Skip to main content

Advertisement

Springer Nature Link
Log in

A variable weight-based interval type-2 fuzzy rough comprehensive evaluation method for curtain grouting efficiency assessment

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Curtain grouting efficiency evaluation is vital to ensure the safety and stability of dam foundation constructions. However, the existing grouting efficiency evaluations rarely consider the intrapersonal uncertainty and interpersonal uncertainty involved in the indicator weight determination and cannot objectively reflect the impact of different indicator values on the evaluation results, which may lead to inaccurate results. To address these issues, a variable weight-based interval type-2 fuzzy rough comprehensive evaluation method is proposed. The interval type-2 fuzzy rough AHP is developed to determine the indicator weights under intrapersonal and interpersonal uncertainties, specifically, the individual linguistic judgment is expressed by interval type-2 fuzzy sets (IT2FSs), then the individual judgments are aggregated by rough sets to establish the interval type-2 fuzzy rough number to simultaneously handle individual linguistic vagueness and group preference diversity; furthermore, the multi-attributive border approximation area comparison (MABAC) method extended by variable weight theory is adopted to conduct the grouting efficiency evaluation, and it takes into account the influence of indicator value variations, where the indicator weight can be adjusted according to its actual value to obtain more objective and reliable evaluation results. Finally, the feasibility and superiority of the proposed method are illustrated through a practical case study application and comparisons with several different methods. The proposed model has powerful uncertainty handling capacity and can truly reveal the severity of a problem, ensures more objective and comprehensive evaluation results and is highly practical, which can help grouting engineers judge grouting efficiency more scientifically and effectively.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

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

References

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

    Article  Google Scholar 

  2. Zadhesh J, Rastegar F, Sharifi F et al (2015) Consolidation grouting quality assessment using artificial neural network (ANN). Indian Geotech J 45:136–144. https://doi.org/10.1007/s40098-014-0116-4

    Article  Google Scholar 

  3. Chen M, Lu WB, Zhang WJ et al (2015) An analysis of consolidation grouting effect of bedrock based on its acoustic velocity increase. Rock Mech Rock Eng 48:1259–1274. https://doi.org/10.1007/s0603-014-0624-7

    Article  Google Scholar 

  4. Hernqvist L, Fransson Å, Gustafson G et al (2009) Analyses of the grouting results for a section of the APSE tunnel at Äspö hard rock laboratory. Int J Rock Mech Min Sci 46:439–449. https://doi.org/10.1016/j.ijrmms.2008.02.003

    Article  Google Scholar 

  5. Han Z, Wang C, Zhu H (2015) Research on deep joints and lode extension based on digital borehole camera technology. Polish Marit Res 22:10–14. https://doi.org/10.1515/pomr-2015-0025

    Article  Google Scholar 

  6. Rahmani H, Submitted AT, Partial IN et al (2009) Estimation of grout distribution in a fractured rock by numerical modeling. Vancouver, Canada: The University of British Columbia Master Thesis

  7. Bryson LS, Ortiz R, Leandre J (2014) Effects of a grout curtain on hydraulic and electrical conductivity in a laboratory-scale seepage model. 3233–3242. https://doi.org/10.1061/9780784413272.314

  8. Wang X, Qin Q, Fan C (2017) Research on comprehensive evaluation for grouting effect of broken and soft floor. Arab J Geosci 10:1–7. https://doi.org/10.1007/s12517-017-3198-3

    Article  Google Scholar 

  9. 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. https://doi.org/10.1016/j.eswa.2015.09.006

    Article  Google Scholar 

  10. Zhu Y, Wang X, Deng S et al (2019) Evaluation of curtain grouting efficiency by cloud model – based fuzzy comprehensive evaluation method. KSCE J Civ Eng 23:2852–2866. https://doi.org/10.1007/s12205-019-0519-y

    Article  Google Scholar 

  11. Liu Z, Song W, Cui B et al (2019) A comprehensive evaluation model for curtain grouting efficiency assessment based on prospect theory and interval-valued intuitionistic fuzzy sets extended by improved D numbers. Energies. https://doi.org/10.3390/en12193674

    Article  Google Scholar 

  12. Yang Z, Wang Y (2020) The cloud model based stochastic multi-criteria decision making technology for river health assessment under multiple uncertainties. J Hydrol 581:124437. https://doi.org/10.1016/j.jhydrol.2019.124437

    Article  Google Scholar 

  13. Yang T, Zhang Q, Wan X et al (2020) Comprehensive ecological risk assessment for semi-arid basin based on conceptual model of risk response and improved TOPSIS model-a case study of Wei River Basin. China Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137502

    Article  Google Scholar 

  14. Kayapinar Kaya S, Aycin E (2021) An integrated interval type 2 fuzzy AHP and COPRAS-G methodologies for supplier selection in the era of Industry 4.0. Neural Comput Appl 33:10515–10535. https://doi.org/10.1007/s00521-021-05809-x

    Article  Google Scholar 

  15. Büyüközkan G, Mukul E, Kongar E (2021) Health tourism strategy selection via SWOT analysis and integrated hesitant fuzzy linguistic AHP-MABAC approach. Socioecon Plann Sci. https://doi.org/10.1016/j.seps.2020.100929

    Article  Google Scholar 

  16. Wu Y, Xu C, Ke Y et al (2019) Portfolio selection of distributed energy generation projects considering uncertainty and project interaction under different enterprise strategic scenarios. Appl Energy 236:444–464. https://doi.org/10.1016/j.apenergy.2018.12.009

    Article  Google Scholar 

  17. Dehe B, Bamford D (2015) Development, test and comparison of two Multiple Criteria Decision Analysis (MCDA) models: a case of healthcare infrastructure location. Expert Syst Appl 42:6717–6727. https://doi.org/10.1016/j.eswa.2015.04.059

    Article  Google Scholar 

  18. Moslem S, Gul M, Farooq D et al (2020) An integrated approach of best-worst method (bwm) and triangular fuzzy sets for evaluating driver behavior factors related to road safety. Mathematics. https://doi.org/10.3390/math8030414

    Article  Google Scholar 

  19. De A, Kundu P, Das S, Kar S (2020) A ranking method based on interval type-2 fuzzy sets for multiple attribute group decision making. Soft Comput 24:131–154. https://doi.org/10.1007/s00500-019-04285-9

    Article  MATH  Google Scholar 

  20. Kutlu Gündoğdu F, Duleba S, Moslem S, Aydın S (2021) Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model. Appl Soft Comput 100:106920. https://doi.org/10.1016/j.asoc.2020.106920

    Article  Google Scholar 

  21. Cheng SH (2018) Autocratic multiattribute group decision making for hotel location selection based on interval-valued intuitionistic fuzzy sets. Inf Sci (Ny) 427:77–87. https://doi.org/10.1016/j.ins.2017.10.018

    Article  MathSciNet  Google Scholar 

  22. Sang X, Zhou Y, Yu X (2019) An uncertain possibility-probability information fusion method under interval type-2 fuzzy environment and its application in stock selection. Inf Sci (Ny) 504:546–560. https://doi.org/10.1016/j.ins.2019.07.032

    Article  MathSciNet  MATH  Google Scholar 

  23. Wang H, Yao J, Zhang X, Zhang Y (2021) An area similarity measure for trapezoidal interval type-2 fuzzy sets and its application to service quality evaluation. Int J Fuzzy Syst. https://doi.org/10.1007/s40815-021-01099-6

    Article  Google Scholar 

  24. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci (Ny) 8:199–249. https://doi.org/10.1016/0020-0255(75)90036-5

    Article  MathSciNet  MATH  Google Scholar 

  25. Wu Q, Liu X, Qin J, Zhou L (2021) Multi-criteria group decision-making for portfolio allocation with consensus reaching process under interval type-2 fuzzy environment. Inf Sci (Ny). https://doi.org/10.1016/j.ins.2021.04.096

    Article  Google Scholar 

  26. Wang H, Pan X, Yan J et al (2020) A projection-based regret theory method for multi-attribute decision making under interval type-2 fuzzy sets environment. Inf Sci (Ny) 512:108–122. https://doi.org/10.1016/j.ins.2019.09.041

    Article  MathSciNet  MATH  Google Scholar 

  27. Qin J, Xi Y, Pedrycz W (2020) Failure mode and effects analysis (FMEA) for risk assessment based on interval type-2 fuzzy evidential reasoning method. Appl Soft Comput J 89:106134. https://doi.org/10.1016/j.asoc.2020.106134

    Article  Google Scholar 

  28. Gölcük İ (2020) An interval type-2 fuzzy reasoning model for digital transformation project risk assessment. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113579

    Article  Google Scholar 

  29. Efe B, Efe ÖF (2021) Quality function deployment based failure mode and effect analysis approach for risk evaluation. Neural Comput Appl 1:10159–10174. https://doi.org/10.1007/s00521-021-05778-1

    Article  Google Scholar 

  30. Chen Z, Ming X, Zhou T, Chang Y (2020) Sustainable supplier selection for smart supply chain considering internal and external uncertainty: an integrated rough-fuzzy approach. Appl Soft Comput J 87:106004. https://doi.org/10.1016/j.asoc.2019.106004

    Article  Google Scholar 

  31. Zhang Z, Chu X (2009) A new integrated decision-making approach for design alternative selection for supporting complex product development. Int J Comput Integr Manuf 22:179–198. https://doi.org/10.1080/09511920802217259

    Article  Google Scholar 

  32. Akay D, Kulak O, Henson B (2011) Conceptual design evaluation using interval type-2 fuzzy information axiom. Comput Ind 62:138–146. https://doi.org/10.1016/j.compind.2010.10.007

    Article  Google Scholar 

  33. Xu Z (2006) A note on linguistic hybrid arithmetic averaging operator in multiple attribute group decision making with linguistic information. Gr Decis Negot 15:593–604. https://doi.org/10.1007/s10726-005-9008-4

    Article  Google Scholar 

  34. Zhu GN, Hu J, Ren H (2020) A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Appl Soft Comput J 91:106228. https://doi.org/10.1016/j.asoc.2020.106228

    Article  Google Scholar 

  35. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356. https://doi.org/10.1007/BF01001956

    Article  MATH  Google Scholar 

  36. Chen Z, Ming X, Zhou T et al (2020) A hybrid framework integrating rough-fuzzy best-worst method to identify and evaluate user activity-oriented service requirement for smart product service system. J Clean Prod. https://doi.org/10.1016/j.jclepro.2020.119954

    Article  Google Scholar 

  37. Deveci M, Özcan E, John R et al (2020) A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method. J Environ Manage. https://doi.org/10.1016/j.jenvman.2020.110916

    Article  Google Scholar 

  38. Huang G, Xiao L, Zhang G (2021) Risk evaluation model for failure mode and effect analysis using intuitionistic fuzzy rough number approach. Soft Comput 25:4875–4897. https://doi.org/10.1007/s00500-020-05497-0

    Article  Google Scholar 

  39. Pamučar D, Petrović I, Ćirović G (2018) Modification of the Best-Worst and MABAC methods: a novel approach based on interval-valued fuzzy-rough numbers. Expert Syst Appl 91:89–106. https://doi.org/10.1016/j.eswa.2017.08.042

    Article  Google Scholar 

  40. Pamučar D, Ćirović G (2015) The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Syst Appl 42:3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057

    Article  Google Scholar 

  41. Liang W, Zhao G, Wu H, Dai B (2019) Risk assessment of rockburst via an extended MABAC method under fuzzy environment. Tunn Undergr Sp Technol 83:533–544. https://doi.org/10.1016/j.tust.2018.09.037

    Article  Google Scholar 

  42. Dorfeshan Y, Mousavi SM (2020) A novel interval type-2 fuzzy decision model based on two new versions of relative preference relation-based MABAC and WASPAS methods (with an application in aircraft maintenance planning). Neural Comput Appl 32:3367–3385. https://doi.org/10.1007/s00521-019-04184-y

    Article  Google Scholar 

  43. Yazdani M, Pamucar D, Chatterjee P, Chakraborty S (2020) Development of a decision support framework for sustainable freight transport system evaluation using rough numbers. Int J Prod Res 58:4325–4351. https://doi.org/10.1080/00207543.2019.1651945

    Article  Google Scholar 

  44. Wang P (1985) Shadow of fuzzy sets and random sets. Beijing Normal University Press, Beijing

    MATH  Google Scholar 

  45. Lin C, Zhang M, Zhou Z et al (2020) A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model. Tunn Undergr Sp Technol 95:103136. https://doi.org/10.1016/j.tust.2019.103136

    Article  Google Scholar 

  46. Zheng G, Wang Y, Li C, Wang X (2020) Real-time quantification of human physiological state in high temperature environments based on variable weight theory. J Therm Biol 89:102531. https://doi.org/10.1016/j.jtherbio.2020.102531

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  49. Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14:808–821. https://doi.org/10.1109/TFUZZ.2006.879986

    Article  Google Scholar 

  50. Kahraman C, Öztayşi B, Uçal Sari I, Turanoǧlu E (2014) Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Syst 59:48–57. https://doi.org/10.1016/j.knosys.2014.02.001

    Article  Google Scholar 

  51. Song W, Cao J (2017) A rough DEMATEL-based approach for evaluating interaction between requirements of product-service system. Comput Ind Eng 110:353–363. https://doi.org/10.1016/j.cie.2017.06.020

    Article  Google Scholar 

  52. Zhang YZ, Li HX (2006) Variable weighted synthesis inference method for fuzzy reasoning and fuzzy systems. Comput Math with Appl 52:305–322. https://doi.org/10.1016/j.camwa.2006.08.021

    Article  MathSciNet  MATH  Google Scholar 

  53. Javan HT, Khanlari A, Motamedi O, Mokhtari H (2018) A hybrid advertising media selection model using AHP and fuzzy-based GA decision making. Neural Comput Appl 29:1153–1167. https://doi.org/10.1007/s00521-016-2517-z

    Article  Google Scholar 

  54. Sharma HK, Roy J, Kar S, Prentkovskis O (2018) Multi criteria evaluation framework for prioritizing Indian Railway stations using modified rough AHP-Mabac method. Transp Telecommun 19:113–127. https://doi.org/10.2478/ttj-2018-0010

    Article  Google Scholar 

  55. Jain V, Sangaiah AK, Sakhuja S et al (2018) Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Comput Appl 29:555–564. https://doi.org/10.1007/s00521-016-2533-z

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the editor and the reviewers for useful comments and suggestions that helped to improve the paper.

Funding

This work was supported by the National Natural Science Foundation of China [Grant Number: 51839007], National Key R&D Program of China [Grant Number: 2018YFC0406704] and the National Natural Science Foundation of China [Grant Number: 51779169].

Author information

Authors and Affiliations

  1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, 135 Yaguan Road, Tianjin, 300350, People’s Republic of China

    Yushan Zhu, Xiaoling Wang, Wenlong Chen, Hui Guo & Dong Li

Authors
  1. Yushan Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiaoling Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wenlong Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Hui Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Dong Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

YZ: contributed to methodology, formal analysis and writing—original draft; XW: contributed to conceptualization, resources and supervision; WC: contributed to investigation and writing—review and editing; HG: contributed to writing—review and editing and visualization; DL: contributed to writing—review and editing.

Corresponding author

Correspondence to Xiaoling Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Zhu, Y., Wang, X., Chen, W. et al. A variable weight-based interval type-2 fuzzy rough comprehensive evaluation method for curtain grouting efficiency assessment. Neural Comput & Applic 34, 7851–7879 (2022). https://doi.org/10.1007/s00521-021-06864-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06864-0

Keywords