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Assessment of a Tailings Dam Breach by Experimental, Numerical, and Gene-Expression Programming Model

Author

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  • Arian Eghbali

    (Razi University)

  • Mehdi Soltanabadi

    (Razi University)

  • Mitra Javan

    (Razi University)

  • Omid Mohseni

    (University of Minnesota)

Abstract

Empirical evidence from documented tailings dam failures highlights the severe consequences for economic systems, human lives, and ecological integrity. The spatial distribution and depositional configuration of tailings within the impoundment structure are regarded as the critical factors influencing the heterogeneous behavioral responses during failure events. This study uses experimental and numerical approaches to investigate the influence of a lateral slope of non-liquefied tailings on localized tailings dam breach mechanisms. The HEC-RAS 2D model was employed to simulate failure scenarios, with the numerical model calibrated against experimental data to evaluate flow characteristics and hydrograph profiles under conditions with and without a lateral slope. Gene-Expression Programming (GEP) was successfully applied to predict flood hydrographs at the failure location based on the simulated data. Results indicate that erosion in the direction perpendicular to the dam is more pronounced in the presence of a lateral tailings slope compared to the scenario without a lateral slope. While a 2% lateral slope exerts minimal influence on the outflow hydrograph, it reduces tailings erosion from the reservoir by approximately 1.3 times in localized failure scenarios. The GEP-derived formula demonstrated high accuracy in computing the flood hydrograph, offering a reliable approach for predicting tailings dam breach-induced flooding.

Suggested Citation

  • Arian Eghbali & Mehdi Soltanabadi & Mitra Javan & Omid Mohseni, 2025. "Assessment of a Tailings Dam Breach by Experimental, Numerical, and Gene-Expression Programming Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(10), pages 4763-4778, August.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:10:d:10.1007_s11269-025-04172-z
    DOI: 10.1007/s11269-025-04172-z
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    References listed on IDEAS

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    1. Khabat Khosravi & Zohreh Sheikh Khozani & Javad Hatamiafkoueieh, 2023. "Prediction of embankments dam break peak outflow: a comparison between empirical equations and ensemble-based machine learning algorithms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 1989-2018, September.
    2. Hasan Oğulcan Marangoz & Tuğce Anılan & Servet Karasu, 2024. "Investigating the Non-Linear Effects of Breach Parameters on a Dam Break Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(5), pages 1773-1790, March.
    3. Saad SH. Sammen & T. A. Mohamed & A. H. Ghazali & A. H. El-Shafie & L. M. Sidek, 2017. "Generalized Regression Neural Network for Prediction of Peak Outflow from Dam Breach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 549-562, January.
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