Numerical modelling of borehole water-inflow tests in the foundation of the Alqueva arch dam
Bibliographic record
Abstract
Borehole water-inflow tests allow measurement of discharges and water pressures in isolated sections of drains and piezometric boreholes. A series of water-inflow tests and water electrical conductivity analyses were carried out in an area of the foundation of an arch dam. Detailed three-dimensional (3D) numerical models developed for the analyses of the test data in two foundation areas are presented. Results of rock mass permeability tests and areas where seepage paths cross each drain, identified with both water-inflow tests and water electrical conductivity analyses, were taken into account. Models were validated against flow rates and water pressures recorded in situ. By examining water-inflow tests using numerical models, the main flow processes are identified and quantified. Test results and conclusions drawn from the detailed 3D models were used to elaborate a global model of the foundation. The present study shows that borehole water-inflow tests add valuable information to the usual monitoring data, which improves our ability to analyse the behaviour of concrete dam foundations. It is also concluded that although discontinuum models provide a more natural representation of flow in jointed rock masses, equivalent continuum models can still be used successfully to study both global and local hydraulic behaviour of dam foundations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".