Skip to main content

Advertisement

Springer Nature Link
Log in

Permeability characteristics of bedrock fissures under disturbance conditions based on neural network

  • S.I. : SPIoT 2020
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

During different periods of rock fracture engineering, the surrounding rock is subject to intermittent and periodic vibration disturbances of different degrees. This disturbance has a great influence on the permeability characteristics of bedrock fissures. The permeability characteristics of bedrock fissure permeability are the most important hydrogeological element of underground water-containing medium, and determining the stratum permeability is an important link to evaluate the hydrogeological conditions of the mining area. Therefore, this paper studies the permeability characteristics of bedrock fissures based on the disturbance of neural network. First, use the basic structure of the neural network to understand the method applied to the study of permeability characteristics of bedrock cracks; secondly, put forward the rock permeability model driven by coal mining to help the subsequent experimental design of various relevant factors after the bedrock cracks Finally, the stress intensity factor method at the tip of the fracture is used to calculate the permeability of the bedrock fracture. The experimental data shows that the infiltration rate tested in the first minute of the experiment is 5.5 * 10−4 cm/s, which is equivalent to 12 times the stable infiltration rate. At 50 min after the start of the test, the infiltration rate dropped to 4.6 * 10−4 cm/s, which was close to the stable infiltration rate. The experimental results show that under the disturbance condition of neural network, the infiltration rate of bedrock fissures is accelerated and the permeability is increased.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Xu D, Hu XY, Shan CL, Li RH (2016) Landslide monitoring in southwestern china via time-lapse electrical resistivity tomography. Appl Geophys 13(1):1–12

    Article  Google Scholar 

  2. Figueiredo B, Tsang CF, Niemi A, Lindgren G (2016) Review: the state-of-art of sparse channel models and their applicability to performance assessment of radioactive waste repositories in fractured crystalline formations. Hydrogeol J 24(7):1–16

    Article  Google Scholar 

  3. Ye S, Franceschini A, Zhang Y, Janna C, Gong X, Yu J et al (2018) A novel approach to model earth fissure caused by extensive aquifer exploitation and its application to the wuxi case, china. Water Resour Res 54(3):2249–2269

    Article  Google Scholar 

  4. Zhang Y, Wang Z, Xue Y, Wu J, Yu J (2016) Mechanisms for earth fissure formation due to groundwater extraction in the su-xi-chang area, china. Bull Eng Geol Env 75(2):745–760

    Article  Google Scholar 

  5. Meng FF, Pu H, Chen JR, Xiao C (2017) Extension law of thin bedrock fissure based on particle discrete element. Meitan Xuebao/J China Coal Soc 42(2):421–428

    Google Scholar 

  6. Jaunat J, Dupuy A, Huneau F, Celle-Jeanton H, Le Coustumer P (2016) Groundwater flow dynamics of weathered hard-rock aquifers under climate-change conditions: an illustrative example of numerical modeling through the equivalent porous media approach in the north-western pyrenees (france). Hydrogeol J 24(6):1359–1373

    Article  Google Scholar 

  7. Wang GY, You G, Zhu JQ, Yu J, Gong XL, Wu JQ (2016) Investigations of changjing earth fissures, jiangyin, jiangsu, china. Environ Earth Sci 75(6):5021–50210

    Google Scholar 

  8. Rozkowski J, Rozkowski K (2016) Influence of fissuring and karstification of the carbonate aquifer unsaturated zone on its vulnerability to contamination (cracow upper jurassic region, poland). Environ Earth Sci 75(12):1–9

    Article  Google Scholar 

  9. Worthman C, Troiano B (2016) Capillary discourses, fissure points, and tacitly confessing the self: foucaults later work and educational research. J Adult Contin Educ 22(1):46–67

    Article  Google Scholar 

  10. Masoudian MS, Hashemi MA, Tasalloti A, Marshall AM (2018) Elastic-brittle-plastic behaviour of shale reservoirs and its implications on fracture permeability variation: an analytical approach. Rock Mech Rock Eng 51(5):1565–1582

    Article  Google Scholar 

  11. Xu Z, Liu C, Zhou X, Gao G, Feng X (2019) Full-scale physical modelling of fissure grouting in deep underground rocks. Tunn Undergr Sp Technol 89:249–261

    Article  Google Scholar 

  12. Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci 114(13):3521–3526

    Article  MathSciNet  Google Scholar 

  13. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB et al (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312

    Article  Google Scholar 

  14. Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2016) Reading text in the wild with convolutional neural networks. Int J Comput Vision 116(1):1–20

    Article  MathSciNet  Google Scholar 

  15. Bergmeir C, Benítez JM (2017) Rsnns: neural networks in r using the stuttgart neural network simulator (snns). Carpathian J Electron Comput Eng 46(2):3–6

    Google Scholar 

  16. Valipour M (2016) Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms. Meteorol Appl 23(1):91–100

    Article  Google Scholar 

  17. Gong M, Zhao J, Liu J, Miao Q, Jiao L (2016) Change detection in synthetic aperture radar images based on deep neural networks. Neural Netw Learn Syst IEEE Trans 27(1):125–138

    Article  MathSciNet  Google Scholar 

  18. Alanis AY (2018) Electricity prices forecasting using artificial neural networks. IEEE Latin Am Trans 16(1):105–111

    Article  Google Scholar 

  19. Carleo G, Troyer M (2017) Many-body physics solving the quantum many-body problem with artificial neural networks. Science 355(6325):602–606

    Article  MathSciNet  Google Scholar 

  20. Soltanolkotabi M, Javanmard A, Lee JD (2019) Theoretical insights into the optimization landscape of over-parameterized shallow neural networks. IEEE Trans Inf Theory 65(2):742–769

    Article  MathSciNet  Google Scholar 

  21. Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251

    Article  Google Scholar 

  22. Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2017) Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sens 55(2):645–657

    Article  Google Scholar 

  23. Cao Y, Shi D (2017) The influence of crack location on stress intensity factor at crack tip near the bi-material interface of finite size. Acta Mechanica Solida Sinica 38(3):263–270

    Google Scholar 

  24. Shlyannikov V, Tumanov A (2017) The effect of creep damage formulation on crack tip fields, creep stress intensity factor and crack growth assessments. Frattura ed Integrità Strutturale 11(41):285–292

    Article  Google Scholar 

  25. Braun Matías, Albuixech Vicente Francisco González (2019) Analysis of the stress intensity factor dependence with the crack velocity using a lattice model. Fatigue Fract Eng Mater Struct 42(5):1075–1084

    Article  Google Scholar 

  26. Zhang Y (2019) Analysis of seepage field of polluted water in agricultural planting soil in Ordos Basin. Universidad Del Zulia 36(5):1274–1286

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 41972259) and National Key R&D Program of China (No. 2018YFC0406400).

Author information

Authors and Affiliations

  1. School of Water Resource and Environment, China University of Geosciences, Beijing, 100083, China

    Yu-zhe Zhang, Xiong Wu, Xiao Zhang & Ao-shuang Mei

Authors
  1. Yu-zhe Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ao-shuang Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiong Wu.

Ethics declarations

Conflict of interest

These 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, Yz., Wu, X., Zhang, X. et al. Permeability characteristics of bedrock fissures under disturbance conditions based on neural network. Neural Comput & Applic 33, 4041–4051 (2021). https://doi.org/10.1007/s00521-020-05625-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05625-9

Keywords