Physical analysis of earth dams from the integration of the electric resistivity geophysical method to geotechnical analysis
Bibliographic record
Abstract
Dams are structures that dam rivers and streams for a variety of purposes. These structures, often need to be sturdy to withstand the force of the impoundment and the high values of accumulated water load. The constant maintenance of these structures is essential, since a possible accident can lead to damage of catastrophic proportions. This study presents an inexpensive alternative, simple and quick application for investigation of seepage of water in Earth dams, built with distinct loan material. The research methods used were: geotechnical tests as granulometric analysis, determination of the physical indexes of soil, permeability test with permeameter of Guelph and the geophysical method of electric resistivity, from electrical resistivity tomography. At each dam, were acquired three geophysical lines parallel to the longitudinal axis of the dam. The spacing between electrodes was 2m and the array used in the study was Wenner. The results are presented from geophysical images with 2D and 3D electrical resistivity values measured and modeled, where it was possible to identify areas of low relative values of electrical resistivity, interpreted as saturated areas and likely infiltration of the body of the dam. The quantitative data of the Geotechnical testing contribute to greater understanding of the internal water flow.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".