Abstract
Rockburst is a frequent challenge during tunnel and other underground construction and is an extreme rock damage phenomenon. Therefore, it is very crucial to accurately estimate the damage potential of rockburst events. Microseismic (MS) monitoring can be used to obtain the relevant MS parameters for short-term rockburst prediction in real time that reflect the evolution of short-term rockburst. In this study, short-term rockburst potential data containing 7 MS parameters (cumulative number of events, cumulative released energy, cumulative apparent volume, event rate, energy rate, apparent volume rate, and incubation time) and 91 rockburst events (none rockburst, low rockburst, moderate rockburst, and high rockburst) were collected from the Jinping Hydropower Station diversion tunnel project in China. The objective of this paper is to propose an ensemble learning (EML) model based on the LévyFlight-Jaya optimization (LFJaya) and fivefold cross-validation (CV) method to achieve an accurate prediction of short-term rockburst damage potential using MS information. The EML consists of light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and logistic regression (LR), with seven MS parameters as the EML inputs and four rockburst levels as target variables. 70% and 30% of the cases were randomly selected for training and testing, respectively. Five metrics (accuracy, kappa, precision, recall, and F1-score) and nonparametric statistical tests were used to evaluate the performance of the model. It can be observed from the results of this study that the proposed EML has a higher test accuracy (89.29%) than the multiple base classifiers used in the study. With the use of the ensemble model, the decision boundary becomes more precise and overfitting is significantly improved. Additionally, the internal decision-making process of EML was elucidated through an analysis of the model parameters using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It was discovered that the cumulative released energy, the number of MS events, and the cumulative apparent volume (which reflects the number and strength of rock fractures) exert a significant influence on the prediction of short-term rockburst potential. Finally, developed graphical user interface (GUI) accurately predicted six instances of rockburst in the deeply buried tunnel of Jinping. Verification results indicated that the proposed EML exhibits strong generalization and can effectively utilize MS information to achieve precise short-term rockburst potential predictions.
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References
Adoko AC, Gokceoglu C, Wu L, Zuo QJ (2013) Knowledge-based and data-driven fuzzy modeling for rockburst prediction. Int J Rock Mech Min Sci 61:86–95. https://doi.org/10.1016/j.ijrmms.2013.02.010
Afraei S, Shahriar K, Madani SH (2019) Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 1: literature review and data preprocessing procedure. Tunn Undergr Space Technol 83:324–353. https://doi.org/10.1016/j.tust.2018.09.022
Alcott JM, Kaiser PK, Simser BP (1999) Use of microseismic source parameters for rockburst hazard assessment. Seism Caused Mines Fluid Inject Reserv Oil Extr. https://doi.org/10.1007/978-3-0348-8804-2_4
Allen M, Poggiali D, Whitaker K, Marshall TR, Kievit RA (2019) Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res. https://doi.org/10.12688/wellcomeopenres.15191.1
Askaripour M, Saeidi A, Rouleau A, Mercier-Langevin P (2022) Rockburst in underground excavations: a review of mechanism, classification, and prediction methods. Undergr Space. https://doi.org/10.1016/j.undsp.2021.11.008
Blake W, Hedley DG (2003) Rockbursts: case studies from North American hard-rock mines. SME.
Brady BT, Leighton F (1977) Seismicity anomaly prior to a moderate rock burst: a case study. Int J Rock Mech Min Sci Geomech Abstr 14(3):127–132. https://doi.org/10.1016/0148-9062(77)90003-1
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Cai M (2013) Principles of rock support in burst-prone ground. Tunn Undergr Space Technol 36:46–56. https://doi.org/10.1016/j.tust.2013.02.003
Cao A, Liu Y, Yang X, Li S, Liu Y (2022) FDNet: Knowledge and data fusion-driven deep neural network for coal burst prediction. Sensors 22(8):3088. https://doi.org/10.3390/s22083088
Chen K, Chen H, Zhou C, Huang Y, Qi X, Shen R, Liu F, Zuo M, Zou X, Wang J (2020) Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Res 171:115454. https://doi.org/10.1016/j.watres.2019.115454
Chen BR, Feng XT, Li QP, Luo RZ, Li S (2015) Rock burst intensity classification based on the radiated energy with damage intensity at Jinping II hydropower station, China. Rock Mech Rock Eng 48:289–303. https://doi.org/10.1007/s00603-013-0524-2
Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, https://doi.org/10.1145/2939672.2939785
Chen C, Zhang Q, Ma Q, Yu B (2019) LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion. Chemom Intell Lab Syst 191:54–64. https://doi.org/10.1016/j.chemolab.2019.06.003
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30. https://doi.org/10.5555/1248547.1248548
Dong LJ, Li XB, Peng K (2013) Prediction of rockburst classification using random forest. Trans Nonferr Metals Soc China 23(2):472–477. https://doi.org/10.1016/S1003-6326(13)62487-5
Fajklewicz Z (1983) Rock-burst forecasting and genetic research in coal-mines by microgravity method. Geophys Prospect 31(5):748–765. https://doi.org/10.1111/j.1365-2478.1983.tb01083.x
Feng X, Chen B, Li S, Zhang C, Xiao Y, Feng G, Zhou H, Qiu S, Zhao Z, Yu Y (2012) Studies on the evolution process of rockbursts in deep tunnels. J Rock Mech Geotech Eng 4(4):289–295. https://doi.org/10.3724/SP.J.1235.2012.00289
Feng XT, Chen BR, Zhang CQ, Li SJ, Wu SY (2013) Mechanism, warning and dynamic control of rockburst development process. Science Press, Beijing
Feng GL, Feng XT, Chen BR, Xiao YX, Yu Y (2015) A microseismic method for dynamic warning of rockburst development processes in tunnels. Rock Mech Rock Eng 48:2061–2076. https://doi.org/10.1007/s00603-014-0689-3
Feng XT, Liu J, Chen B, Xiao Y, Feng G, Zhang F (2017) Monitoring, warning, and control of rockburst in deep metal mines. Engineering 3(4):538–545. https://doi.org/10.1016/J.ENG.2017.04.013
Feng G, Xia G, Chen B, Xiao Y, Zhou R (2019) A method for rockburst prediction in the deep tunnels of hydropower stations based on the monitored microseismicity and an optimized probabilistic neural network model. Sustainability 11(11):3212. https://doi.org/10.3390/su11113212
Futagami K, Fukazawa Y, Kapoor N, Kito T (2021) Pairwise acquisition prediction with SHAP value interpretation. J Financ Data Sci 7:22–44. https://doi.org/10.1016/j.jfds.2021.02.001
Ghosh G, Sivakumar C (2018) Application of underground microseismic monitoring for ground failure and secure longwall coal mining operation: a case study in an Indian mine. J Appl Geophys 150:21–39. https://doi.org/10.1016/j.jappgeo.2018.01.004
Glazer S (2018) Mine seismology: data analysis and interpretation. Springer, Berlin. https://doi.org/10.1007/978-3-319-32612-2
Guo D, Chen H, Tang L, Chen Z, Samui P (2021) Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model. Acta Geotechnica. https://doi.org/10.1007/s11440-021-01299-2
Guo J, Guo J, Zhang Q, Huang M (2022) Research on rockburst classification prediction based on BP-SVM model. IEEE Access 10:50427–50447. https://doi.org/10.1109/ACCESS.2022.3173059
Heal D (2010) Observations and analysis of incidences of rockburst damage in underground mines.
Iacca G, dos Santos Junior VC, de Melo VV (2021) An improved Jaya optimization algorithm with Lévy flight. Expert Syst Appl 165:113902. https://doi.org/10.1016/j.eswa.2020.113902
Ingle KK, Jatoth RK (2020) An efficient JAYA algorithm with lévy flight for non-linear channel equalization. Expert Syst Appl 145:112970. https://doi.org/10.1016/j.eswa.2019.112970
Jin A, Basnet PMS, Mahtab S (2022) Microseismicity-based short-term rockburst prediction using non-linear support vector machine. Acta Geophys 70(4):1717–1736. https://doi.org/10.1007/s11600-022-00817-4
Kadkhodaei MH, Ghasemi E, Sari M (2022) Stochastic assessment of rockburst potential in underground spaces using Monte Carlo simulation. Environ Earth Sci 81(18):447. https://doi.org/10.1007/s12665-022-10561-z
Kaiser PK, Cai M (2012) Design of rock support system under rockburst condition. J Rock Mech Geotech Eng 4(3):215–227. https://doi.org/10.3724/SP.J.1235.2012.00215
Ke B, Khandelwal M, Asteris PG, Skentou AD, Mamou A, Armaghani DJ (2021) Rock-burst occurrence prediction based on optimized Naïve Bayes models. IEEE Access 9:91347–91360. https://doi.org/10.1109/ACCESS.2021.3089205
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) Lightgbm: A highly efficient gradient boosting decision tree. Adv Neural Inform Process Syst 30:3146–3154. https://doi.org/10.5555/3294996.3295074
Kim Y, Kim Y (2022) Explainable heat-related mortality with random forest and SHapley Additive exPlanations (SHAP) models. Sustain Cities Soc 79:103677. https://doi.org/10.1016/j.scs.2022.103677
Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M (2002) Logistic regression. Springer-Verlag, New York. https://doi.org/10.1007/978-1-4419-1742-3
Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics. https://doi.org/10.2307/2529310
Li N, Feng X, Jimenez R (2017) Predicting rock burst hazard with incomplete data using Bayesian networks. Tunn Undergr Space Technol 61:61–70. https://doi.org/10.1016/j.tust.2016.09.010
Li TZ, Li YX, Yang XL (2017) Rock burst prediction based on genetic algorithms and extreme learning machine. J Central South Univ 24(9):2105–2113. https://doi.org/10.1007/s11771-017-3619-1
Li D, Liu Z, Xiao P, Zhou J, Armaghani DJ (2022) Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization. Undergr Space 7(5):833–846. https://doi.org/10.1016/j.undsp.2021.12.009
Li X, Mao H, Li B, Xu N (2021) Dynamic early warning of rockburst using microseismic multi-parameters based on Bayesian network. Eng Sci Technol Int J 24(3):715–727. https://doi.org/10.1016/j.jestch.2020.10.002
Li N, Zare Naghadehi M, Jimenez R (2020) Evaluating short-term rock burst damage in underground mines using a systems approach. Int J Min Reclam Environ 34(8):531–561. https://doi.org/10.1080/17480930.2019.1657654
Liang W, Sari A, Zhao G, McKinnon SD, Wu H (2020) Short-term rockburst risk prediction using ensemble learning methods. Nat Hazards 104:1923–1946. https://doi.org/10.1007/s11069-020-04255-7
Liang W, Sari YA, Zhao G, McKinnon SD, Wu H (2021) Probability estimates of short-term rockburst risk with ensemble classifiers. Rock Mech Rock Eng 54:1799–1814. https://doi.org/10.1007/s00603-021-02369-3
Lin Y, Zhou K, Li J (2018) Application of cloud model in rock burst prediction and performance comparison with three machine learning algorithms. IEEE Access 6:30958–30968. https://doi.org/10.1109/ACCESS.2018.2839754
Liu JP, Feng XT, Li YH, Sheng Y (2013) Studies on temporal and spatial variation of microseismic activities in a deep metal mine. Int J Rock Mech Min Sci 60:171–179. https://doi.org/10.1016/j.ijrmms.2012.12.022
Liu Y, Hou S (2020) Rockburst prediction based on particle swarm optimization and machine learning algorithm. In: Information Technology in Geo-Engineering: Proceedings of the 3rd International Conference (ICITG), Guimarães, Portugal 3 pp 292–303. https://doi.org/10.1007/978-3-030-32029-4_25
Liu GF, Jiang Q, Feng GL, Chen DF, Chen BR, Zhao ZN (2021) Microseismicity-based method for the dynamic estimation of the potential rockburst scale during tunnel excavation. Bull Eng Geol Env 80:3605–3628. https://doi.org/10.1007/s10064-021-02173-x
Liu Z, Shao J, Xu W, Meng Y (2013) Prediction of rock burst classification using the technique of cloud models with attribution weight. Nat Hazards 68:549–568. https://doi.org/10.1007/s11069-013-0635-9
Ma T, Lin D, Tang L, Li L, Tang CA, Yadav KP, Jin W (2022) Characteristics of rockburst and early warning of microseismic monitoring at qinling water tunnel. Geomat Nat Haz Risk 13(1):1366–1394. https://doi.org/10.1080/19475705.2022.2073830
Ma T, Tang C, Tang L, Zhang W, Wang L (2015) Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station. Tunn Undergr Space Technol 49:345–368. https://doi.org/10.1016/j.tust.2015.04.016
Ma X, Westman E, Slaker B, Thibodeau D, Counter D (2018) The b-value evolution of mining-induced seismicity and mainshock occurrences at hard-rock mines. Int J Rock Mech Min Sci 104:64–70. https://doi.org/10.1016/j.ijrmms.2018.02.003
Mammone A, Turchi M, Cristianini N (2009) Support vector machines. Wiley Interdiscip Rev Comput Stat 1(3):283–289. https://doi.org/10.1002/wics.49
Mark C (2016) Coal bursts in the deep longwall mines of the United States. Int J Coal Sci Technol 3(1):1–9. https://doi.org/10.1007/s40789-016-0102-9
Mendecki A, Gibowicz S, Lasocki S (1997) Keynote lecture: principles of monitoring seismic rockmass response to mining. In: Gibowiez SJ (ed) Proceedings of the fourth international symposium on rockbursts and seismieity in mines pp 69–80
Myrvang A, Grimstad E (1983) Rockburst problems in Norwegian highway tunnels—recent case histories. Rockbursts: prediction and control. Symposium pp 133–139
Naji AM, Emad MZ, Rehman H, Yoo H (2019) Geological and geomechanical heterogeneity in deep hydropower tunnels: a rock burst failure case study. Tunn Undergr Space Technol 84:507–521. https://doi.org/10.1016/j.tust.2018.11.009
Peng J, Zou K, Zhou M, Teng Y, Zhu X, Zhang F, Xu J (2021) An explainable artificial intelligence framework for the deterioration risk prediction of hepatitis patients. J Med Syst 45:1–9. https://doi.org/10.1007/s10916-021-01736-5
Polikar R (2012) Ensemble machine learning: Methods and applications. Springer, New York, pp 1–34. https://doi.org/10.1007/978-1-4419-9326-7_1
Pu Y, Apel DB, Liu V, Mitri H (2019) Machine learning methods for rockburst prediction-state-of-the-art review. Int J Min Sci Technol 29(4):565–570. https://doi.org/10.1016/j.ijmst.2019.06.009
Pu Y, Apel DB, Wang C, Wilson B (2018) Evaluation of burst liability in kimberlite using support vector machine. Acta Geophys 66:973–982. https://doi.org/10.1007/s11600-018-0178-2
Pu Y, Apel DB, Wei C (2019) Applying machine learning approaches to evaluating rockburst liability: a comparation of generative and discriminative models. Pure Appl Geophys 176(10):4503–4517. https://doi.org/10.1007/s00024-019-02197-1
Qiu L, Liu Z, Wang E, He X, Feng J, Li B (2020) Early-warning of rock burst in coal mine by low-frequency electromagnetic radiation. Eng Geol 279:105755. https://doi.org/10.1016/j.enggeo.2020.105755
Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34. https://doi.org/10.5267/j.ijiec.2015.8.004
Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33:1–39. https://doi.org/10.1007/s10462-009-9124-7
Sagi O, Rokach L (2018) Ensemble learning: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):e1249. https://doi.org/10.1002/widm.1249
Sauer J, Mariani VC, dos Santos CL, Ribeiro MHDM, Rampazzo M (2021) Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings. Evolving Syst. https://doi.org/10.1007/s12530-021-09404-2
Shapley LS (1953) 17. A value for n-person games. In: Kuhn HW, Tucker AW (eds) Contributions to the theory of games (AM-28), volume II. Princeton University Press, Princeton, pp 307–318. https://doi.org/10.1515/9781400881970-018
Shirani Faradonbeh R, Taheri A (2019) Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques. Eng Comput 35(2):659–675. https://doi.org/10.1007/s00366-018-0624-4
Shukla R, Khandelwal M, Kankar P (2021) Prediction and assessment of rock burst using various meta-heuristic approaches. Min Metall Explor 38:1375–1381. https://doi.org/10.1007/s42461-021-00415-w
Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manage 45(4):427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Sun L, Hu N, Ye Y, Tan W, Wu M, Wang X, Huang Z (2022) Ensemble stacking rockburst prediction model based on Yeo-Johnson, K-means SMOTE, and optimal rockburst feature dimension determination. Sci Rep 12(1):15352. https://doi.org/10.1038/s41598-022-19669-5
Sun Y, Li G, Zhang J, Huang J (2021) Rockburst intensity evaluation by a novel systematic and evolved approach: machine learning booster and application. Bull Eng Geol Env 80:8385–8395. https://doi.org/10.1007/s10064-021-02460-7
Tang LZ, Xia K (2010) Seismological method for prediction of areal rockbursts in deep mine with seismic source mechanism and unstable failure theory. J Cent South Univ Technol 17(5):947–953. https://doi.org/10.1007/s11771-010-0582-5
Vapnik VN (1995) The nature of statistical learning. Theory. https://doi.org/10.1007/978-1-4757-3264-1
Wang J, Liu P, Ma L, He M, Xiong H (2021) A rockburst proneness evaluation method based on multidimensional cloud model improved by control variable method and rockburst database. Lithosphere. https://doi.org/10.2113/2022/5354402
Wang J, Zhang J (2010) Preliminary engineering application of microseismic monitoring technique to rockburst prediction in tunneling of Jinping II project. J Rock Mech Geotech Eng 2(3):193–208. https://doi.org/10.3724/SP.J.1235.2010.00193
Wang SM, Zhou J, Li CQ, Armaghani DJ, Li XB, Mitri HS (2021) Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. J Central South Univ 28(2):527–542
Wang M, Zhu ZM, Liu JH (2012) The photoelastic analysis of stress intensity factor for cracks around a tunnel. Appl Mech Mater 142:197–200. https://doi.org/10.4028/www.scientific.net/AMM.142.197
Woźniak M, Grana M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inform Fusion 16:3–17. https://doi.org/10.1016/j.inffus.2013.04.006
Wu S, Wu Z, Zhang C (2019) Rock burst prediction probability model based on case analysis. Tunn Undergr Space Technol 93:103069. https://doi.org/10.1016/j.tust.2019.103069
Xie X, Jiang W, Guo J (2021) Research on rockburst prediction classification based on GA-XGB model. IEEE Access 9:83993–84020. https://doi.org/10.1109/ACCESS.2021.3085745
Xu N, Li T, Dai F, Zhang R, Tang C, Tang L (2016) Microseismic monitoring of strainburst activities in deep tunnels at the Jinping II hydropower station, China. Rock Mech Rock Eng 49:981–1000. https://doi.org/10.1007/s00603-015-0784-0
Xu G, Li K, Li M, Qin Q, Yue R (2022) Rockburst intensity level prediction method based on FA-SSA-PNN model. Energies 15(14):5016. https://doi.org/10.3390/en15145016
Xue Y, Bai C, Qiu D, Kong F, Li Z (2020) Predicting rockburst with database using particle swarm optimization and extreme learning machine. Tunn Undergr Space Technol 98:103287. https://doi.org/10.1016/j.tust.2020.103287
Xue Y, Li G, Li Z, Wang P, Gong H, Kong F (2022) Intelligent prediction of rockburst based on Copula-MC oversampling architecture. Bull Eng Geol Env 81(5):209. https://doi.org/10.1007/s10064-022-02659-2
Xue R, Liang Z, Xu N, Dong L (2020) Rockburst prediction and stability analysis of the access tunnel in the main powerhouse of a hydropower station based on microseismic monitoring. Int J Rock Mech Min Sci 126:104174. https://doi.org/10.1016/j.ijrmms.2019.104174
Yin X, Liu Q, Huang X, Pan Y (2021) Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application. Tunn Undergr Space Technol 117:104133. https://doi.org/10.1016/j.tust.2021.104133
Yin X, Liu Q, Pan Y, Huang X, Wu J, Wang X (2021) Strength of stacking technique of ensemble learning in rockburst prediction with imbalanced data: comparison of eight single and ensemble models. Nat Resour Res 30:1795–1815. https://doi.org/10.1007/s11053-020-09787-0
Zhang M (2022) Classification prediction of rockburst in railway tunnel Based on hybrid PSO-BP neural network. Geofluids. https://doi.org/10.1155/2022/4673073
Zhang M, Liu S, Shimada H (2018) Regional hazard prediction of rock bursts using microseismic energy attenuation tomography in deep mining. Nat Hazards 93:1359–1378. https://doi.org/10.1007/s11069-018-3355-3
Zhao Y, Jiang Y (2010) Acoustic emission and thermal infrared precursors associated with bump-prone coal failure. Int J Coal Geol 83(1):11–20. https://doi.org/10.1016/j.coal.2010.04.001
Zheng S, He C, Hsu SC, Sarkis J, Chen JH (2020) Corporate environmental performance prediction in China: an empirical study of energy service companies. J Clean Product 266:121395. https://doi.org/10.1016/j.jclepro.2020.121395
Zhou J, Huang S, Qiu Y (2022) Optimization of random forest through the use of MVO, GWO and MFO in evaluating the stability of underground entry-type excavations. Tunn Undergr Space Technol 124:104494. https://doi.org/10.1016/j.tust.2022.104494
Zhou J, Huang S, Zhou T, Armaghani DJ, Qiu Y (2022) Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential. Artif Intell Rev 55(7):5673–5705
Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30(5):04016003. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000553
Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Space Technol 81:632–659. https://doi.org/10.1016/j.tust.2018.08.029
Zhou J, Li XB, Shi XZ (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Safety Sci 50(4):629–644. https://doi.org/10.1016/j.ssci.2011.08.065
Zhou J, Qiu Y, Khandelwal M, Zhu S, Zhang X (2021) Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. Int J Rock Mech Min Sci 145:104856
Zhou J, Qiu Y, Zhu S, Armaghani DJ, Khandelwal M, Mohamad ET (2021) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Undergr Space 6(5):506–515
Zhou J, Shi XZ, Huang RD, Qiu XY, Chong C (2016) Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines. Trans Nonferr Metals Soc China 26(7):1938–1945. https://doi.org/10.1016/S1003-6326(16)64312-1
Zhou KP, Yun L, Deng HW, Li JL, Liu CJ (2016) Prediction of rock burst classification using cloud model with entropy weight. Trans Nonferr Metals Soc China 26(7):1995–2002. https://doi.org/10.1016/S1003-6326(16)64313-3
Zhou J, Zhu S, Qiu Y, Armaghani DJ, Zhou A, Yong W (2022) Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm. Acta Geotech 17(4):1343–1366
Zitar RA, Al-Beta MA, Awadallah MA, Doush IA, Assaleh K (2022) An intensive and comprehensive overview of JAYA algorithm, its versions and applications. Archiv Comput Method Eng 29(2):763–792. https://doi.org/10.1007/s11831-021-09585-8
Zhou J, Chen C, Wang M, Khandelwal M (2021) Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors. Int J Min Sci Technol 31(5):799–812
Zhou J, Zhang R, Qiu Y, Khandelwal M (2023) A true triaxial strength criterion for rocks by gene expression programming. J Rock Mech Geotech Eng. https://doi.org/10.1016/j.jrmge.2023.03.004
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This research is partially supported by the National Natural Science Foundation Project of China (42177164 and 41807259) and the Distinguished Youth Science Foundation of Hunan Province of China (2022JJ10073).
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Qiu, Y., Zhou, J. Short-term rockburst prediction in underground project: insights from an explainable and interpretable ensemble learning model. Acta Geotech. 18, 6655–6685 (2023). https://doi.org/10.1007/s11440-023-01988-0
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DOI: https://doi.org/10.1007/s11440-023-01988-0