A fuzzy expert system for deterioration modeling of buried metallic pipes
Bibliographic record
Abstract
This paper presents the framework of a proposed expert system that is used to predict the deterioration rate of buried metallic pipes, based on surrounding soil properties. The knowledge base of the expert system is developed using two sources of information available for evaluating the deterioration of pipes: expert knowledge and field data. The novelty of the proposed approach lies in the modeling process and the framework of the expert system, complying with the nature of the information available. The knowledge base is composed of a subjective and an objective model. The former is based upon fuzzy IF-THEN rules representing the expert knowledge obtained from published work and an expert survey. It determines the soil corrosivity potential (CoP). The objective model is a single-input-single-output (SISO) model that relates the deterioration rate (DR) to CoP. The objective model may be developed using either fuzzy modeling or a regression analysis of field data. The result of the latter is based on a set of available field data, used in a previous study, is presented.
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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 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".