Remedial Grouting of Existing Embankment Dam Foundations: Lessons Learned (and Ignored)
Bibliographic record
Abstract
The authors have had intimate experience in the design, construction, and evaluation of remedial grout curtains for embankment especially dams during the “heyday” of the last 20 years in North America. Their experiences–and those of other active participants in such projects–have been widely published in the technical press, and have been incorporated in recent federal guidelines. However, not all the “lessons described” in such publications have been translated as “lessons learned,” and indeed many “lessons learned” have been ignored totally in certain quarters. The paper focuses on several topics which the authors feel merit particular attention in this regard, namely: drilling techniques for overburden and rock, design and testing of grout mixes, placement and sealing of standpipes and MPSP’s, data management systems (DMS), refusal and closure, allowable injection pressures, joint instrumentation monitoring plan, and long-term monitoring. The authors trust that the conclusions of the paper will provide guidance to engineers about to participate in a major grouting project for the first time, and comfort to more experienced engineers faced with conflicting “opinions” from unqualified but strongly opinionated “experts.”
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".