Skip to main content
Log in

Assessment of plastic zones surrounding the power station cavern using numerical, fuzzy and statistical models

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Plastic zones evaluation around the powerhouse caverns is a very crucial issue in designing and constructing these structures and accurate determination of their related optimum support systems. Due to inherent difficulties during the field measurement of plastic zones around the powerhouse caverns and shortcomings of the available methods in this field, applying new predictive models is an attractive and helpful topic. Accordingly, plastic zones around the powerhouse caverns have been investigated in this research using numerical analysis (NA), fuzzy inference system (FIS) and multivariate regression (MVR) model. Based on the numerical simulations, a new predictive equation has been developed to determine the plastic zone at middle point of sidewall and induced key point around a cavern. The basic parameters including rock geomechanical properties and geometrical characteristics of cavern structures have been considered as input variables in plastic zones modeling at middle points of roof, floor, left sidewall and right sidewall as well as at key point. For FIS and MVR models construction, sufficient datasets were introduced based on the numerical simulations. Performance of established models has been assessed applying testing dataset and utilizing powerful statistical indices. Accordingly, it is proved that the derived results from FIS and NA models are more precise than MVR model and they are more satisfactory in plastic zone estimation. Finally, parametric study results revealed that lateral stress coefficient, depth of overburden and rock mass rating are the most effectual parameters and tensile strength is the least influencing parameter on the plastic zone around a cavern.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  1. Zhu WS, Sui B, Li XJ, Li SC, Wang WT (2008) A methodology for studying the high wall displacement of large scale underground cavern complexes and it’s applications. Tunn Undergr Sp Technol 23(6):651–664

    Google Scholar 

  2. Chong L, Jinhai X, Jianzhong P, Chao M (2012) Plastic zone distribution laws and its types of surrounding rock in large-span roadway. Int J Min Sci Technol 22(1):23–28

    Google Scholar 

  3. Feng W, Huang R, Li T (2012) Deformation analysis of a soft–hard rock contact zone surrounding a tunnel. Tunn Undergr Sp Technol 32:190–197

    Google Scholar 

  4. Xiang Y, Feng S (2013) Theoretical prediction of the potential plastic zone of shallow tunneling in vicinity of pile foundation in soils. Tunn Undergr Sp Technol 38:115–121

    Google Scholar 

  5. Zhang W, Goh ATC (2014) Multivariate adaptive regression splines model for reliability assessment of serviceability limit state of twin caverns. Geomech Eng 7(4):431–458

    Google Scholar 

  6. Chen YF, Zheng HK, Wang M, Hong JM, Zhou CB (2015) Excavation-induced relaxation effects and hydraulic conductivity variations in the surrounding rocks of a large-scale underground powerhouse cavern system. Tunn Undergr Sp Technol 49:253–267

    Google Scholar 

  7. Yu S, Zhu WS, Yang WM, Zhang DF, Ma QS (2015) Rock Bridge fracture model and stability analysis of surrounding rock in underground cavern group. Struct Eng Mech 53(3):481–495

    Google Scholar 

  8. Zhang W, Goh ATC (2016) Predictive models of ultimate and serviceability performances for underground twin caverns. Geomech Eng 10(2):175–188

    Google Scholar 

  9. Zhang QB, He L, Zhu WS (2016) Displacement measurement techniques and numerical verification in 3D geomechanical model tests of an underground cavern group. Tunn Undergr Sp Technol 56:54–64

    Google Scholar 

  10. Li HB, Yang XG, Zhang XB, Zhou JW (2017) Deformation and failure analyses of large underground caverns during construction of the Houziyan Hydropower Station, Southwest China. Eng Fail Anal 80:164–185

    Google Scholar 

  11. Jiang Q, Su G, Feng XT, Chen G, Zhang MZ, Liu C (2018) Excavation optimization and stability analysis for large underground caverns under high geostress: a case study of the Chinese Laxiwa project. Rock Mech Rock Eng 52(3):1–21

    Google Scholar 

  12. Behnia M, Cheraghi Seifabad M (2018) Stability analysis and optimization of the support system of an underground powerhouse cavern considering rock mass variability. Environ Earth Sci 77:645. https://doi.org/10.1007/s12665-018-7835-2

    Article  Google Scholar 

  13. Gao X, Chuan Yan E, Jim Yeh TC, Cai JS, Liang Y, Wang M (2018) A geostatistical inverse approach to characterize the spatial distribution of deformability and shear strength of rock mass around an unlined rock cavern. Eng Geol 245(1):106–119

    Google Scholar 

  14. Li X, Chen HM, Sun Y, Zhou R, Wang L (2018) Study on the splitting failure of the surrounding rock of underground caverns. Geomech Eng 14(5):499–507

    Google Scholar 

  15. Xua MF, Wu SC, Gao YT, Ma J, Wu QL (2019) Analytical elastic stress solution and plastic zone estimation for a pressure relief circular tunnel using complex variable methods. Tunn Undergr Sp Technol 84:381–398

    Google Scholar 

  16. Wang M, Li HB, Han JQ, Xiao XH, Zhou JW (2019) Large deformation evolution and failure mechanism analysis of the multi-freeface surrounding rock mass in the Baihetan underground powerhouse. Eng Fail Anal 100:214–226

    Google Scholar 

  17. Ren Q, Xu L, Zhu A, Shan M, Zhang L, Gu J, Shen L (2019) Comprehensive safety evaluation method of surrounding rock during underground cavern construction. Undergr Sp. https://doi.org/10.1016/j.undsp.2019.10.003

    Article  Google Scholar 

  18. Li B, Xu N, Dai F, Zhang G, Xiao P (2019) Dynamic analysis of rock mass deformation in large underground caverns considering microseismic data. Int J Rock Mech Min Sci 122:104078. https://doi.org/10.1016/j.ijrmms.2019.104078

    Article  Google Scholar 

  19. Monjezi M, Rezaei M, Yazdyan Varjani A (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46(8):1273–1280

    Google Scholar 

  20. Monjezi M, Rezaei M, Yazdyan A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37(3):2637–2643

    Google Scholar 

  21. Monjezi M, Rezaei M (2011) Developing a new fuzzy model to predict burden from rock geomechanical properties. Expert Syst Appl 38(3):9266–9273

    Google Scholar 

  22. Rezaei M, Yazdyan Monjezi M, Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49(2):298–305

    Google Scholar 

  23. Rezaei M, Majdi A, Monjezi M (2014) An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Comput Appl 24(1):233–241

    Google Scholar 

  24. Rezaei M, Asadizadeh M, Hossaini MF, Majdi A (2015) Prediction of representative deformation modulus of longwall panel roof rock strata using Mamdani fuzzy system. Int J Min Sci Technol 25(1):23–30

    Google Scholar 

  25. Rezaei M (2018) Indirect measurement of the elastic modulus of intact rocks using the Mamdani fuzzy inference system. Measurement 129:319–331

    Google Scholar 

  26. Rezaei M (2019) Forecasting the stress concentration coefficient around the mined panel using soft computing methodology. Eng Comput 35(2):451–466

    Google Scholar 

  27. Liu C, Gu C, Chen B (2017) Zoned elasticity modulus inversion analysis method of a high arch dam based on unconstrained lagrange support vector regression (support vector regression arch dam). Eng Comput 33(3):443–456

    Google Scholar 

  28. 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

    Google Scholar 

  29. Ghasemi E, Gholizadeh H, Adoko AC (2019) Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach. Eng Comput. https://doi.org/10.1007/s00366-018-00695-9

    Article  Google Scholar 

  30. Rezaei M, Asadizadeh M (2019) Predicting unconfined compressive strength of intact rock using new hybrid intelligent models. J Min Environ. https://doi.org/10.22044/jme.2019.8839.1774

    Article  Google Scholar 

  31. Liao CP (1993) Fuzzy influence function method for calculating mine subsidence in a horizontal seam. Geotech Geol Eng 11(4):235–247

    Google Scholar 

  32. Li W, Mei S, Zai S, Zhao S, Liang X (2006) Fuzzy models for analysis of rock mass displacements due to underground mining in mountainous areas. Int J Rock Mech Min Sci 43(4):503–511

    Google Scholar 

  33. Rezaei M, Rajabi M (2018) Vertical displacement estimation in roof and floor of an underground powerhouse cavern. Eng Fail Anal 90:290–309

    Google Scholar 

  34. Jing L (2003) A review of techniques advances and outstanding issues in numerical modelling for rock mechanics and rock engineering. Int J Rock Mech Min Sci 40(3):283–353

    Google Scholar 

  35. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Google Scholar 

  36. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13

    MATH  Google Scholar 

  37. Liitiainen E, Verleysen M, Corona F, Lendasse A (2009) Residual variance estimation in machine learning. Neurocomputing 72(16–18):3692–3703

    Google Scholar 

  38. Hoek E, Carranza-Torres C, Corkum B (2002) Hoek–Brown failure criterion 2002 edition. In: Proceedings of the 5th North American Rock Mechanics Symposium, Toronto, July, pp 267–273

  39. Rajabi M, Rahmannejad R, Rezaei M, Ganjalipour K (2017) Evaluation of the maximum horizontal displacement around the power station caverns using artificial neural network. Tunn Undergr Sp Technol 64:51–60

    Google Scholar 

  40. Zhu WS, Li XJ, Zhang QB, Zheng WH, Xin XL, Sun AH, Li SC (2010) A study on sidewall displacement prediction and stability evaluations for large underground power station caverns. Int J Rock Mech Min Sci 47(7):1055–1062

    Google Scholar 

  41. Abdollahipour A, Rahmannejad R (2013) Investigating the effects of lateral stress to vertical stress ratios and caverns shape on the cavern stability and sidewall displacements. Arabian J Geosci 6(12):4811–4819

    Google Scholar 

  42. Liu J, Zhao XD, Zhang SJ, Xie LK (2018) Analysis of support requirements for underground water-sealed oil storage cavern in China. Tunn Undergr Sp Technol 71:36–46

    Google Scholar 

  43. Sopacı E, Akgün H (2008) Engineering geological investigations and the preliminary support design for the proposed Ordu Peripheral Highway Tunnel, Ordu, Turkey. Eng Geol 96(1–2):43–61

    Google Scholar 

  44. Sinotech Engineering Consultants Inc (1999) Design of large underground caverns—a case history based on the Mingtan Pumped Storage Project in Taiwan. Geotech Geol Eng 23:175–197

    Google Scholar 

  45. Hosseinitoudeshki V (2013) Numerical analysis of K0 to tunnels in rock masses exhibiting strain-softening behaviour (case study in Sardasht dam tunnel, NW Iran). Int Res J Basic Appl Sci 4(6):1572–1581

    Google Scholar 

  46. Panda MK, Mohanty S, Pingua BMP, Mishra AK (2014) Engineering geological and geotechnical investigations along the head race tunnel in Teesta Stage-III hydroelectric project, India. Eng Geol 181(1):297–308

    Google Scholar 

  47. Rezaei M, Hossaini MF, Majdi A (2015) Development of a time-dependent energy model to calculate the mining-induced stress over gates and pillars. J Rock Mech Geotech Eng 7(3):306–317

    Google Scholar 

  48. Rezaei M (2018) Long-term stability analysis of goaf area in longwall mining using minimum potential energy theory. J Min Environ 9(1):169–182

    Google Scholar 

  49. Rezaei M, Farouq Hossaini M, Majdi A, Najmoddini I (2018) Study the roof behavior over the longwall gob in long-term condition. J Geol Min Res 10(2):15–27

    Google Scholar 

  50. Khoshjavan S, Mazlumi M, Rezai B, Rezai M (2010) Estimation of hardgrove grindability index (HGI) based on the coal chemical properties using artificial neural networks. Orient J Chem 26(4):1271–1280

    Google Scholar 

  51. Sayadi AR, Tavassoli SMM, Monjezi M, Rezaei M (2014) Application of neural networks to predict net present value in mining projects. Arabian J Geosci 7(3):1067–1072

    Google Scholar 

  52. Majdi A, Rezaei M (2013) Application of artificial neural networks for predicting the height of destressed zone above the mined panel in longwall coal mining. In: 47th U.S. Rock Mechanics/Geomechanics Symposium, San Francisco, June, pp 1665–1673

  53. Rezaei M, Hossaini MF, Majdi A, Najmoddini I (2017) Determination of the height of destressed zone above the mined panel: an ANN model. Int J Min Geo-Eng 51(1):1–7

    Google Scholar 

  54. Rezaei M (2017) Feasibility of novel techniques to predict the elastic modulus of rocks based on the laboratory data. Int J Geotech Eng. https://doi.org/10.1080/19386362.2017.1397873

    Article  Google Scholar 

  55. Rezaei M (2018) Development of an intelligent model to estimate the height of caving–fracturing zone over the longwall gobs. Neural Comput Appl 30(7):2145–2158

    Google Scholar 

  56. Nikafshan Rad H, Hasanipanah M, Rezaei M, Lotfi Eghlim A (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34(4):709–717

    Google Scholar 

  57. Asadizadeh M, Rezaei M (2019) Surveying the mechanical response of nonpersistent jointed slabs subjected to compressive axial loading utilising GEP approach. Int J Geotech Eng. https://doi.org/10.1080/19386362.2019.1596610

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Rezaei.

Ethics declarations

Conflict of interest

All of the authors have declared that no conflict of interest is encompassed for the current research.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (XLSX 173 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rezaei, M., Rajabi, M. Assessment of plastic zones surrounding the power station cavern using numerical, fuzzy and statistical models. Engineering with Computers 37, 1499–1518 (2021). https://doi.org/10.1007/s00366-019-00900-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00900-3

Keywords

Navigation