Skip to main content

Advertisement

SpringerLink
Log in

Prediction of curtain grouting efficiency based on ANFIS

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

As an important method for improving dam foundations, curtain grouting is designed to create a hydraulic barrier to decrease permeability, enhance strength, and reduce deformability of rock masses. To evaluate the improvement of rock masses, the Lugeon value (LU), rock quality designation (RQD), and fracture filled rate (FFR) after grouting are key evaluation indicators of grouting efficiency. A prediction method based on an adaptive neuro-fuzzy inference system is proposed to predict and evaluate curtain grouting efficiency in this study. Geological factors (fracture intensity, LU, and RQD before grouting), effective grouting operation factors (effective grouting pressure, effective grouting time, effective grout volume, and effective cement take), and tested interval depth are considered to be the critical factors that greatly influence the efficiency of curtain grouting and are selected as input parameters for prediction models. The grouting efficiency evaluation indicators (the LU value, RQD, and FFR after grouting) are selected as output parameters for evaluation of the efficiency. In addition, a formula for estimating the influence radius of grouting boreholes, which is used to determine the sphere of grouting influence, is proposed. To better reflect the influence of the position of grouting boreholes on the effects of grouting, this study suggests that the effective grouting operation factors can be calculated using an improved inverse distance weighting method. As a case study, this approach is used to predict the results of grouting and to evaluate the efficiency of curtain grouting in hydropower project A, located in the southwestern part of China. The approach shows considerable accuracy in predicting the results of grouting and evaluating grouting efficiency.

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  • Ahmed S, De Marsily G (1987) Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity. Water Resour Res 23(9):1717–1737

    Article  Google Scholar 

  • Akhondi M, Mohammadi Z (2014) Preliminary analysis of spatial development of karst using a geostatistical simulation approach. B Eng Geol Environ 73(4):1037–1047. doi:10.1007/s10064-014-0599-3

    Article  Google Scholar 

  • Amadei B, Savage WZ (2001) An analytical solution for transient flow of Bingham viscoplastic materials in rock fractures. Int J Rock Mech Min 38(2):285–296. doi:10.1016/S1365-1609(00)00080-0

    Article  Google Scholar 

  • Azimian A, Ajalloeian R (2015) Permeability and groutability appraisal of the Nargesi dam site in Iran based on the secondary permeability index, joint hydraulic aperture and Lugeon tests. B Eng Geol Environ 74(3):845–859. doi:10.1007/s10064-014-0675-8

    Article  Google Scholar 

  • Bryson L S, Ortiz R, Leandre J (2014) Effects of a grout curtain on hydraulic and electrical conductivity in a laboratoryscale seepage model[C]. In: 2014 congress on geo-characterization and modeling for sustainability. Geo-Congress 2014, Atlanta, United States pp 3233–3242

  • Carter TG, Dershowitz W, Shuttle DA, Jefferies M (2012) Improved methods of design for grouting fractured rock. In: Proceedings of the 4th international conference on grouting and deep mixing, pp 1472–1483

  • Chen M, Lu W, Zhang W, Yan P, Zhou C (2015) An analysis of consolidation grouting effect of bedrock based on its acoustic velocity increase. Rock Mech Rock Eng 48(3):1259–1274. doi:10.1007/s00603-014-0624-7

    Article  Google Scholar 

  • Cheng M, Hoang N (2014) Groutability prediction of microfine cement based soil improvement using evolutionary LS-SVM inference model. J Civ Eng Manag 20(6):839–848

    Article  Google Scholar 

  • Chiles JP (1988) Fractal and geostatistical methods for modeling of a fracture network. Math Geol 20(6):631–654

    Article  Google Scholar 

  • Deere DU (1962) Technical description of rock cores for engineering purposes. University of Illinois, Illinois

    Google Scholar 

  • Deere DU (1968) Chapter 1: geological considerations. In: Stagg KG, Zienkiewicz OC (eds) Rock mechanics in engineering practice, Wiley, London pp 1–20.

  • Ehsanzadeh B, Ahangari K (2014) A novel approach in estimation of the soilcrete column’s diameter and optimization of the high pressure jet grouting using adaptive neuro fuzzy inference system (ANFIS). Open J Geol 04(08):386–398. doi:10.4236/ojg.2014.48030

    Article  Google Scholar 

  • Eriksson M, Friedrich M, Vorschulze C (2004) Variations in the rheology and penetrability of cement-based grouts—an experimental study. Cem Concr Res 34(7):1111–1119

    Article  Google Scholar 

  • Ewert F (1994) Evaluation and interpretation of water pressure tests. Grouting in the ground. Thomas Telford, London, pp 141–162

    Google Scholar 

  • Ewert F (1997) Permeability, groutability and grouting of rocks related to dam sites part 3. Dam Eng 8:215–248

    Google Scholar 

  • Fan G, Zhong D, Yan F, Yue P (2016) A hybrid fuzzy evaluation method for curtain grouting efficiency assessment based on an AHP method extended by D numbers. Expert Syst Appl 44:289–303. doi:10.1016/j.eswa.2015.09.006

    Article  Google Scholar 

  • Ferrari F, Apuani T, Giani GP (2014) Rock mass rating spatial estimation by geostatistical analysis. Int J Rock Mech Min 70:162–176. doi:10.1016/j.ijrmms.2014.04.016

    Article  Google Scholar 

  • Fransson Å (2001) Characterisation of a fractured rock mass for a grouting field test. Tunn Undergr Sp Tech 16(4):331–339

    Article  Google Scholar 

  • Funehag J, Fransson Å (2006) Sealing narrow fractures with a Newtonian fluid: model prediction for grouting verified by field study. Tunn Undergr Sp Tech 21(5):492–498. doi:10.1016/j.tust.2005.08.010

    Article  Google Scholar 

  • Funehag J, Gustafson G (2008a) Design of grouting with silica sol in hard rock—new methods for calculation of penetration length. Part I. Tunn Undergr Sp Tech 23(1):1–8. doi:10.1016/j.tust.2006.12.005

    Article  Google Scholar 

  • Funehag J, Gustafson G (2008b) Design of grouting with silica sol in hard rock—new design criteria tested in the field. Part II. Tunn Undergr Sp Tech 23(1):9–17. doi:10.1016/j.tust.2006.12.004

    Article  Google Scholar 

  • Guo X, Meng S (2011) Intelligent prediction on toe- slab foundation grouting of high faced rockfill dam based on LIBSVM. Yangtze River 42(01):33–36 (in Chinese)

    Google Scholar 

  • Gustafson G (2012) Hydrogeology for rock engineers. BeFo Rock Eng Res Found, Stockholm

    Google Scholar 

  • Gustafson G, Stille H (1996) Prediction of groutability from grout properties and hydrogeological data. Tunn Undergr Sp Tech 11(3):325–332

    Article  Google Scholar 

  • Håkansson U (1993) Rheology of fresh cement based grouts. PhD Thesis, Royal Institute of Technology

  • Hassanlourad M, Vosoughi M, Sarrafi A (2014) Predicting the grouting ability of sandy soils by artificial neural networks based on experimental tests. Civil Eng Infrastruct J 47(2):239–253

    Google Scholar 

  • Hässler L, Håkansson U, Stille H (1992a) Computer-simulated flow of grouts in jointed rock. Tunn Undergr Sp Tech 7(4):441–446

    Article  Google Scholar 

  • Hässler L, Håkansson U, Stille H (1992b) Classification of jointed rock with emphasis on grouting. Tunn Undergr Sp Tech 7(4):447–452

    Article  Google Scholar 

  • Hu J (2005) The research for the state grouting experiment in cracked rock mass. Central South University, Changsha (in Chinese)

    Google Scholar 

  • Huang XG, Yang XL, Fang YZ (2012) Study on grouting effect evaluation of F4 weathered slot in Xiamen Xiang’an submarine tunnel. Appl Mech Mater 256:1217–1220

    Article  Google Scholar 

  • Huo J, Song H, Luo L (2015) Investigation of groundwater chemistry at a dam site during its construction: a case study of Xiangjiaba Dam, China. Environ Earth Sci 74(3):2451–2461. doi:10.1007/s12665-015-4261-6

    Article  Google Scholar 

  • Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Narayanasamy MS, Mohd Amin MF (2015) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. B Eng Geol Environ 74(4):1301–1319. doi:10.1007/s10064-014-0687-4

    Article  Google Scholar 

  • Jaksa MB, Maier HR, Shahin MA (2008) Future challenges for artificial neural network modelling in geotechnical engineering. In: The 12th international conference of international association for computer methods and advances in geomechanics (IACMAG), Geo, India

  • Jang J (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  • Kayabasi A, Yesiloglu-Gultekin N, Gokceoglu C (2015) Use of non-linear prediction tools to assess rock mass permeability using various discontinuity parameters. Eng Geol 185:1–9. doi:10.1016/j.enggeo.2014.12.007

    Article  Google Scholar 

  • Kikuchi K, Igari T, Mito Y, Utsuki S (1997) In situ experimental studies on improvement of rock masses by grouting treatment. Int J Rock Mech Min 34(3):131–138

    Google Scholar 

  • Kim J, Yoon W (2014) Application of resistivity monitoring to evaluate cement grouting effect in earth filled dam. In: Proceedings of the international conference on numerical analysis and applied mathematics 2014 (ICNAAM-2014), 270005

  • Kucuk K, Aksoy CO, Basarir H, Onargan T, Genis M, Ozacar V (2011) Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling. Tunn Undergr Sp Tech 26(1):38–45. doi:10.1016/j.tust.2010.06.011

    Article  Google Scholar 

  • Kvartsberg S, Fransson Å (2013) Hydrogeological characterisation and stochastic modelling of a hydraulically conductive fracture system affected by grouting: a case study of horizontal circular drifts. Tunn Undergr Sp Tech 38:38–49

    Article  Google Scholar 

  • Lei JS, Chen JF, Cao XT, Wang QF (2013) The regression prediction analysis of grouting concretion Stone’s strength based on SVR. Adv Mater Res 859:171–176. doi:10.4028/www.scientific.net/AMR.859.171

    Article  Google Scholar 

  • Li J, Zhao Q, Fu R, Yang Y (2001) The application of artificial neural network in grouting projects. J Geol Hazards Environ Preserv 12(03):85–88 (in Chinese)

    Google Scholar 

  • Liao K, Fan J, Huang C (2011) An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts. Comput Geotech 38(8):978–986. doi:10.1016/j.compgeo.2011.07.008

    Article  Google Scholar 

  • Lin P, Zhu X, Li Q, Liu H, Yu Y (2016) Study on optimal grouting timing for controlling uplift deformation of a super high arch dam. Rock Mech Rock Eng 49(1):115–142. doi:10.1007/s00603-015-0732-z

    Article  Google Scholar 

  • Lisa H, Christian B, Åsa F, Gunnar G, Johan F (2012) A hard rock tunnel case study: characterization of the water-bearing fracture system for tunnel grouting. Tunn Undergr Sp Tech 30:132–144

    Article  Google Scholar 

  • Long J, Billaux DM (1987) From field data to fracture network modeling: an example incorporating spatial structure. Water Resour Res 23(7):1201–1216

    Article  Google Scholar 

  • Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci-UK 34(9):1044–1055

    Article  Google Scholar 

  • Lugeon M (1933) Barrages et geologic methods de recherché terrasement et un permeabilisation. Litrairedes Universite, Paris

    Google Scholar 

  • Lynch C, Dodson M, McCartney JS (2012) Grouting verification using 3-d seismic tomography. Grout Deep Mix 2012:1506–1515

    Article  Google Scholar 

  • Mortazavi A, Maadikhah A (2016) An investigation of the effects of important grouting and rock parameters on the grouting process. Geomech Geoeng 11:1–17

    Article  Google Scholar 

  • Oh H, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci-UK 37(9):1264–1276. doi:10.1016/j.cageo.2010.10.012

    Article  Google Scholar 

  • Ozturk CA, Simdi E (2014) Geostatistical investigation of geotechnical and constructional properties in Kadikoy-Kartal subway, Turkey. Tunn Undergr Sp Tech 41:35–45. doi:10.1016/j.tust.2013.11.002

    Article  Google Scholar 

  • Palmstrom A (2005) Measurements of and correlations between block size and rock quality designation (RQD). Tunn Undergr Sp Tech 20(4):362–377

    Article  Google Scholar 

  • Rafiee A, Vinches M (2008) Application of geostatistical characteristics of rock mass fracture systems in 3D model generation. Int J Rock Mech Min 45(4):644–652

    Article  Google Scholar 

  • Razack M, Lasm T (2006) Geostatistical estimation of the transmissivity in a highly fractured metamorphic and crystalline aquifer (Man-Danane Region, Western Ivory Coast). J Hydrol 325(1):164–178

    Article  Google Scholar 

  • Roman WM, Hockenberry AN, Berezniak JN, Wilson DB, Knight MA (2013) Evaluation of grouting for hydraulic barriers in rock. Environ Eng Geosci 19(4):363–375

    Article  Google Scholar 

  • Sadeghiyeh SM, Hashemi M, Ajalloeian R (2013) Comparison of permeability and groutability of Ostur Dam site rock mass for grout curtain design. Rock Mech Rock Eng 46(2):341–357. doi:10.1007/s00603-012-0282-6

    Article  Google Scholar 

  • Saeidi O, Stille H, Torabi SR (2013) Numerical and analytical analyses of the effects of different joint and grout properties on the rock mass groutability. Tunn Undergr Sp Tech 38:11–25. doi:10.1016/j.tust.2013.05.005

    Article  Google Scholar 

  • Shahin MA, Jaksa MB, Maier HR (2001) Artificial neural network applications in geotechnical engineering. Aust Geomech 36(1):49–62

    Google Scholar 

  • Shuttle DA, Dershowitz W, Glynn E, Burch S, Novak T (2000) Discrete fracture network analysis of foundation grouting. In: 4th North American rock mechanics symposium

  • Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12(1):40–45. doi:10.1016/j.asoc.2011.09.010

    Article  Google Scholar 

  • Snow DT (1968) Rock fracture spacings, openings and porosities. J Soil Mech Found Div Proc Am Soc Civil Eng 94:73–91

    Google Scholar 

  • Sohrabi-Bidar A, Rastegar-Nia A, Zolfaghari A (2015) Estimation of the grout take using empirical relationships (case study: Bakhtiari dam site). B Eng Geol Environ. doi:10.1007/s10064-015-0754-5

    Article  Google Scholar 

  • Sui W, Liu J, Hu W, Qi J, Zhan K (2015) Experimental investigation on sealing efficiency of chemical grouting in rock fracture with flowing water. Tunn Undergr Sp Tech 50:239–249. doi:10.1016/j.tust.2015.07.012

    Article  Google Scholar 

  • Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. In: Proceedings of the IFAC symposium on fuzzy information, knowledge representation and decision analysis, pp 55–60

  • Tekin E, Akbas SO (2011) Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts. B Eng Geol Environ 70(1):153–161. doi:10.1007/s10064-010-0295-x

    Article  Google Scholar 

  • Tinoco J, Correia AG, Cortez P (2011) Support vector machines in mechanical properties prediction of jet grouting columns. Semana da Engenharia 2011. http://hdl.handle.net/1822/15084

  • Tinoco J, Correia AG, Cortez P (2012) Application of a sensitivity analysis procedure to interpret uniaxial compressive strength prediction of jet grouting laboratory formulations performed by SVM model. In: ISSMGE-TC 2i 1 international symposium on ground improvement Is-Gi, 317–326

  • Tinoco J, Gomes Correia A, Cortez P (2014a) Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns. Comput Geotech 55:132–140. doi:10.1016/j.compgeo.2013.08.010

    Article  Google Scholar 

  • Tinoco J, Correia AG, Cortez P. (2014b) A novel approach to predicting Young's modulus of jet grouting laboratory formulations over time using data mining techniques. Eng Geol 169:50–60. doi:10.1016/j.enggeo.2013.11.015

    Article  Google Scholar 

  • Tran H, Hoang N (2014) An artificial intelligence approach for groutability estimation based on autotuning support vector machine. J Constr Eng 2014:1–9. doi:10.1155/2014/109184

    Article  Google Scholar 

  • Uromeihy A, Barzegari G (2007) Evaluation and treatment of seepage problems at Chapar-Abad Dam, Iran. Eng Geol 91(2):219–228

    Article  Google Scholar 

  • Utsuki S, Mito Y (2014) Experimental study of grouting for mechanical improvement of bedrock. In: ISRM international symposium-8th Asian rock mechanics symposium

  • Wang S, Hao Z (2001) The genetic algorithm-neural network method to forecast the miniature crack grouting in rock matrix. Chin J Geotech Eng 23(05):572–575

    Google Scholar 

  • Xu HF, Wang C, Li CF, Jiang M, Geng HS, Chen F (2013) Estimating diffusion radius grouting into broken rock mass. Appl Mech Mater 353–356:44–49. doi:10.4028/www.scientific.net/AMM.353-356.44

    Article  Google Scholar 

  • Yan F (2014) Theories and applications of unified grouting model and analysis in hydraulic and hydroelectric projects. Tianjin University, Tianjin (in Chinese)

    Google Scholar 

  • Yang C (2004) Estimating cement take and grout efficiency on foundation improvement for Li–Yu–Tan dam. Eng Geol 75(1):1–14. doi:10.1016/j.enggeo.2004.04.005

    Article  Google Scholar 

  • Yang X, Li Y (2008) Construction and quality analysis of curtain grouting in foundation of dam for Yangtze Three Gorges project. Geotechnical engineering for disaster mitigation and rehabilitation. Springer, Berlin, pp 790–796

    Chapter  Google Scholar 

  • Yang M, Chen M, He Y (2001) Current research state of grouting technology and its development direction in future. Chin J Rock Mech Eng 20(6):839–841

    Google Scholar 

  • Yang MJ, Yue ZQ, Lee PK, Su B, Tham LG (2002) Prediction of grout penetration in fractured rocks by numerical simulation. Can Geotech J 39(6):1384–1394. doi:10.1139/t02-063

    Article  Google Scholar 

  • Zadhesh J, Rastegar F, Sharifi F, Amini H, Nasirabad HM (2015) Consolidation grouting quality assessment using artificial neural network (ANN). Indian Geotech J 45(2):136–144. doi:10.1007/s40098-014-0116-4

    Article  Google Scholar 

  • Zettler AH, Poisel R, Reichl I, Stadler G (1997) Pressure sensitive grouting (PSG) using an artifical neural network combined with fuzzy logic. Int J Rock Mech Min 34(3):351–358

    Google Scholar 

  • Zhang L, Li Q, Song Y (2007) Neural network-based experimental study on shaft water sealing by grouting. In: Geoscience and remote sensing symposium, 2007. IGARSS 2007. IEEE International, pp 3142–3145

  • Zhong DH, Yan FG, Li MC, Huang CX, Fan K, Tang JF (2015) A real-time analysis and feedback system for quality control of dam foundation grouting engineering. Rock Mech Rock Eng 48(5):1947–1968

    Article  Google Scholar 

  • Zolfaghari A, Sohrabi Bidar A, Maleki Javan MR, Haftani M, Mehinrad A (2015) Evaluation of rock mass improvement due to cement grouting by Q-system at Bakhtiary dam site. Int J Rock Mech Min 74:38–44. doi:10.1016/j.ijrmms.2014.12.004

    Article  Google Scholar 

  • Ztürk CA, Nasuf E (2002) Geostatistical assessment of rock zones for tunneling. Tunn Undergr Sp Tech 17(3):275–285. doi:10.1016/S0886-7798(02)00023-8

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the Natural Science Foundation of China (Grant Nos. 51439005 and 51339003) and the National Basic Research Program of China 973 Program (Grant No. 2013CB035904).

Author information

Authors and Affiliations

  1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 92 Weijin Road, Nankai District, Tianjin, 300072, China

    Xiaochao Li, Denghua Zhong, Bingyu Ren, Guichao Fan & Bo Cui

Authors
  1. Xiaochao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Denghua Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bingyu Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guichao Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bo Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Denghua Zhong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Zhong, D., Ren, B. et al. Prediction of curtain grouting efficiency based on ANFIS. Bull Eng Geol Environ 78, 281–309 (2019). https://doi.org/10.1007/s10064-017-1039-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10064-017-1039-y

Keywords

Search

Navigation