2605003831
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An Integrated Multi-Criteria Risk-Assessment Framework for Rockburst in Underground Construction

  • Shahab Hosseini 1,   
  • Nakhshin Jasimi 1,   
  • Bahar Mehdizadeh 2,   
  • Manoj Khandelwal 3,   
  • Ramesh Murlidhar Bhatawdekar 4,   
  • Pejman Sabet 5,   
  • Masoud Monjezi 1,   
  • Seyed Yaser Mousavi Siamakani 6,   
  • Danial Jahed Armaghani 7,*

Received: 18 Dec 2025 | Revised: 06 May 2026 | Accepted: 07 May 2026 | Published: 20 May 2026

Abstract

Rockburst is among the most critical hazards in underground construction, and it is caused by complex interactions among in-situ stress, rock mass conditions and excavation processes. This study proposes an integrated multi-criteria risk-assessment framework that combines failure mode and effects analysis (FMEA); value-function modelling; and the combined compromise solution (CoCoSo) methods to analyse ruckburst risk. Unlike existing rockburst risk-assessment approaches that rely on single aggregation schemes or purely predictive models, the proposed framework enables robust and consistent prioritisation of failure modes by incorporating expert judgement, multiple value-function mappings and compromise-based decision aggregation. Six weighted criteria—severity, occurrence likelihood, detection difficulty, cost, response timeliness and success likelihood—were applied to 20 representative rockburst-related failure modes. The results consistently identify microcrack development, ground-anchor failure, stress redistribution and support failure as the most critical risks across all aggregation schemes. The proposed framework provides a practical decision-support tool for underground construction projects, supporting targeted monitoring, optimised support design and proactive risk-mitigation strategies.

References 

  • 1.

    Pu, Y.; Apel, D.B.; Liu, V.; et al. Machine Learning Methods for Rockburst Prediction—State-of-the-Art Review. Int. J. Min. Sci. Technol. 2019, 29, 565–570. https://doi.org/10.1016/j.ijmst.2019.06.009.

  • 2.

    Ahmad, M.; Hu, J.-L.; Hadzima-Nyarko, M.; et al. Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study. Symmetry 2021, 13, 632. https://doi.org/10.3390/sym13040632.

  • 3.

    Armaghani, D.J.; Yang, P.; He, X.; et al. Toward Precise Long-term Rockburst Forecasting: A Fusion of SVM and Cutting-edge Meta-heuristic Algorithms. Nat. Resour. Res. 2024, 33, 2037–2062. https://doi.org/10.1007/s11053-024-10371-z.

  • 4.

    Mutaz, E.; Serati, M.; Williams, D.J. Crack Mode-changing Stress Level in Porous Rocks under Polyaxial Stress Conditions. Acta Geotech. 2024, 19, 783–803. https://doi.org/10.1007/s11440-023-01994-2.

  • 5.

    Zhang, Y.; Qiu, Y.; Du, K.; et al. Optimizing Flyrock Forecasting in Open-pit Blasting Using Hybrid Machine Learning Models. Rock Mech. Rock Eng. 2025, 58, 12523–12550. https://doi.org/10.1007/s00603-025-04730-2.

  • 6.

    Zhang, Y.; Zhou, J.; Li, J.; et al. Advancing Overbreak Prediction in Drilling and Blasting Tunnel Using MVO, SSA and HHO-based SVM Models with Interpretability Analysis. Geomech. Geophys. Geo-Energy Geo-Resour. 2025, 11, 53. https://doi.org/10.1007/s40948-025-00963-1.

  • 7.

    Rezaei, M.; Hosseini, S.P.; Jahed Armaghani, D.; et al. Improving the P-wave Velocity Determination by Considering the Effects of Joint Properties in Artificial Rock Samples. J. Min. Environ. 2025, 16, 1009–1025. https://doi.org/10.22044/jme.2024.15261.2924.

  • 8.

    Zhao, J.; Hosseini, S.; Chen, Q.; et al. Super Learner Ensemble Model: A Novel Approach for Predicting Monthly Copper Price in Future. Resour. Policy 2023, 85, 103903. https://doi.org/10.1016/j.resourpol.2023.103903.

  • 9.

    Shan, F.; He, X.; Armaghani, D.J.; et al. Success and Challenges in Predicting TBM Penetration Rate Using Recurrent Neural Networks. Tunn. Undergr. Space Technol. 2022, 130, 104728. https://doi.org/10.1016/j.tust.2022.104728.

  • 10.

    Zhao, J.; Li, D.; Zhou, J.; et al. Performance Evaluation of Rock Fragmentation Prediction Based on RF-BOA, AdaBoost-BOA, GBoost-BOA, and ERT-BOA Hybrid Models. Deep Undergr. Sci. Eng. 2025, 4, 3–17. https://doi.org/10.1002/dug2.12089.

  • 11.

    Hosseini, S.; Armaghani, D.J.; He, X.; et al. Fuzzy Cognitive Map for Evaluating Critical Factors Causing Rockbursts in Underground Construction: A Fundamental Study. Rock Mech. Rock Eng. 2024, 57, 9713–9738. https://doi.org/10.1007/s00603-024-04045-8.

  • 12.

    Ke, B.; Khandelwal, M.; Asteris, P.G.; et al. Rock-burst Occurrence Prediction Based on Optimized Naïve Bayes Models. IEEE Access 2021, 9, 91347–91360.

  • 13.

    Zhang, M.; Zhang, J.; Fan, J.; et al. Model Interpretability and Intensity Prediction of Rockbursts Using a Method Innovation Based on the QGHSCSO-CatBoost Algorithm. Rock Mech. Rock Eng. 2026, 59, 2107–2136. https://doi.org/10.1007/s00603-025-04958-y.

  • 14.

    Zhou, J.; Koopialipoor, M.; Li, E.; et al. Prediction of Rockburst Risk in Underground Projects Developing a Neuro-bee Intelligent system. Bull. Eng. Geol. Environ. 2020, 79, 4265–4279. https://doi.org/10.1007/s10064-020-01788-w.

  • 15.

    Mehdizadeh, B.; Fakharian, P.; Nouri, Y.; et al. Machine Learning-Assisted Analysis of Fracture Energy in Externally Bonded Reinforcement on Groove Bond Strength Prediction. Buildings 2026, 16, 1070.

  • 16.

    Gh, S.H.; Miyandehi, B.M.; Khotbehsara, M.M.; et al. Study quality steel mill slag for use in concrete containing metakaolin. In Proceedings of the International Conference on Advances in Engineering, Tehran, Iran, 22–23 April 2015.

  • 17.

    Ziaie, A.; Mehdizadeh, B.; Safi Jahanshahi, F.; et al. Prediction of Liquefaction-Induced lateral displacements using hybrid GBRT and EOA. J. Soft Comput. Civ. Eng. 2026, 10, e225827.

  • 18.

    Raeisi, A.; Sharbatdar, M.K.; Naderpour, H.; et al.  Flexural capacity prediction of RC beams strengthened in terms of NSM system using soft computing. J. Soft Comput. Civ. Eng. 2024, 8, 1–26.

  • 19.

    Nouri, Y.; Ghanbari, M.A.; Fakharian, P. Flexural behavior of hybrid GFRP-steel reinforced concrete beam: experimental and explainable artificial intelligence. Eng. Struct. 2025, 345, 121565.

  • 20.

    Gao, W. Forecasting of Rockbursts in Deep Underground Engineering Based on Abstraction Ant Colony Clustering Algorithm. Nat. Hazards 2015, 76, 1625–1649. https://doi.org/10.1007/s11069-014-1561-1.

  • 21.

    Ebrahimipour, V.; Rezaie, K.; Shokravi, S. An Ontology Approach to Support FMEA Studies. Expert Syst. Appl. 2010, 37, 671–677. https://doi.org/10.1016/j.eswa.2009.06.033.

  • 22.

    Zheng, L.Y.; Liu, Q.; McMahon, C.A. Integration of process FMEA with product and process design based on key characteristics. In Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology, HongKong, China, 14–16 December 2009; pp. 1673–1686.

  • 23.

    Yazdani, M.; Zarate, P.; Kazimieras Zavadskas, E.; et al. A Combined Compromise Solution (CoCoSo) Method for Multi-criteria Decision-making Problems. Manag. Decis. 2019, 57, 2501–2519. https://doi.org/10.1108/MD-05-2017-0458.

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How to Cite
Hosseini, S.; Jasimi, N.; Mehdizadeh, B.; Khandelwal, M.; Murlidhar Bhatawdekar, R.; Sabet, P.; Monjezi, M.; Mousavi Siamakani, S. Y.; Armaghani, D. J. An Integrated Multi-Criteria Risk-Assessment Framework for Rockburst in Underground Construction. Bulletin of Computational Intelligence 2026, 2 (2), 181–195. https://doi.org/10.53941/bci.2026.100010.
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