Data Mining Applied to the Instrumentation Data Analysis of a Large Dam
Bibliographic record
Abstract
Dams are conceived with the purpose of bringing great benefits to society. It is expected that their construction, operation and eventual decommissioning should occur safely. If a dam breaks down the destruction scale may be very high; it may put not only the environment in the surrounding areas at risk but also human lives. Therefore, adequate design, construction and operation of dams are a worldwide concern. International guidelines aiming at dams' safety and many productive discussions about this theme have been proposed by the ICOLD -International Commission on Large Dams (ICOLD, 2007). An adequate auscultation system must be present in dams in order to monitor their structures and foundations during life cycle period. Generally an auscultation system is composed by a set of instruments installed in important points of a dam and of the subsoil where its foundation is based on. These instruments generate a large amount of data, which should be used to understand dam behavior and help engineers in decision making process involving dam safety. Usually the instrumentation readings compose a huge set where important information is mixed with non relevant data. So it would be very useful to have an automatic tool capable to point the significant information or hierarchically organize instrumentation data. The objective of this work is to present a data mining based methodology to group and organize data from a dam instrumentation system aiming to assist dam safety engineers. The purpose with this work was to select, cluster and rank 72 rods of 30 extensometers located at the F stretch of Itaipu's dam, by means of Multivariate Statistical Analysis techniques. The Principal Components Analysis was used as a method to select the extensometers' rods, Clustering Analysis identified the extensometer rods that were similar and Factor Analysis was used to rank the extensometer rods. This text is organized as follows: the second section is about Instrumentation system and its relevance to dam safety, the third section describes the KDD Process, the fourth section is a brief description of what Clustering Analysis is, the fifth section describes Itaipu's Dam, the sixth section introduces the Methodology, the seventh section shows the results and the eighth one has the conclusions.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.008 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".