Mining of Mineral Deposits

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A DEMATEL-TOPSIS control model for enhancing rock mass rating reliability in weak rock conditions

Hüseyin Onur Dönmez1, Hakan Tunçdemir1, Ömür Acaroğlu1

1Istanbul Technical University, Istanbul, Turkey


Min. miner. depos. 2026, 20(3): 1-9


https://doi.org/10.33271/mining20.03.001

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      ABSTRACT

      Purpose. This study aims to improve the reliability of the classical Rock Mass Rating system by introducing a control-based DEMATEL-TOPSIS framework that explicitly incorporates parameter interactions and reduces classification uncertainty near boundary conditions.

      Methods. Parameter ratings were represented using triangular fuzzy numbers to model evaluation uncertainty, DEMATEL was applied to quantify interrelationships among classification criteria, and TOPSIS was used to determine the most representative rock mass class. The framework was validated using tunnel datasets obtained from two underground engineering projects under different geological conditions.

      Findings. Results showed that incorporating parameter interactions improves classification consistency and reduces ambiguity for rock masses located near transition intervals, while maintaining strong agreement with conventional RMR evaluations (86%). A high Spearman’s rank correlation coefficient (ρ = 0.872) confirmed the statistical compatibility between classical and controlled classifications, supporting the reliability of the proposed framework. Influential criteria within the interaction structure were also identified systematically.

      Originality. The study introduces a control-oriented extension of the RMR system that integrates parameter interdependencies into the classification process without modifying its original empirical structure. A graphical user interface enables transparent comparison between classical and controlled classification outcomes in engineering applications.

      Practical implications. The proposed framework supports engineering decision-making by identifying potential misclassifications near class boundaries and providing a user-friendly tool for tunnel and mining projects involving weak rock masses. The developed interface facilitates consistent evaluation of parameter influence levels and improves transparency during support design assessments.

      Keywords: rock mass rating; multi-criteria decision-making; DEMATEL; TOPSIS; weak rock


      REFERENCES

  1. Bieniawski, Z.T. (1973). Engineering classification of rock masses. Transactions of the South African Institution of Civil Engineers, 15, 335-344.
  2. Barton, N.R., Lien, R., & Lunde, J. (1974). Engineering classification of rock masses for the design of tunnel support. Rock Mechanics and Rock Engineering, 6(4), 189-239. https://doi.org/10.1007/BF01239496
  3. Einstein, H.H. (1991). Observation, quantification, and judgment: Terzaghi and engineering geology. Journal of Geotechnical Engineering, 117(11), 1772-1778. https://doi.org/10.1061/(ASCE)0733-9410(1991)117:11(1772)0733-9410(1991)117:11(1772))
  4. Singh, B., & Goel, R.K. (1999). Rock mass classification: A practical approach in civil engineering. Oxford, United Kingdom: Elsevier Science Ltd, 267 p. https://doi.org/10.1016/B978-0-08-043013-3.X5000-7
  5. Alejano, L.R. (2025). Rock mass classification systems: A useful rock mechanics tool, often misused: LR Alejano. Rock Mechanics and Rock Engineering, 58(10), 11147-11167. https://doi.org/10.1007/s00603-024-04087-y
  6. Nguyen, V.U. (1985). Some fuzzy set applications in mining geomechanics. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 22(6), 369-379. https://doi.org/10.1016/0148-9062(85)90002-690002-6)
  7. Juang, C.H., Lee, D.H., & Sheu, C. (1992). Mapping slope failure potential using fuzzy sets. Journal of Geotechnical Engineering, 118(3), 475-494. https://doi.org/10.1061/(ASCE)0733-9410(1992)118:3(475)0733-9410(1992)118:3(475))
  8. Habibagahi, G., & Katebi, S. (1996). Rock mass classification using fuzzy sets. Iranian Journal of Science and Technology, Transactions B: Engineering, 20(3), 273-284.
  9. Aydin, A. (2004). Fuzzy set approaches to classification of rock masses. Engineering Geology, 74(3-4), 227-245. https://doi.org/10.1016/j.enggeo.2004.03.011
  10. Khademi Hamidi, J., Shahriar, K., Rezai, B., & Bejari, H. (2010). Application of fuzzy set theory to rock engineering classification systems: An illustration of the rock mass excavability index. Rock Mechanics and Rock Engineering, 43(3), 335-350. https://doi.org/10.1007/s00603-009-0029-1
  11. Jalalifar, H., Mojedifar, S., Sahebi, A.A., & Nezamabadi-Pour, H. (2011). Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Computers and Geotechnics, 38(6), 783-790. https://doi.org/10.1016/j.compgeo.2011.04.005
  12. Daftaribesheli, A., Ataei, M., & Sereshki, F. (2011). Assessment of rock slope stability using the fuzzy slope mass rating (FSMR) system. Applied Soft Computing, 11(8), 4465-4473. https://doi.org/10.1016/j.asoc.2011.08.032
  13. Chen, C.S., & Liu, Y.C. (2007). A methodology for evaluation and classification of rock mass quality on tunnel engineering. Tunnelling and Underground Space Technology, 22(4), 377-387. https://doi.org/10.1016/j.tust.2006.10.003
  14. Saeidi, O., Torabi, S.R., & Ataei, M. (2013). Development of a new index to assess the rock mass drillability. Geotechnical and Geological Engineering, 31(5), 1477-1495. https://doi.org/10.1007/s10706-013-9672-0
  15. Jalalifar, H., Mojedifar, S., & Sahebi, A.A. (2014). Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model. International Journal of Mining Science and Technology, 24(2), 237-244. https://doi.org/10.1016/j.ijmst.2014.01.015
  16. Yardimci, A.G., & Karpuz, C. (2018). Fuzzy approach for preliminary design of weak rock slopes in lignite mines. Bulletin of Engineering Geology and the Environment, 77, 253-264. https://doi.org/10.1007/s10064-017-1022-7
  17. Song, S., Xu, Q., Chen, J., Zhang, W., Cao, C., & Li, Y. (2020). Engineering classification of jointed rock mass based on connectional expectation: A case study for Songta dam site, China. Advances in Civil Engineering, 2020, 3581963. https://doi.org/10.1155/2020/3581963
  18. Wu, S., Chen, J., & Wu, M. (2020). Study on stability classification of underground engineering surrounding rock based on concept lattice-TOPSIS. Arabian Journal of Geosciences, 13, 346. https://doi.org/10.1007/s12517-020-05320-y
  19. Bagloo, H., & Ataee-pour, M. (2024). A classification model of dimensional stones using AHP and fuzzy logic. Geotechnical and Geological Engineering, 42(2), 1173-1187. https://doi.org/10.1007/s10706-023-02611-5
  20. Dönmez, H.O., Tunçdemir, H., & Acaroğlu, Ö. (2025). Fuzzy AHP-TOPSIS framework for rock mass classification based on RMQR criteria. Geotechnical and Geological Engineering, 43, 325. https://doi.org/10.1007/s10706-025-03294-w
  21. ISRM. (1978). Suggested methods for the quantitative description of discontinuities in rock masses. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 15(6), 319-368. https://doi.org/10.1016/0148-9062(78)91472-991472-9)
  22. Bieniawski, Z.T. (1989). Engineering rock mass classifications. New York, United States: John Wiley & Sons, 272 p.
  23. Coşar, S. (2004). Application of rock mass classification systems for future support design of the Dim Tunnel near Alanya (Master’s Thesis). Ankara, Turkey: Middle East Technical University.
  24. Çümen, Ö.F., & Karakaş, A. (2021). Engineering geological investigation of the Kırık Tunnel route. Kocaeli Journal of Science and Engineering, 4(2), 93-102. https://doi.org/10.34088/kojose.904895
  25. Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X90241-X)
  26. Gabus, A., & Fontela, E. (1972). World problems: An invitation to further thought within the framework of DEMATEL. Geneva, Switzerland: Battelle Geneva Research Center.
  27. Tzeng, G.H., Chiang, C.H., & Li, C.W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028-1044. https://doi.org/10.1016/j.eswa.2006.02.004
  28. Hsu, C.W., Kuo, T.C., Chen, S.H., & Hu, A.H. (2013). Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. Journal of Cleaner Production, 56, 164-172. https://doi.org/10.1016/j.jclepro.2011.09.012
  29. Bai, C., & Sarkis, J. (2013). A grey-based DEMATEL model for evaluating business process management critical success factors. International Journal of Production Economics, 146(1), 281-292. https://doi.org/10.1016/j.ijpe.2013.07.011
  30. Dalvi-Esfahani, M., Niknafs, A., Kuss, D.J., Nilashi, M., & Afrough, S. (2019). Social media addiction: Applying the DEMATEL approach. Telematics and Informatics, 43, 101250. https://doi.org/10.1016/j.tele.2019.101250
  31. Wang, H.L., Zhao, X.F., Chen, H.J., Yi, K., Xie, W.C., & Xu, W.Y. (2023). Evaluation of toppling rock slopes using a composite cloud model with DEMATEL-CRITIC method. Water Science and Engineering, 16(3), 280-288. https://doi.org/10.1016/j.wse.2023.04.002
  32. Karuppiah, K., Garza-Reyes, J.A., & Virmani, N. (2025). Pathways to a sustainable blue economy: Exploring its barriers in an emerging economy. Business Strategy and the Environment, 34(5), 6095-6110. https://doi.org/10.1002/bse.4294
  33. Danso, S.Y. (2026). GIS-driven hybrid multicriteria model for flood susceptibility assessment in a coastal metropolis of Ghana. Geosystems and Geoenvironment, 5(1), 100462. https://doi.org/10.1016/j.geogeo.2025.100462
  34. Hwang, C.L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications – A state-of-the-art survey. Heidelberg, Germany: Springer Berlin, 269 p. https://doi.org/10.1007/978-3-642-48318-9
  35. Gauthier, T.D. (2001). Detecting trends using Spearman’s rank correlation coefficient. Environmental Forensics, 2(4), 359-362. https://doi.org/10.1006/enfo.2001.0061
  36. Li, H., Cao, Y., & Su, L. (2022). Pythagorean fuzzy multi-criteria decision-making approach based on Spearman rank correlation coefficient. Soft Computing, 26, 3001-3012. https://doi.org/10.1007/s00500-021-06615-2

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