Modeling Pipe Deterioration using Soil Properties - An Application of Fuzzy Logic Expert System
Bibliographic record
Abstract
Several factors may contribute to the structural failure of cast/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 the most common method used to predict soil corrosivity potential, which is based on soil properties. For a given soil sample, each soil property is evaluated for its contribution towards the corrosivity of soil. The 10-P method uses binary logic to classify the soil, either as corrosive or non-corrosive. Fuzzy logic extends the binary logic in this context as it recognizes the real world phenomena in which each property has certain degree of membership between 0 and 1. The main objective of the present research is to develop a fuzzy logic expert system capable of establishing a criterion (such as corrosion rate or breakage rate) for predicting the deterioration of cast/ductile iron water mains using soil properties. The proposed expert system includes a fuzzy model consisting of a series of IF-THEN rules to determine soil corrosivity potential (CoP) based on soil properties. The fuzzy model contains the data of linguistic variables (database) characterizing various soil properties, and a rule base that constructs relationships among those properties and CoP. Subsequently, the expert system uses a linear regression model to link CoP to the deterioration rate of metallic pipes. A case study on cast iron pipes is examined to illustrate the application of the proposed expert system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".