Characterization of subsurface heterogeneities in river embankments using geophysical and fiber optic techniques coupled with saline injection testing
Bibliographic record
Abstract
The behavior of river embankments in face of flood events depends not only on the levee structure itself, but also on the nature and state of the underlying sediments. In particular, the presence of natural heterogeneities in the hydraulic conductivity distribution may easily lead to preferential water flow pathways during extreme river events. In this paper, we present an integrated approach for the characterization and monitoring of embankment/subsoil structures based primarily on the use of spatially distributed techniques, and specifically 3D electrical resistivity tomography (ERT) and distributed fiber optic sensing (DFOS), both used as permanent installations in 25 m deep boreholes. Two similar field sites have been setup along the banks of the Adige river, Northern Italy. In this paper, we discuss the application details and the monitoring results at both sites, that differ in the presence of granular material and subsoil heterogeneities. In particular, in both sites, saline water injection tests have been performed and monitored using both ERT and DFOS. These tests provided fundamental information about the motion of water under changing hydraulic head in the sediments below the embankment structure, showing the presence of heterogeneities and assessing the overall permeability of these sediments.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".