Condition evaluation of water retaining structures by a functional approach: comparative practices in Canada and France
Bibliographic record
Abstract
Hydro-Québec and Cemagref have implemented condition evaluation methods for water retaining structures: the condition index (CI) method for embankment dams in Quebec, and SIRS Digue for fluvial dykes in France. These methods were developed from similar approaches, including functional analysis, gathering and formalization of knowledge about failure mechanisms, and definition of performance indicators. Development of both methods was done within a group of experts and lead to technical guides that explain the application limits. The main objective is to prioritize maintenance tasks within a large inventory of dams for Hydro-Quebec, and for fluvial dykes, in France. The use of these methods reaches also other goals at the same time: aid to safety review and dam diagnosis, and experience transfer among the technical staff. / Hydro-Québec et le Cemagref ont mis en ½uvre des méthodes d'évaluation de l'état des ouvrages : respectivement la méthode des indices de condition (IC) pour les barrages en remblai au Québec et le SIRS Digue pour les digues fluviales en France. Ces méthodes ont été développées à partir d'approches méthodologiques analogues, l'analyse fonctionnelle, le recueil et la formalisation de la connaissance sur les mécanismes de rupture des ouvrages et la définition d'indicateurs de performance. Leur développement a été réalisé au sein d'un groupe d'experts et a conduit à la rédaction de guides techniques constituant les règles d'application. L'objectif prioritaire recherché est la programmation des actions de maintenance au sein du parc de barrages d'Hydro-Québec et du parc de digues en France. L'utilisation de ces méthodes permet d'atteindre également des résultats sous-jacents : l'aide à la revue de sécurité et au diagnostic des ouvrages et la capitalisation de la connaissance au sein des organismes.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".