A methodology for assessing rainfall-induced pore pressure changes in open pit slopes

Authors: Bellin, J; Raynor, M; Kettle, R; Tasoren, K

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DOI https://doi.org/10.36487/ACG_repo/2025_89

Cite As:
Bellin, J, Raynor, M, Kettle, R & Tasoren, K 2020, 'A methodology for assessing rainfall-induced pore pressure changes in open pit slopes', in PM Dight (ed.), Slope Stability 2020: Proceedings of the 2020 International Symposium on Slope Stability in Open Pit Mining and Civil Engineering, Australian Centre for Geomechanics, Perth, pp. 1305-1318, https://doi.org/10.36487/ACG_repo/2025_89

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Abstract:
Rainfall-induced pore pressure responses are a well-known, yet mainly poorly understood, driver for slope instability. This knowledge gap is particularly relevant at shallow depths where slopes are more sensitive to changes in pore pressures. The goal of this paper is to increase the industry’s understanding of the controls on rainfall-driven pore pressure fluctuations through in-depth data analysis of measured pore pressure responses at an operational mine site using a suite of semi-automated tools. A methodology is presented for analysis and correlation of rainfall data with observed pore pressure response using Python. Empirical relationships are presented between extreme rainfall events and the magnitude and lag time of the resultant pore pressure response. The impact of short-term extreme events is compared with longer-term interannual variations in rainfall and measured pore pressure for the example site. Pore pressure trends are compared with the historical failure database in an attempt to reconcile the timing of slope failures with rainfall events and observed pore pressure responses. The paper provides a valuable methodology for those seeking to incorporate extreme events and climatic variability into a risk-based slope design process. The implications of the review for groundwater management plans and rainfall trigger-action-response-plans (TARPs) are also discussed.

Keywords: vibrating wire piezometer, pore pressure, extreme rainfall, Python, data management

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