Paper contribution

Fall of Ground Risk Assessment in Underground Excavations Using a Fuzzy Cognitive Rock Engineering System

Assiya Tursanova; Rashid Afshar; Amoussou Adoko

Proceedings
EUROCK 2026
Pages
237-242
Submitted
28 January 2026
Accepted
15 June 2026
Published
12 August 2026

Summary

Abstract

Fall of Ground (FoG) incidents remain among the most critical safety and operational hazards in underground mining. These events, including roof falls, rib spalling, and localized collapses—typically result from complex interactions between stress redistribution and structural weaknesses in the rock mass. Every year, mines worldwide continue to report FoG-related casualties despite advancements in ground support technologies. A quantitative assessment of FoG hazards is therefore essential to support the implementation of effective and reliable ground control strategies. This study introduces a causality-based Rock Engineering System (RES) for the risk assessment and prediction of FoG hazards in underground excavations. A FoG Risk Index is proposed to quantify the hazard level systematically and evaluate the influence of contributing factors. Traditional FoG assessment methods often rely heavily on qualitative judgment or are limited in their ability to handle complex interactions among geological, structural, and stress-related factors. Moreover, they frequently lack mechanisms to incorporate uncertainty and the reliability of expert input. To address these limitations, an integrated and data-driven approach that captures both the causal relationships among factors and the inherent uncertainty in expert knowledge is needed. The RES is weighted using a fuzzy cognitive map (FCM), with uncertainty and expert judgment reliability incorporated through Z-number theory—resulting in a robust framework for FoG hazard prediction. A database of 150 FoG cases from a local underground mine was compiled to develop and validate the model. The results indicate that the predicted FoG Risk Index aligns well with observed FoG events, indicating the model’s accuracy and practical applicability. It is concluded that the proposed index can be effectively used to assess FoG risk in underground excavations.

Contributors

Authors

  1. Assiya Tursanova School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan
  2. Rashid Afshar School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan
  3. Amoussou Adoko School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan ORCID 0000-0003-1396-7811

Index terms

Keywords

Full text

Attachments

Works cited

References

  1. Adoko, A. C., J. Vallejos and R. Trueman. 2020. Stability assessment of underground mine stopes subjected to stress relaxation. Mining Technology: Transactions of the Institute of Mining and Metallurgy.
  2. Adoko, A. C., M. Zhalel and P. T. Phumaphi. 2019. Fall of ground characterization: A case study of BCL Mine, Botswana. In the Procedings of 53rd U.S. Rock Mechanics/Geomechanics Symposium.
  3. Adoko, A. C., Q.-J. Zuo and L. Wu. 2011. A Fuzzy Model for High-Speed Railway Tunnel Convergence Prediction in Weak Rock. Electronic Journal of Geotechnical Engineering. 16(Q): 1275-1295.
  4. Cosma, G., D. Brown, M. Archer, M. Khan and A. Graham Pockley. 2017. A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert Systems with Applications. 70: 1-19.
  5. Hekmatnejad, A., B. Crespin, J. A. Vallejos, A. Opazo and A. C. Adoko. 2021. A hybrid predictive model of unstable rock blocks around a tunnel based on estimated volumetric fracture intensity and circular variance from borehole data sets. Tunnelling and Underground Space Technology. 111.
  6. Hudson, J. A. 1992. Rock engineering systems: theory and practice. Chichester, Ellis Horwood.
  7. Imashev, A., A. Mussin and A. C. Adoko. 2024. Investigating an Enhanced Contour Blasting Technique Considering Rock Mass Structural Properties. Applied Sciences. 14(23): 11461.
  8. Isleyen, E., S. Duzgun and R. McKell Carter. 2021. Interpretable deep learning for roof fall hazard detection in underground mines. Journal of Rock Mechanics and Geotechnical Engineering. 13(6): 1246-1255.
  9. Małkowski, P. and D. Juszyński. 2021. Roof fall hazard assessment with the use of artificial neural network. International Journal of Rock Mechanics and Mining Sciences. 143: 104701.
  10. Peng, S. 2022. Roof bolting and underground roof falls. Geohazard Mechanics.
  11. Salmi, E. F., E. J. Sellers and E. Hamman. 2025. Rock Engineering Systems for Quantifying Risks and Controls for the Resilient Design and Optimisation of Long and Ultra-long Ore Passes. Rock Mechanics and Rock Engineering. 58(11): 12359-12418.
  12. Wang, Y. J., L. S. Zhao and Y. S. Xu. 2022. Analysis of Characteristics of Roof Fall Collapse of Coal Mine in Qinghai Province, China. Applied Sciences (Switzerland). 12(3).
  13. Wu, L., A. C. Adoko and B. Li. 2018. An Illustration of Determining Quantitatively the Rock Mass Quality Parameters of the Hoek–Brown Failure Criterion. Rock Mechanics and Rock Engineering. 51(4): 1063-1076.
  14. Zhang, Z., S. Hosseini, M. Monjezi and M. Yari. 2024. Extension of reliability information of Z-numbers and fuzzy cognitive map: Development of causality-weighted rock engineering system to predict and risk assessment of blast-induced rock size distribution. International Journal of Rock Mechanics and Mining Sciences. 178: 105779.
  15. Zuo, Q., X. Li, P. Li, M. Deng and A. C. Adoko. 2024. Surrounding rock instability mechanism for fault-crossing tunnels in water-rich soft rock. Bulletin of Engineering Geology and the Environment. 83(6).