Dam ageing diagnosis and risk analysis: Development of methods to support expert judgment
Bibliographic record
Abstract
Diagnosis and risk analysis are essential to ensure the safety of dams. Dam specialist engineers have useful methods available to help them in their task: physical modelling for assessing dam stability, statistical analysis of dam monitoring data, and, more recently, functional modelling for operational safety analysis. However, an expert's judgment is necessary when the works are complex and unique, when data are imprecise or insufficient, and when preliminary diagnosis or detailed analysis are being made. Using their experience and knowledge, dam specialist engineers are able to provide recommendations to address specific problems. The authors propose methods to support expert diagnosis and risk analysis that capitalize on the expert's knowledge and feedback. Their approach is threefold: (i) an ageing functional model based on the failure mode and effect analysis (FMEA) method using a causal graph representation of ageing scenarios leading to loss of functions; (ii) a qualitative method of describing dam ageing historical data and representing trends in performance loss; and (iii) qualitative methods to assess the risk of performance loss of dams and their components. In terms of practical applications, our research has produced a knowledge database on dam mechanisms. Also, an ageing historical database was compiled from dams that have experienced deterioration. Finally, we are developing computer aids to assist engineers in diagnosis and risk analysis tasks.Key words: dam, diagnosis, risk analysis, ageing, criticality.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".