DOI https://doi.org/10.36487/ACG_repo/2615_53
Cite As:
Lim, A, Linero-Molina, S, Teh, M, Thompson, J & Dixon, J 2026, 'Long-term settlement of mine voids backfills', in AB Fourie, G Boggs, J Heyes & M Tibbett (eds),
Mine Closure 2026: Proceedings of the 19th International Conference on Mine Closure, Australian Centre for Geomechanics, Perth, pp. 1-13,
https://doi.org/10.36487/ACG_repo/2615_53
Abstract:
Open pit mining voids can create lasting land use constraints due to physical instability, water-related hazards and permanent changes to topography. Depending on the materials present, open pit slopes have the potential to erode or fail long after mining ceases, while abandoned voids often fill with water, forming deep, stagnant lakes that may pose safety or environmental hazards that need to be mitigated. These voids can also alter groundwater flows and disrupt surface water regimes, potentially changing drainage patterns and affecting surrounding land and environment receptors.
Backfilling voids with mine waste rock is a common strategy to achieve closure completion criteria and reduce closure liabilities by improving stability, restoring a landform that achieves visual amenity requirements, and limiting adverse impacts on water systems. However, a key challenge is determining the appropriate design backfill height, as loosely dumped waste rock material can undergo significant long-term settlement that directly impacts the final closure surface profile.
This paper presents an approach for modelling and predicting void waste rock backfill settlement, demonstrated through a specific case study. The approach aimed to provide guidance to ensure that longterm backfill levels would remain above the pre-mining watertable, thereby preventing pit lake formation and meeting closure completion criteria. A conceptual settlement model was developed, accounting for both creep and saturation-induced collapse mechanisms. Historical large-scale oedometer test results were used to illustrate the application of the settlement prediction tool. The findings offered valuable insights into prediction reliability and identified opportunities to improve the capabilities of the prediction.
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