Fuzzy Expert System to Assess Corrosion of Cast/Ductile Iron Pipes from Backfill Properties
Bibliographic record
Abstract
Abstract: Several factors may contribute to the structural failure of cast and ductile iron water mains, the most important of which is considered to be corrosion. The ANSI/AWWA C105/A21.5-99 10-point scoring (10-P) method is commonly used to predict the corrosivity potential of a given soil sample using certain soil properties. The 10-P and other scoring methods use binary logic to classify the soil as either corrosive or noncorrosive. Fuzzy logic extends binary logic in this context as it recognizes the real world phenomena using a certain degree of membership between 0 and 1. This article presents a fuzzy logic expert system capable of predicting the deterioration of cast and ductile iron water mains based on surrounding soil properties. The proposed model consists of two modules: a knowledge base and an inference mechanism. The knowledge base provides information for better decision making and is developed in a two-tier fuzzy modeling process. First in direct approach, the expert knowledge generates a subjective model to describe the characteristics of the system using fuzzy linguistic variables. Later in system identification, the field data are used to develop an objective model, which is eventually used in conjunction with the subjective model to provide a more reliable knowledge base for the expert system. The inference mechanism uses fuzzy approximate reasoning methods to process the encoded information of the knowledge base.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".