Skip to main content
SpringerLink
Log in

Prediction of rockburst hazard based on particle swarm algorithm and neural network

  • S.I: Cognitive-inspired Computing and Applications
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Rockburst is a typical dynamic phenomenon of mine destruction. With the expansion of mining scale and mining depth, its harm is becoming more and more serious. This has become an important problem to be solved urgently in the mining industry. This paper aims to study the prediction and prediction of rockburst risk based on particle swarm algorithm and neural network. This paper proposes a shock risk assessment method based on BP neural network. It uses existing shock pressure data to establish a regression model through BP network, and uses PSO algorithm to optimize connection weights to evaluate the slow convergence of BP network and easy to fall into local optimality. The shortcomings of and the degree of convergence was evaluated. In addition, this paper proposes a rockburst risk prediction method. In order to improve the accuracy of rockburst prediction, regional prevention and control measures and certain risk mitigation methods can also be quickly adopted. It not only identifies the mechanical properties of coal and rock mass in the laboratory, but also judges the possibility of rockbursts during mining. The experimental results in this paper show that 10 main factors that affect rockbursts are selected, 20 standard mechanical data sets are used, and a PSO-BP-based mine explosion risk assessment model is established, and the model is compared with the standard BP model Make a comparison. The results show that compared with the standard BP model, the evaluation accuracy of the PSO-BP model is increased by 15%. Finally, an example of mine risk assessment verifies the accuracy and overall applicability of the method.

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

Similar content being viewed by others

References

  1. Ganin Y, Ustinova E, Ajakan H et al (2017) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096–2030

    MathSciNet  MATH  Google Scholar 

  2. Zengqiang Y, Linming D, Chang L et al (2016) Application of high-pressure water jet technology and the theory of rockburst control in roadway. Int J Min ence Technol 26(005):929–935

    Article  Google Scholar 

  3. Schoenenberger AW, Moser A, Bertschi D et al (2018) Improvement of Risk Prediction After Transcatheter Aortic Valve Replacement by Combining Frailty With ConventionalRisk Scores. Jacc Cardiovasc Interv 11(4):395–403

    Article  Google Scholar 

  4. Zhao Y, Chen G, Yu H et al (2018) Development of risk prediction models for glioma based on genome-wide association study findings and comprehensive evaluation of predictive performances. Oncotarget 9(9):8311–8325

    Article  Google Scholar 

  5. Rahman MS, Sultana M (2017) Performance of Firth-and logF -type penalized methods in risk prediction for small or sparse binary data. BMC Med Res Methodol 17(1):1–15

    Article  Google Scholar 

  6. Wu JH, Li J, Wang J et al (2020) Risk prediction of type 2 diabetes in steel workers based on convolutional neural network. Neural Comput Applic 32:9683–9698

    Article  Google Scholar 

  7. Ke Y, Fu B, Zhang W (2016) Semi-varying coefficient multinomial logistic regression for disease progression risk prediction. Stat Med 35(26):4764–4778

    Article  MathSciNet  Google Scholar 

  8. Weng L, Huang L, Taheri A et al (2017) Rockburst characteristics and numerical simulation based on a strain energy density index: A case study of a roadway in Linglong gold mine, China. Tunnell Underground Space Technol 69:223–232

    Article  Google Scholar 

  9. Xu Z, Cheng C, Sugumaran V (2020) Big data analytics of crime prevention and control based on image processing upon cloud computing. J Surveill Secur Saf 1:16–33

    Google Scholar 

  10. Cai W, Dou L, Li Z et al (2016) Verification of passive seismic velocity tomography in rockburst hazard assessment. Chinese J Geophy- Chinese Edition 59(1):252–262

    Google Scholar 

  11. Fuawka K, Pytel W, Mertuszka P (2018) The effect of selected rockburst prevention measures on seismic activity – Case study from the Rudna copper mine. J Sustain Min 17(1):1–10

    Article  Google Scholar 

  12. Gong F, Luo Y, Si X et al (2017) Experimental modelling on rockburst in deep hard rock circular tunnels. Chin J Rock Mechan Eng 36(7):1634–1648

    Google Scholar 

  13. Li N, Feng X, Jimenez R (2017) Predicting rockburst hazard with incomplete data using Bayesian networks. Tunnell Underground Space Technol Incorporating Trenchless Technol Res 61:61–70

    Article  Google Scholar 

  14. Chen L , Li Q , Yang J , et al. (2018) Laboratory Testing on Energy Absorption of High-Damping Rubber in a New Bolt for Preventing Rockburst in Deep Hard Rock Mass. Shock and vibration, 2018:7214821.1–7214821.12.

  15. Jiang JQ, Wang P, Jiang LS et al (2018) Numerical simulation on mining effect influenced by a normal fault and its induced effect on rockburst. Geomecha Eng 14(4):337–344

    Google Scholar 

  16. Yanjun QI, Zhaoxing D, Hongwen J et al (2017) Experimental modelling on failure characteristics of rockburst with different properties tunnel. J China Univ Min Technol 46(6):1239–1250

    Google Scholar 

  17. Vižintin G, Mayer J, Lajlar B et al (2017) rockburst dependency on the type of steel arch support in the Velenje mine. Materiali in Tehnologije 51(1):11–18

    Article  Google Scholar 

  18. Goh ATC (2017) Seismic liquefaction potential assessed by neural networks. Environ Earth ences 76(9):1467–1480

    Google Scholar 

  19. Jiang B, Wang L, Gu S et al (2017) Study on rockburst mechanism of TBM excavation for deep tunnel based on energy principle. Caikuang yu Anquan Gongcheng Xuebao/J Mi Safety Eng 34(6):1103–1109

    Google Scholar 

  20. Ko CH, Chen JK (2017) Grasping force based manipulation for multifingered hand-arm robot using neural networks. Num Algebra Control Optimiz 4(1):59–74

    Article  MathSciNet  Google Scholar 

  21. Pereira S, Pinto A, Alves V et al (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imag 35(5):1240–1251

    Article  Google Scholar 

  22. Mei S, Montanari A, Nguyen PM (2018) A mean field view of the landscape of two-layers neural networks. Proc Natl Acad ences 115(33):E7665–E7671

    Article  MathSciNet  Google Scholar 

  23. Gong M, Liu J, Li H et al (2017) A multiobjective sparse feature learning model for deep neural networks. IEEE Trans Neural Netw Learn Syst 26(12):3263–3277

    Article  MathSciNet  Google Scholar 

  24. Rajchl M, Lee M, Oktay O et al (2016) DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks. IEEE Trans Med Imaging 36(2):674–683

    Article  Google Scholar 

  25. Ghafoorian M, Karssemeijer N, Heskes T et al (2017) Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin. Neuroimage Clin 14:391–399

    Article  Google Scholar 

  26. Yang W, Jin L, Tao D et al (2016) DropSample: a new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition. Pattern Recogn 58(4):190–203

    Article  Google Scholar 

  27. Fernandez-Gamez MA, Gil-Corral AM, Galan-Valdivieso F (2016) Corporate reputation and market value: Evidence with generalized regression neural networks. Expert Syst Appl 46:69–76

    Article  Google Scholar 

  28. Ramezani Mayiami M, Hajimirsadeghi M, Skretting K et al (2021) Bayesian topology learning and noise removal from network data. Discov Internet Things 1:11

    Article  Google Scholar 

  29. Tay D, Poh CL, Van Reeth E et al (2018) The effect of sample age and prediction resolution on myocardial infarction risk prediction. Biomed Health Inform IEEE J 19(3):1178–1185

    Article  Google Scholar 

  30. Mathew T J , Sherly E , Alcantud, José Carlos R, et al. (2018) A multimodal adaptive approach on soft set based diagnostic risk prediction system. J Intell Fuzzy Syst,34(3):1609-1618

Download references

Acknowledgements

Funding was provided by Project supported by discipline innovation team of Liaoning Technical University (LNTU20TD-05).

Author information

Authors and Affiliations

  1. Department of Mining Engineering, School of Mining, Liaoning University of Engineering and Technology, Fuxin, 123000, Liaoning, China

    Meichang Zhang

Authors
  1. Meichang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meichang Zhang.

Ethics declarations

Conflict interest

There are no potential competing interests in our paper. And all authors have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, M. Prediction of rockburst hazard based on particle swarm algorithm and neural network. Neural Comput & Applic 34, 2649–2659 (2022). https://doi.org/10.1007/s00521-021-06057-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06057-9

Keywords

Search

Navigation