Skip to main content

Advertisement

Log in

A new method for classifying rock mass quality based on MCS and TOPSIS

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Rock mass quality classification is essential in rock engineering. In practical engineering the parameters of rock mass vary with sampling disturbance and testing instruments, however obey a certain distribution. In other words, the classification of rock mass quality includes an uncertainty caused by the randomness of the parameters of the rock mass in geological formations. Traditional rock mass classification methods ignore the effect of this parameter uncertainty. In this paper, we propose a new method for evaluating rock mass quality considering the effect of parameter uncertainty through a rigorous reliability analysis. The weights of the classification system indexes are obtained using the game theory, combined with the technique for order preference by similarity to ideal solution (TOPSIS) in determining the limit-state function for reliability analysis. Stochastic uncertainty analysis is performed based on Monte Carlo simulation (MCS) and the limit-state function established by TOPSIS. The rock mass quality classification grade is obtained based on the probability calculation. The TOPSIS model with accurate game theory weighting is evaluated using 25 sets of samples. The results confirmed the reliability of the model. In a case study of rock mass surrounding a cavern, we verified the proposed rock quality classification method using certainty and uncertainty methods in MATLAB. The results demonstrate that the MCS–TOPSIS coupled model is efficient and accurate for classifying rock mass quality, and this approach is easy to implement.

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

Similar content being viewed by others

References

  • Akkoyun O, Toprak ZF (2012) Fuzzy-based quality classification model for natural building stone blocks. Eng Geol 133:66–75

    Article  Google Scholar 

  • Akyol E, Kaya A, Alkan M (2016) Geotechnical land suitability assessment using spatial multi-criteria decision analysis. Arab J Geosci 9:498

    Article  Google Scholar 

  • Alberto R, Domenico C (2002) Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment. Comput Geosci 28:735–749

    Article  Google Scholar 

  • Barton NR, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. Rock Mech 6(4):189–239

    Article  Google Scholar 

  • Bieniawski ZT (1978) Determining rock mass deformability: experience from case histories. Int J Rock Mech Min Sci Geomech Abstr 15(5):237–247

    Article  Google Scholar 

  • Cen DF, Huang D (2017) Direct shear tests of sandstone under constant normal tensile stress condition using a simple auxiliary device. Rock Mech Rock Eng 50(6):1425–1438

    Article  Google Scholar 

  • Ching JY, Kok KP, Shih H (2016) Impact of statistical uncertainty on geotechnical reliability estimation. J Eng Mech 142(6):04016027. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001075

    Article  Google Scholar 

  • Deere DU (1964) Technical description of rock cores for engineering purposes. Rock Mech Eng Geol 1(1):16–22

    Google Scholar 

  • Deere DU, Hendron AJ, Patton FD, Cording EJ (1967) Design of surface and near surface construction in rock. In: Proceedings of the 8th U.S. symposium on rock mechanics failure and breakage of rock. American Institute of mining, metallurgical and petroleum engineers, Inc., New York

  • Gholami R, Rasouli V, Alimoradi A (2013) Improved RMR rock mass classification using artificial intelligence algorithms. Rock Mech Rock Eng 46:1199–1209

    Article  Google Scholar 

  • Gözde PY, Haluk A (2014) Landfill site selection utilizing TOPSIS methodology and clay liner geotechnical characterization: a case study for Ankara, Turkey. Bull Eng Geol Environ 73:369–388

    Article  Google Scholar 

  • Hoek E, Bray JW (1981) Rock slope engineering. The Institute of Mining and Metallurgy, London

    Google Scholar 

  • Huang SZ, Chang JX, Leng GY, Huang Q (2015) Integrated index for drought assessment based on variable fuzzy set theory: a case study in the Yellow River basin, China. J Hydrol 527:608–618

    Article  Google Scholar 

  • Hwang C, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, New York

    Book  Google Scholar 

  • Jain V, Sangaiah AK, Sakhuja S, Thoduka N, Aggarwal R (2016) Supplier selection using fuzzy AHP and TOPSIS: a case study in the indian automotive industry. Neural Comput Appl 11:1–10

    Google Scholar 

  • Jalalifar H, Mojedifar S, Sahebi AA, Nezamabadi H (2011) Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 38:783–790

    Article  Google Scholar 

  • Kang ZQ, Feng XT, Zhou H (2006) Application of extenics theory to evaluation of underground cavern rock quality based on stratification analysis method. Chin J Rock Mech Eng 25:3687–3693 (In Chinese)

    Google Scholar 

  • Lai CG, Chen XH, Chen XY, Wang ZL, Wu XS, Zhao SW (2015) A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory. Nat Hazards 77:1243–1259

    Article  Google Scholar 

  • Liu YC, Chen CS (2007) A new approach for application of rock mass classification on rock slope stability assessment. Eng Geol 89:129–143

    Article  Google Scholar 

  • Liu ZX, Dang WG (2014) Rock quality classification and stability evaluation of undersea deposit based on M-IRMR. Tunn Undergr Space Technol 40:95–101

    Article  Google Scholar 

  • Liu F, Zhao SZ, Weng MC, Liu YQ (2017) Fire risk assessment for large-scale commercial buildings based on structure entropy weight method. Saf Sci 94:26–40

    Article  Google Scholar 

  • Mehmet LS, Vedat D (2004) Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Eng Geol 71:303–321

    Article  Google Scholar 

  • Mert E, Yilmaz S, Inal M (2011) An assessment of total RMR classification system using unified simulation model based on artificial neural networks. Neural Comput Appl 20:603–610

    Article  Google Scholar 

  • Mojtaba G, Kamran G, Mostafa J, Siamak A (2011) A multi-dimensional approach to the assessment of tunnel excavation methods. Int J Rock Mech Min Sci 48:1077–1085

    Article  Google Scholar 

  • Rad HN, Jalali Z, Jalalifar H (2015) Prediction of rock mass rating system based on continuous functions using Chaos-ANFIS model. Int J Rock Mech Min Sci 73:1–9

    Article  Google Scholar 

  • Roszkowska E, Kacprzak D (2016) The fuzzy saw and fuzzy TOPSIS procedures based on ordered fuzzy numbers. Inf Sci 369:564–584

    Article  Google Scholar 

  • Saideep N, Sankaran M (2016) Reliability analysis under epistemic uncertainty. Reliab Eng Syst Saf 155:9–20

    Article  Google Scholar 

  • Sangaiah AK, Gopal J, Basu A, Subramaniam PR (2015) An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to gsd project outcome. Neural Comput Appl 28:111–123

    Article  Google Scholar 

  • Seaberg D, Devine L, Zhuang J (2017) A review of game theory applications in natural disaster management research. Nat Hazards 89:1461–1483

    Article  Google Scholar 

  • Selvachandran G, Peng X (2018) A modified TOPSIS method based on vague parameterized vague soft sets and its application to supplier selection problems. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3409-1

    Article  Google Scholar 

  • Umer J, Riaz MM, Ghafoor A, Cheema TA (2017) Weighted fusion of MRI and PET images based on fractal dimension. Multidimens Syst Signal Process 28:679–690

    Article  Google Scholar 

  • Wang Y, Adeyemi EA (2016a) Evaluating variability and uncertainty of geological strength index at a specific site. Rock Mech Rock Eng 49:3559–3573

    Article  Google Scholar 

  • Wang Y, Adeyemi EA (2016b) Bayesian characterization of correlation between uniaxial compressive strength and Young’s modulus of rock. Int J Rock Mech Min Sci 85:10–19

    Article  Google Scholar 

  • Wang MW, Xu XY, Li J, Jin JL, Shen FQ (2015) A novel model of set pair analysis coupled with extenics for evaluation of surrounding rock stability. Math Probl Eng 2015:892549

    Google Scholar 

  • Wang QQ, Li WP, Yan SS, Wu YL, Pei YB (2016a) GIS based frequency ratio and index of entropy models to landslide susceptibility mapping (Daguan, China). Environ Earth Sci 75:780

    Article  Google Scholar 

  • Wang Y, Cao ZJ, Li DQ (2016b) Bayesian perspective on geotechnical variability and site characterization. Eng Geol 203:117–125

    Article  Google Scholar 

  • Wu LZ, Zhou Y, Sun P, Shi JS, Liu GG, Bai LY (2017) Laboratory characterization of rainfall-induced loess slope failure. Catena 150:1–8

    Article  Google Scholar 

  • Wu LZ, Shao GQ, Huang RQ, He Q (2018a) Overhanging rock: theoretical, physical and numerical modeling. Rock Mech Rock Eng 51(11):3585–3597

    Article  Google Scholar 

  • Wu LZ, Zhang LM, Zhou Y, Xu Q, Liu GG, Bai LY (2018b) Theoretical analysis and model test for rainfall induced shallow landslides in the red-bed area of Sichuan. Bull Eng Geol Environ 77(4):1343–1353

    Article  Google Scholar 

  • Wu LZ, Deng H, Huang RQ, Zhang LM, Guo XG, Zhou Y (2019) Evolution of lakes created by landslide dams and the role of dam erosion: a case study of the Jiajun landslide on the Dadu River, China. Quat Int. https://doi.org/10.1016/j.quaint.2018.08.001

    Article  Google Scholar 

  • Zhang W, Li XB, Gong FQ (2008) Stability classification model of mine-lane surrounding rock based on distance discriminant analysis method. J Central South Univ Technol 15(1):117–120

    Article  Google Scholar 

Download references

Acknowledgements

The research reported in this paper was supported by the National Key Research and Development Program of China (no. 2018YFC1504702), the National Natural Science Foundation of China (no. 41672282, 41772334), State Key Laboratory of Geohazard Prevention and Geoenvironment Prevention Independent Research Project (no. SKLGP2017Z003), the Youth Science and Technology Innovation Team in Sichuan Province (no. 2015TD0030), and the first author thanks the Innovative Team of Chengdu University of Technology.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Zhang.

Ethics declarations

Conflict of interest

The authors of this manuscript declare that there is no conflict of interest regarding the publication of this manuscript.

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

Wu, L.Z., Li, S.H., Zhang, M. et al. A new method for classifying rock mass quality based on MCS and TOPSIS. Environ Earth Sci 78, 199 (2019). https://doi.org/10.1007/s12665-019-8171-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-019-8171-x

Keywords

Navigation