Integrating complex hydrogeological and geotechnical models – a discussion of methods and issues
Bibliographic record
Abstract
Prediction of pore pressure data used in complex 3D geotechnical slope stability modelling often runs into problems associated with dissimilar model domains, grids, nodal density, etc. This is often due to the larger scale hydrogeological model being restricted to the use of laterally extensive layers to represent the site lithology, whereas the geotechnical models often use a cubic or tetrahedral convex blocking method for model construction. Hydrogeological model platforms do not always allow the modeller to reproduce the geology (especially if steeply dipping, over turned, or pinching out) or the slope details to the level of detail expected for the stability modelling. To alleviate this problem, the use of regular sized elements in horizontal to moderately variable layers/slices is described. The model geometry is not new, but presented here as a means of solving some common problems encountered in pit design modelling. The resulting efficiencies in model construction, ability to modify the geology and pit wall design during the modelling process, and more accurately simulate a complex 3D problem in the hydrogeological model simulation are discussed. Methods for simulating drainage tunnels, drill hole fans, and horizontal drains using ‘discrete elements’ are presented. Additionally, the problems encountered with using larger scale (mine scale) models to determine boundary conditions for the smaller, pit wall scale models are discussed, with several methods for dealing with this reviewed. This paper describes methods used to construct a FEFLOW® (DHI-WASY GmbH, 2012) finite element model of the West Wall 3DEC® (Itasca, 2013) stability analysis for the Ok Tedi mine life extension (MLE) that overcame some of these issues. However, the methods used are not limited to FEFLOW® or even Finite Element models, and are used with other codes that the hydrogeological modellers are familiar with. The paper does not presume to be a comprehensive examination of the methods and issues, rather to provide useful tips and discussion points for the slope stability modelling audience. As such, recognised limitations of the methods are included, and the authors invite readers to use this as a means to initiate further consideration of the modelling issues involved in the increasingly complex stability analyses taking place these days.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".