UBC Theses and Dissertations
Improved empirical and numerical predictive modelling of potential tailings dam breaches and their downstream impacts Ghahramani, Masoumeh (Negar)
Tailings dams are a fundamental component of mining infrastructure as they retain mine tailings, a complex material composed of finely ground rock, water and process effluent. Tailings dam breaches (TDBs) can cause catastrophic tailings flows that travel fast, cover large areas and cause widespread inundation. The ability to understand and predict the motion of tailings flows is a crucial step in protecting people, infrastructure and the environment. This thesis aims to improve predictive empirical and numerical models of potential tailings dam breaches and their downstream impacts to help practitioners develop more reliable inundation maps, dam classifications and emergency response and preparedness plans. To do so, a new tailings flow runout classification system was first developed. A comprehensive database of 33 TDBs was then compiled, and a new volume vs. inundation area relationship was developed for tailings flow runout prediction. Comparisons with similar relationships developed for other types of mass movements indicated that tailings flows are, on average, less mobile than lahars but more mobile than non-volcanic debris flows, rock avalanches, and waste dump failures. The adaptability of four numerical models to tailings flow runout modelling was also explored by conducting back-analyses of two well-described historical TDBs through a benchmarking exercise. The results showed that all four models are capable of reproducing the bulk characteristics of the real events. However, the study also highlighted challenges in the selection of appropriate model input parameter values and the need to develop better guidance on the use of these types of models for tailings flow runout prediction. To address these challenges through improved understanding of numerical model uncertainties and sensitivities, the First-Order Second-Moment (FOSM) methodology was applied to a sub-database of 11 back-analyzed historical tailings flows using the HEC-RAS numerical model. The results showed that the total released volume is among the top contributors to the sensitivity of modelled inundation area and maximum flow depth, while surface roughness is among the top contributors to the sensitivity of modelled maximum flow velocity and flow front arrival time. The FOSM methodology was also used to demonstrate a probabilistic approach to model-based tailings flow runout prediction.
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