Tool Based on the Network Method for the Verification against Failure by Piping on Retaining Structures
Bibliographic record
Abstract
In the design of retaining structures, different geotechnical phenomena must be studied so they can be classified as safe.One of them is pipping, which is a physical process related to seepage under the structure.It leads to unstable situations that might finally end in a failure of the structure.As a way to quantify this risk, an accepted calculation is to compare the critical and the estimated hydraulic gradient.This comparison depends on the geometrical scenario, geotechnical parameters and flow conditions.However, the majority of the available solutions, such as formulations and graphics, have been developed only considering isotropic soils, which means that no realistic results can be obtained since media are commonly anisotropic.The aim of this paper is to provide a methodology with which an estimation of the average exit gradient can be obtained employing a computational model based on the network method.It consists on the analogy between electrical quantities (voltage and intensity) and geotechnical variables, which are water head and groundwater flow.The safety factor is calculated in the same way whether the considered soil is isotropic or anisotropic, and, in this way, the structure can be classified as safe from a geotechnical point of view.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".