Effect of Grain Size of Granular Soils on Shear Wave Velocity and Electrical Resistivity for Levee Health Monitoring
Bibliographic record
Abstract
Levees are geologically complex earthen structures that vary laterally and vertically. Current levee inspection practices consist of mainly visual inspections of the surface with limited instrumentation that measures data at discrete locations. To better characterize and monitor the subsurface of these highly complex and spatially distributed systems, non-invasive geophysical methods, such as the multichannel analysis of surface waves (MASW) and electromagnetic induction (EMI), can be used to map the geophysical properties, i.e., shear wave velocity and apparent electric resistivity, respectively. This study focuses on investigating the effect of soil grain size on shear wave velocity and electric resistivity measurements conducted in the laboratory for a range of relative density, water content, and confining stress values. The testing program involved two types of sand: Ottawa C109 sand and Nevada sand, which have different grain sizes, with a D50 of 0.36 mm and 0.18 mm, respectively. It is shown that both shear wave velocity and electric resistivity measurements were affected by grain size and can therefore be used to distinguish between different types of sands at depth. Laboratory testing showed that the coarser sand has higher shear wave velocity at lower water contents and that shear wave velocity decreases with increasing water content. Finer sand particles exhibited lower electrical resistivity for all soil densities and water contents compared to the coarser sand specimens, but the relationship was more pronounced at lower water contents.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".