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<p>This study presents a predictive framework for assessing acid mine drainage (AMD) generation risk in mine waste rock dumps by integrating fuzzy fault tree (FFT) analysis with the technique for order preference by similarity to ideal solution (TOPSIS). Unlike conventional methods that evaluate isolated parameters, this hybrid approach combines expert judgments with uncertain geological, geochemical and environmental factors to produce probabilistic risk estimates within a transparent decision-support system. The methodology was applied to a modeled copper waste dump, designed to represent typical geological and climatic conditions in sulfide-bearing environments. The analysis identifies key factors controlling AMD generation, including sulfide mineralogy, particle size, porosity, hydraulic conductivity, and precipitation. Structural and probabilistic analyses reveal that environmental parameters, particularly rainfall and oxygen availability, dominate AMD initiation and distribution, accounting for more than 80% of the overall risk. By enabling early identification of high-risk scenarios and supporting the prioritization of preventive measures, the proposed framework provides a flexible and robust tool for AMD risk assessment. It contributes to improved planning and sustainable management of mine waste facilities under uncertain conditions.</p>

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Acid Mine Drainage Risk Assessment in Waste Rock Dumps: A Fuzzy Fault Tree and TOPSIS-Based Approach

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • Asieh Hekmat,
  • Teresa Maria Valente,
  • Javiera Paz Gerding,
  • Ramón Díaz-Noriega,
  • Enrique Jelvez

摘要

This study presents a predictive framework for assessing acid mine drainage (AMD) generation risk in mine waste rock dumps by integrating fuzzy fault tree (FFT) analysis with the technique for order preference by similarity to ideal solution (TOPSIS). Unlike conventional methods that evaluate isolated parameters, this hybrid approach combines expert judgments with uncertain geological, geochemical and environmental factors to produce probabilistic risk estimates within a transparent decision-support system. The methodology was applied to a modeled copper waste dump, designed to represent typical geological and climatic conditions in sulfide-bearing environments. The analysis identifies key factors controlling AMD generation, including sulfide mineralogy, particle size, porosity, hydraulic conductivity, and precipitation. Structural and probabilistic analyses reveal that environmental parameters, particularly rainfall and oxygen availability, dominate AMD initiation and distribution, accounting for more than 80% of the overall risk. By enabling early identification of high-risk scenarios and supporting the prioritization of preventive measures, the proposed framework provides a flexible and robust tool for AMD risk assessment. It contributes to improved planning and sustainable management of mine waste facilities under uncertain conditions.