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Published in: Neural Computing and Applications 9/2021

16-01-2021 | S.I. : SPIoT 2020

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

Authors: Yu-zhe Zhang, Xiong Wu, Xiao Zhang, Ao-shuang Mei

Published in: Neural Computing and Applications | Issue 9/2021

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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.

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Literature
1.
go back to reference Xu D, Hu XY, Shan CL, Li RH (2016) Landslide monitoring in southwestern china via time-lapse electrical resistivity tomography. Appl Geophys 13(1):1–12CrossRef Xu D, Hu XY, Shan CL, Li RH (2016) Landslide monitoring in southwestern china via time-lapse electrical resistivity tomography. Appl Geophys 13(1):1–12CrossRef
2.
go back to reference 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):1–16CrossRef 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):1–16CrossRef
3.
go back to reference 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):2249–2269CrossRef 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):2249–2269CrossRef
4.
go back to reference 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):745–760CrossRef 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):745–760CrossRef
5.
go back to reference 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 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
6.
go back to reference 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):1359–1373CrossRef 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):1359–1373CrossRef
7.
go back to reference 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 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
8.
go back to reference 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):1–9CrossRef 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):1–9CrossRef
9.
go back to reference 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):46–67CrossRef 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):46–67CrossRef
10.
go back to reference 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):1565–1582CrossRef 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):1565–1582CrossRef
11.
go back to reference 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 89:249–261CrossRef 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 89:249–261CrossRef
12.
go back to reference 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):3521–3526MathSciNetCrossRef 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):3521–3526MathSciNetCrossRef
13.
go back to reference 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):1299–1312CrossRef 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):1299–1312CrossRef
14.
go back to reference Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2016) Reading text in the wild with convolutional neural networks. Int J Comput Vision 116(1):1–20MathSciNetCrossRef Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2016) Reading text in the wild with convolutional neural networks. Int J Comput Vision 116(1):1–20MathSciNetCrossRef
15.
go back to reference 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 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
16.
go back to reference 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):91–100CrossRef 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):91–100CrossRef
17.
go back to reference 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):125–138MathSciNetCrossRef 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):125–138MathSciNetCrossRef
18.
go back to reference Alanis AY (2018) Electricity prices forecasting using artificial neural networks. IEEE Latin Am Trans 16(1):105–111CrossRef Alanis AY (2018) Electricity prices forecasting using artificial neural networks. IEEE Latin Am Trans 16(1):105–111CrossRef
19.
go back to reference Carleo G, Troyer M (2017) Many-body physics solving the quantum many-body problem with artificial neural networks. Science 355(6325):602–606MathSciNetCrossRef Carleo G, Troyer M (2017) Many-body physics solving the quantum many-body problem with artificial neural networks. Science 355(6325):602–606MathSciNetCrossRef
20.
go back to reference 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):742–769MathSciNetCrossRef 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):742–769MathSciNetCrossRef
21.
go back to reference 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):6232–6251CrossRef 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):6232–6251CrossRef
22.
go back to reference 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):645–657CrossRef 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):645–657CrossRef
23.
go back to reference 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 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
24.
go back to reference 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):285–292CrossRef 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):285–292CrossRef
25.
go back to reference 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):1075–1084CrossRef 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):1075–1084CrossRef
26.
go back to reference Zhang Y (2019) Analysis of seepage field of polluted water in agricultural planting soil in Ordos Basin. Universidad Del Zulia 36(5):1274–1286 Zhang Y (2019) Analysis of seepage field of polluted water in agricultural planting soil in Ordos Basin. Universidad Del Zulia 36(5):1274–1286
Metadata
Title
Permeability characteristics of bedrock fissures under disturbance conditions based on neural network
Authors
Yu-zhe Zhang
Xiong Wu
Xiao Zhang
Ao-shuang Mei
Publication date
16-01-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 9/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05625-9

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