MITRI H S. Evaluation method of rockburst:State-of-the-art literature review[J]. Tunnelling and Underground Space Technology, 2018, 81:632-659.
[5] WANG J A, PARK H D. Comprehensive prediction of rockburst based on analysis of strain energy in rocks[J]. Tunnelling and Underground Space Technology, 2001, 16(1):49-57.
[6] TAO Z Y. Support design of tunnels subjected to rockbursting[C]//ISRM International Symposium. Madrid, Spain, 1988:407-411.
[7] ZHANG J J, FU B J, LI Z K, et al. Criterion and classification for strain mode rockbursts based on five-factor comprehensive method[C]//Harmonising Rock Engineering and the Environment:Proceedings of the 12th ISRM International Congress on Rock Mechanics. Beijing, 2011:1435-1440.
[8] 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 Y H, LI W D, LI Q G, et al. Method of fuzzy comprehensive evaluations for rockburst prediction[J]. Chinese Journal of Rock Mechanics and Engineering, 1998, 17(5):493-501. (in Chinese)
[9] CHEN B R, FENG X T, LI Q P, et al. Rock burst intensity classification based on the radiated energy with damage intensity at Jinping II Hydropower Station, China[J]. Rock Mechanics and Rock Engineering, 2015, 48(1):289-303.
[10] AUBERTIN M, GILL D E, SIMON R. On the use of the brittleness index modified (BIM) to estimate the post-peak behavior of rocks[C]//Proceedings of the 1st North American Rock Mechanics Symposium:Models and Measurements Challenges from Industry. Austin, USA, 1994:945-952.
[11] PU Y Y, APEL D B, LIU V, et al. Machine learning methods for rockburst prediction-state-of-the-art review[J]. International Journal of Mining Science and Technology, 2019, 29(4):565-570.
[12] TANG L Z, XIA K W. Seismological method for prediction of areal rockbursts in deep mine with seismic source mechanism and unstable failure theory[J]. Journal of Central South University of Technology, 2010, 17(5):947-953.
[13] ZHOU J, LI X B, SHI X Z. Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines[J]. Safety Science, 2012, 50(4):629-644.
[14] LI N, JIMENEZ R. A logistic regression classifier for long-term probabilistic prediction of rock burst hazard[J]. Natural Hazards, 2018, 90(1):197-215.
[15] FENG X T, WANG L N. Rockburst prediction based on neural networks[J]. Journal of Railway Science and Engineering, 1994, 4(1):7-14.
[16] ADOKO A C, GOKCEOGLU C, WU L, et al. Knowledge-based and data-driven fuzzy modeling for rockburst prediction[J]. International Journal of Rock Mechanics and Mining Sciences, 2013, 61:86-95.
[17] AFRAEI S, SHAHRIAR K, MADANI S H. Statistical assessment of rock burst potential and contributions of considered predictor variables in the task[J]. Tunnelling and Underground Space Technology, 2018, 72:250-271.
[18] 葛启发, 冯夏庭. 基于AdaBoost组合学习方法的岩爆分类预测研究[J]. 岩土力学, 2008, 29(4):943-948. GE Q F, FENG X T. Classification and prediction of rockburst using AdaBoost combination learning method[J]. Rock and Soil Mechanics, 2008, 29(4):943-948. (in Chinese)
[19] 王迎超, 尚岳全, 孙红月, 等. 基于功效系数法的岩爆烈度分级预测研究[J]. 岩土力学, 2010, 31(2):529-534. WANG Y C, SHANG Y Q, SUN H Y, et al. Study of prediction of rockburst intensity based on efficacy coefficient method[J]. Rock and Soil Mechanics, 2010, 31(2):529-534. (in Chinese)
[20] ZHOU J, LI X B, MITRI H S. Classification of rockburst in underground projects:Comparison of ten supervised learning methods[J]. Journal of Computing in Civil Engineering, 2016, 30(5):04016003.
[21] AFRAEI S, SHAHRIAR K, MADANI S H. Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 2:Designing classifiers[J]. Tunnelling and Underground Space Technology, 2019, 84:522-537.
[22] AFRAEI S, SHAHRIAR K, MADANI S H. Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 1:Literature review and data preprocessing procedure[J]. Tunnelling and Underground Space Technology, 2019, 83:324-353.
[23] LI N, JIMENEZ R, FENG X D. The influence of Bayesian networks structure on rock burst hazard prediction with incomplete data[J]. Procedia Engineering, 2017, 191:206-214.
[24] GUO H X, LI Y J, SHANG J, et al. Learning from class-imbalanced data:Review of methods and applications[J]. Expert Systems with Applications, 2017, 73:220-239.
[25] SUN Y M, WONG A K C, KAMEL M S. Classification of imbalanced data:A review[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2009, 23(4):687-719.
[26] GARCÍA V, SÁNCHEZ J S, MOLLINEDA R A. On the effectiveness of preprocessing methods when dealing with different levels of class imbalance[J]. Knowledge-Based Systems, 2012, 25(1):13-21.
[27] GALAR M, FERNANDEZ A, BARRENECHEA E, et al. A review on ensembles for the class imbalance problem:Bagging-, boosting-, and hybrid-based approaches[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(4):463-484.
[28] PRATI R C, BATISTA G E A P A, SILVA D F. Class imbalance revisited:A new experimental setup to assess the performance of treatment methods[J]. Knowledge and Information Systems, 2015, 45(1):247-270.
[29] KRAWCZYK B. Learning from imbalanced data:Open challenges and future directions[J]. Progress in Artificial Intelligence, 2016, 5(4):221-232.
[30] 李艳霞, 柴毅, 胡友强, 等. 不平衡数据分类方法综述[J]. 控制与决策, 2019, 34(4):673-688. LI Y X, CHAI Y, HU Y Q, et al. Review of imbalanced data classification methods[J]. Control and Decision, 2019, 34(4):673-688. (in Chinese)
[31] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE:Synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16(1):321-357.
[32] HAN H, WANG W Y, MAO B H. Borderline-SMOTE:A new over-sampling method in imbalanced data sets learning[C]//International Conference on Intelligent Computing. Hefei, 2005:878-887.
[33] 肖学沛. 锦屏二级水电站交通隧道岩爆预测及防治研究[D]. 成都:成都理工大学, 2005. XIAO X P. A study on the prediciton and prevention of rockburst in the traffic tunnel of Jinping II Hydropower Station[D]. Chengdu:Chengdu University of Technology, 2005. (in Chinese)
[34] QIU D H, LI T B, XUE Y G, et al. Rockburst prediction based on distance discrimination method and optimization technology-based weight calculation method[J]. Electronic Journal of Geotechnical Engineering, 2014, 19:1843-1854.
[35] 贾月卿. 改建铁路成昆线老鼻山隧道岩爆倾向性预测分析与防治技术研究[D]. 西安:西安建筑科技大学, 2018. JIA Y Q. Study on the tendency prediction and control techniques of rock burst in Laobi mountain tunnel of Chengdu to Kunming railway[D]. Xi'an:Xi'an University of Architecture and Technology, 2018. (in Chinese)
[36] 王羽, 许强, 柴贺军, 等. 工程岩爆灾害判别的RBF-AR耦合模型[J]. 吉林大学学报(地球科学版), 2013, 43(6):1943-1949. WANG Y, XU Q, CAI H J, et al. Rock burst prediction in deep shaft based on RBF-AR model[J]. Journal of Jilin University (Earth Science Edition), 2013, 43(6):1943-1949. (in Chinese)
[37] XUE Y G, LI Z Q, LI S C, et al. Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation[J]. Bulletin of Engineering Geology and the Environment, 2019, 78(1):417-429.
[38] 王超圣. 北山花岗岩岩爆特征实验研究及现场综合监测分析[D]. 北京:中国矿业大学, 2018. WANG C S. Experimental study and site comprehensive monitoring analysis on rockburst characteristics of Beishan granite[D]. Beijing:China University of Mining and Technology, 2018. (in Chinese)
[39] 汤志立, 徐千军. 基于9种机器学习算法的岩爆预测研究[J]. 岩石力学与工程学报, 2020, 39(4):773-781. TANG Z L, XU Q J. Rockburst prediction based on nine machine learning algorithms[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39(4):773-781. (in Chinese)