A methodology for assessing rainfall-induced pore pressure changes in open pit slopes
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".