Remedial Grouting of Existing Embankment Dam Foundations: Lessons Learned (and Ignored)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it