Diagnosis of embankment dam distresses using Bayesian networks. Part I. Global-level characteristics based on a dam distress database
Bibliographic record
Abstract
Dam safety has drawn increasing attention from the public. To ensure dam safety, it is essential to diagnose any dam distresses and their causes properly. The main objective of this paper is to develop a robust probability-based tool using Bayesian networks for the diagnosis of embankment dam distresses at the global level based on past dam distress data. A database of 993 distressed in-service embankment dams in China has been compiled, including general information on the dams, distresses, and causes. Based on the database, general characteristics of embankment dam distresses are studied using Bayesian networks, which can tackle not only the multiplicity of dam distresses and causes, but also the complex interrelations among them. Common patterns and causes of distresses are identified. The interrelations among the dam distresses and their causes are quantified using conditional probabilities determined based on the historical frequencies from the dam distress database. A sensitivity analysis is also conducted to identify and rank the most important factors that cause the distresses. With the prior information of common characteristics extracted from the database, Bayesian networks are further used to diagnose a specific distressed dam at the local level by combining global-level performance records and project-specific evidence in a systematic structure, which is presented in a companion paper.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".