Skip to main content
SpringerLink
Log in

Hybridized intelligent multi-class classifiers for rockburst risk assessment in deep underground mines

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The rockburst hazard induced by the extreme release of the stress concentrated in rock mass in deep underground mines poses a significant threat to the safety and economy of the mining projects. Therefore, properly managing this hazard is critical for ensuring rock engineering projects’ sustainability. This study proposes comprehensible and practical classifiers for rockburst risk level appraisal by hybridizing K-means clustering with gene expression programming, GEP, logistic regression, LR, and classification and regression tree, CART (i.e., K-mean-GEP-LR and K-means-CART classifiers). A database containing 246 rockburst events with four risk levels of none, light, moderate, and severe was compiled from previous practices. Preliminary statistical analyses were conducted to detect the extreme outliers and determine the critical rockburst indicators. The K-means clustering analysis was performed to identify the main clusters within the database and relabel the rockburst events. The GEP algorithm was then utilized to develop binary models for predicting the occurrence of each class. Then, the likelihood of each class occurrence was determined using LR. Furthermore, the K-means clustering was combined with the CART algorithm to provide another visual tree structure model. The classifiers’ performance evaluation showed 96% and 95% accuracy values in the training and testing stages, respectively, for the K-means-GEP-LR model, while the accuracy values of 98.8% and 93.0% were obtained for the foregoing stages for the K-means-CART classifier. The results showed the robustness and high classification capability of both models. MatLab codes were also provided for the K-means-GEP-LR model, which assists other researchers/engineers in implementing the model in practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Alavi AH, Hasni H, Lajnef N, Chatti K, Faridazar F (2016) Damage detection using self-powered wireless sensor data: an evolutionary approach. Measurement 82:254–283

    Google Scholar 

  2. Alkroosh I, Nikraz H (2012) Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Eng Appl Artif Intell 25:618–627

    Google Scholar 

  3. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:1–74

    Google Scholar 

  4. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Cole statistics/probability series. Wadsworth & Brooks

    Google Scholar 

  5. Cabalar AF, Cevik A, Guzelbey IH (2010) Constitutive modeling of Leighton Buzzard Sands using genetic programming. Neural Comput Appl 19:657–665

    Google Scholar 

  6. Çanakcı H, Baykasoğlu A, Güllü H (2009) Prediction of compressive and tensile strength of Gaziantep basalt via networks and gene expression pamming. Neural Netw Appl 18:1031–1041

    Google Scholar 

  7. Chen K (2009) On coresets for k-median and k-means clustering in metric and euclidean spaces and their applications. SIAM J Comput 39:923–947

    MathSciNet  Google Scholar 

  8. Çiftçi ON, Fadiloǧlu S, Göǧüş F, Güven A (2009) Genetic programming approach to predict a model acidolysis system. Eng Appl Artif Intell 22:759–766

    Google Scholar 

  9. Dancey CP, Reidy J (2017) Statistics without maths for psychology. Pearson, London

    Google Scholar 

  10. De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81(11):3178–3192

    Google Scholar 

  11. Faradonbeh RS, Armaghani DJ, Amnieh HB, Mohamad ET (2018) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl 29:269–281

    Google Scholar 

  12. Faradonbeh RS, Hasanipanah M, Amnieh HB, Armaghani DJ, Monjezi M (2018) Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environ Monit Assess 190:1–15

    Google Scholar 

  13. Faradonbeh RS, Taheri A, e Sousa LR, Karakus M (2020) Rockburst assessment in deep geotechnical conditions using true-triaxial tests and data-driven approaches. Int J Rock Mech Min Sci 128:104279

    Google Scholar 

  14. Faradonbeh RS, Taheri A, Karakus M (2022) The propensity of the over-stressed rock masses to different failure mechanisms based on a hybrid probabilistic approach. Tunn Undergr Space Technol 119:104214

    Google Scholar 

  15. Farid DM, Zhang L, Rahman CM, Hossain MA, Strachan R (2014) Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Syst Appl 41:1937–1946

    Google Scholar 

  16. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874

    Google Scholar 

  17. Ferreira C (2006) Gene expression programming: mathematical modeling by an artificial intelligence. Springer

    Google Scholar 

  18. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027

  19. Ghasemi E, Gholizadeh H, Adoko AC (2020) Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach. Eng Comput 36:213–225

    Google Scholar 

  20. Ghiringhelli LM, Vybiral J, Levchenko SV, Draxl C, Scheffler M (2015) Big data of materials science: critical role of the descriptor. Phys Rev Lett 114:105503

    Google Scholar 

  21. Giustolisi O, Doglioni A, Savic DA, Webb BW (2007) A multi-model approach to analysis of environmental phenomena. Environ Model Softw 22:674–682

    Google Scholar 

  22. Guo D, Chen H, Tang L, Chen Z, Samui P (2022) Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model. Acta Geotech 17:1183–1205

    Google Scholar 

  23. Hasanipanah M, Faradonbeh RS, Amnieh HB, Armaghani DJ, Monjezi M (2017) Forecasting blast-induced ground vibration developing a CART model. Eng Comput 33:307–316

    Google Scholar 

  24. He M, Cheng T, Qiao Y, Li H (2022) A review of rockburst: experiments, theories, and simulations. J Rock Mech Geotech Eng 15(5):1312–1353

    Google Scholar 

  25. Hosseini S, Monjezi M, Bakhtavar E (2022) Minimization of blast-induced dust emission using gene-expression programming and grasshopper optimization algorithm: a smart mining solution based on blasting plan optimization. Clean Technol Environ Policy 24:2313–2328

    Google Scholar 

  26. Hoseinian FS, Faradonbeh RS, Abdollahzadeh A, Rezai B, Soltani-Mohammadi S (2017) Semi-autogenous mill power model development using gene expression programming. Powder Technol 308:61–69

    Google Scholar 

  27. Janusz A, Grzegorowski M, Michalak M, Wróbel Ł, Sikora M, Ślęzak D (2017) Predicting seismic events in coal mines based on underground sensor measurements. Eng Appl Artif Intell 64:83–94

    Google Scholar 

  28. Kaiser PK, Cai M (2012) Design of rock support system under rockburst condition. J Rock Mech Geotech Eng 4:215–227

    Google Scholar 

  29. Kaiser PK, McCreath DR, Tannant DD (1996) Canadian rockburst support handbook. Geomechanics Research Center

    Google Scholar 

  30. 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

    Google Scholar 

  31. Keshavarz A, Mehramiri M (2015) New Gene Expression Programming models for normalized shear modulus and damping ratio of sands. Eng Appl Artif Intell 45:464–472

    Google Scholar 

  32. Khandelwal M, Armaghani DJ, Faradonbeh RS, Ranjith PG, Ghoraba S (2016) A new model based on gene expression programming to estimate air flow in a single rock joint. Environ Earth Sci 75:1–13

    Google Scholar 

  33. Khandelwal M, Armaghani DJ, Faradonbeh RS, Yellishetty M, Majid MZA, Monjezi M (2017) Classification and regression tree technique in estimating peak particle velocity caused by blasting. Eng Comput 33:45–53

    Google Scholar 

  34. Kodinariya TM, Makwana PR (2013) Review on determining number of cluster in K-Means clustering. Int J 1:90–95

    Google Scholar 

  35. Lee HB, Macqueen JB (1980) A K-Means cluster analysis computer program with cross-tabulations and next-nearest-neighbor analysis. Educ Psychol Measur 40:133–138

    Google Scholar 

  36. Li D, Liu Z, Armaghani DJ, Xiao P, Zhou J (2022) Novel ensemble tree solution for rockburst prediction using deep forest. Mathematics 10:787

    Google Scholar 

  37. Li D, Shirani Faradonbeh R, Lv A, Wang X, Roshan H (2022) A data-driven field-scale approach to estimate the permeability of fractured rocks. Int J Min Reclam Environ 36(10):671–687

    Google Scholar 

  38. Li N, Jimenez R (2018) A logistic regression classifier for long-term probabilistic prediction of rock burst hazard. Nat Hazards 90:197–215. https://doi.org/10.1007/S11069-017-3044-7/TABLES/12

    Article  Google Scholar 

  39. 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 24:715–727

    Google Scholar 

  40. Liang M, Mohamad ET, Faradonbeh RS, Jahed Armaghani D, Ghoraba S (2016) Rock strength assessment based on regression tree technique. Eng Comput 32:343–354. https://doi.org/10.1007/S00366-015-0429-7/FIGURES/10

    Article  Google Scholar 

  41. 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

    Google Scholar 

  42. Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recogn 36:451–461

    Google Scholar 

  43. 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

    Google Scholar 

  44. Loh WY (2008) Classification and regression tree methods. Encycl Stat Qual Reliab 1:315–323

    Google Scholar 

  45. Luo H, Fang Y, Wang J, Wang Y, Liao H, Yu T, Yao Z (2023) Combined prediction of rockburst based on multiple factors and stacking ensemble algorithm. Undergr Space 13:241–261

    Google Scholar 

  46. Loozen G, Ozcelik O, Boon N, de Mol A, Schoen C, Quirynen M, Teughels W (2014) Inter-bacterial correlations in subgingival biofilms: a large-scale survey. J Clin Periodontol 41:1–10

    Google Scholar 

  47. Mercier D, Gaillard P, Aupetit M, Maillard C, Quach R, Muller JD (2006) How to help seismic analysts to verify the French seismic bulletin? Eng Appl Artif Intell 19:797–806. https://doi.org/10.1016/J.ENGAPPAI.2006.05.008

    Article  Google Scholar 

  48. Papadopoulos D, Benardos A (2021) Enhancing machine learning algorithms to assess rock burst phenomena. Geotech Geol Eng 39:5787–5809

    Google Scholar 

  49. Pour AF, Faradonbeh RS, Gholampour A, Ngo TD (2023) Predicting ultimate condition and transition point on axial stress–strain curve of FRP-confined concrete using a meta-heuristic algorithm. Compos Struct 304:116387

    Google Scholar 

  50. 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 29:565–570

    Google Scholar 

  51. Pu Y, Apel DB, Xu H (2019) Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier. Tunn Undergr Space Technol 90:12–18

    Google Scholar 

  52. Russenes BF (1974) Analysis of rock spalling for tunnels in steep valley sides. Norwegian Institute of Technology

    Google Scholar 

  53. Salimi A, Faradonbeh RS, Monjezi M, Moormann C (2018) TBM performance estimation using a classification and regression tree (CART) technique. Bull Eng Geol Env 77:429–440

    Google Scholar 

  54. Salimi A, Rostami J, Moormann C, Hassanpour J (2018) Examining feasibility of developing a rock mass classification for hard rock TBM application using non-linear regression, regression tree and genetic programming. Geotech Geol Eng 36:1145–1159

    Google Scholar 

  55. Sen S, Sezar EA, Gokceoglu C, Yagiz S (2012) On sampling strategies for small and continuous data with the modeling of genetic programming and adaptive neuro-fuzzy inference system. J Intel Fuzzy Syst 23:297–304

    Google Scholar 

  56. Shirani Faradonbeh R, Armaghani DJ, Monjezi M, Mohammad ET (2016) Genetic programming and gene expression programming for flyrock assessment due to mine blasting. Int J Rock Mech Min Sci 88:254–264

    Google Scholar 

  57. Shirani Faradonbeh R, Shaffiee Haghshenas S, Taheri A, Mikaeil R (2020) Application of self-organizing map and fuzzy c-mean techniques for rockburst clustering in deep underground projects. Neural Comput Appl 32:8545–8559

    Google Scholar 

  58. Shirani Faradonbeh R, Taheri A (2019) Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques. Eng Comput 35:659–675

    Google Scholar 

  59. Shirani Faradonbeh R, Taheri A, Karakus M (2022) Fatigue failure characteristics of sandstone under different confining pressures. Rock Mech Rock Eng 55:1227–1252

    Google Scholar 

  60. Syakur MA, Khotimah BK, Rochman EMS, Satoto BD (2018) Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In: IOP conference series: materials science and engineering. IOP Publishing, p 012017

  61. Wu M, Ye Y, Wang Q, Hu N (2022) Development of rockburst research: a comprehensive review. Appl Sci 12:974

    Google Scholar 

  62. Wu S, Wu Z, Zhang C (2019) Rock burst prediction probability model based on case analysis. Tunn Undergr Space Technol 93:103069

    Google Scholar 

  63. Qiu Y, Zhou J (2023) Short-term rockburst damage assessment in burst-prone mines: an explainable XGBOOST hybrid model with SCSO algorithm. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-023-03522-w

    Article  Google Scholar 

  64. 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

    Google Scholar 

  65. Yin X, Liu Q, Pan Y, Huang X (2021) A novel tree-based algorithm for real-time prediction of rockburst risk using field microseismic monitoring. Environ Earth Sci 80:1–19

    Google Scholar 

  66. Youn H, Gu Z (2010) Predicting Korean loading firm failures: an artificial neural network model along with a logistic regression model. Int J Hosp Manag 29:120–127

    Google Scholar 

  67. Zhang M (2022) Prediction of rockburst hazard based on particle swarm algorithm and neural network. Neural Comput Appl 34:2649–2659

    Google Scholar 

  68. Zhang Q, Barri K, Jiao P, Salehi H, Alavi AH (2021) Genetic programming in civil engineering: advent, applications and future trends. Artif Intell Rev 54:1863–1885

    Google Scholar 

  69. Zhang X, Nguyen H, Bui X-N, Tran Q-H, Nguyen D-A, Bui DT, Moayedi H (2020) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res 29:711–721

    Google Scholar 

  70. Zhao H, Chen B (2020) Data-driven model for rockburst prediction. Math Probl Eng. https://doi.org/10.1155/2020/5735496

    Article  Google Scholar 

  71. Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Space Technol 81:632–659

    Google Scholar 

  72. Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30:04016003

    Google Scholar 

  73. Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50:629–644

    Google Scholar 

  74. Zhou K, Gu D (2004) Application of GIS-based neural network with fuzzy self-organization to assessment of rockburst tendency. Chin J Rock Mech Eng 23:3093–3097

    Google Scholar 

Download references

Acknowledgement

This study is also partly supported by The China University of Mining and Technology (CUMT).

Author information

Authors and Affiliations

  1. WA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA, 6430, Australia

    Roohollah Shirani Faradonbeh, Will Vaisey & Mostafa Sharifzadeh

  2. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou

Authors
  1. Roohollah Shirani Faradonbeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Will Vaisey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mostafa Sharifzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roohollah Shirani Faradonbeh.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shirani Faradonbeh, R., Vaisey, W., Sharifzadeh, M. et al. Hybridized intelligent multi-class classifiers for rockburst risk assessment in deep underground mines. Neural Comput & Applic 36, 1681–1698 (2024). https://doi.org/10.1007/s00521-023-09189-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09189-2

Keywords

Search

Navigation