Developing An Intelligent System For Modeling The Dam Behaviour Based On Statistical Pattern Matching Of Sensory Data
Bibliographic record
Abstract
Structural condition and health monitoring is an important issue for dam safety. Dams are usually large and located in remote areas. A computerized monitoring system is very useful for detecting any abnormal behaviour in a dam. Although manual inspection is performed periodically, automatic acquisition of data from the sensors and instruments installed in a dam provide valuable information about its performance. The volume of data acquired from the sensors could be huge. Validating the sensor data, correlating them to the observation from manual inspection and interpreting the data to evaluate the performance of a dam presents a great challenge. This article presents a number of data driven models based on statistical pattern recognition technique. The models have been developed for representing the sensor data in a dam to capture the relationship between the parameters related to the cause and effect. The present models are based on statistical methods such as multi-linear regression analysis. Although such models have been used in the past, a number of limitations have been identified with the existing methods. This paper presents a more robust set of statistical pattern recognition models, where physical parameters such as water level, temperature and deformation play the key roles. The results show a marked improvement from the existing model where some of the governing parameters are artificially created. A case study using the data obtained from a Canadian dam will be presented to demonstrate the effectiveness of the proposed methods. The system developed using the proposed method can be used for validating the sensor data in immediate future, identifying anomaly in dam behaviour, and aiding in decision-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".