Quality Control Testing, Data Analyses, and QC Practices at Rough River Dam, Kentucky
Bibliographic record
Abstract
The Rough River Dam near Falls of Rough in Kentucky is an embankment dam with a history of high piezometric levels and problem seepage. The Louisville District of the U.S. Army Corps of Engineers (USACE) awarded the Rough River Dam Safety Modification, Phase 1B–Exploratory Drilling and Grouting project to Advanced Construction Techniques (ACT) in May 2015. Phase 1B of the drilling and grouting program was initiated in July 2015 and is anticipated to complete in late Spring 2017. The project specifications required strict QC testing and intense testing frequency through different phases of the project. A field trial mix testing program and extensive QC testing during production were performed to meet the contract requirements. This paper analyses the QC test results, including marsh time, specific gravity, bleed, pressure filtration, and unconfined compressive strength; presents a statistical analysis of the test results; a discussion of the inherent variability of the testing methods and batching equipment used; and provides recommendations for grouting QC testing methodology during production grouting.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".