Probabilistic modeling of cast iron water distribution pipe corrosion
Bibliographic record
Abstract
Research Article| August 01 2013 Probabilistic modeling of cast iron water distribution pipe corrosion Sophie Duchesne; Sophie Duchesne 1INRS-ETE, 490 rue de la Couronne, Québec, QC, G1K 9A9, Canada E-mail: sophie.duchesne@ete.inrs.ca Search for other works by this author on: This Site PubMed Google Scholar Naoufel Chahid; Naoufel Chahid 1INRS-ETE, 490 rue de la Couronne, Québec, QC, G1K 9A9, Canada Search for other works by this author on: This Site PubMed Google Scholar Nabila Bouzida; Nabila Bouzida 1INRS-ETE, 490 rue de la Couronne, Québec, QC, G1K 9A9, Canada Search for other works by this author on: This Site PubMed Google Scholar Babacar Toumbou Babacar Toumbou 1INRS-ETE, 490 rue de la Couronne, Québec, QC, G1K 9A9, Canada Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2013) 62 (5): 279–287. https://doi.org/10.2166/aqua.2013.125 Article history Received: January 31 2013 Accepted: April 19 2013 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Sophie Duchesne, Naoufel Chahid, Nabila Bouzida, Babacar Toumbou; Probabilistic modeling of cast iron water distribution pipe corrosion. Journal of Water Supply: Research and Technology-Aqua 1 August 2013; 62 (5): 279–287. doi: https://doi.org/10.2166/aqua.2013.125 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Due to the random nature of the corrosion process, stochastic approaches are more appropriate to mathematically represent the corrosion depths on metallic objects. Based on data from 202 pipes, a model was developed to compute the probability of finding maximal corrosion depth in a given interval of values for 150-mm cast iron water distribution pipes. Only the age of pipes was taken into account as an explanatory variable to compute this probability, since the soil characteristics were not available in the close surroundings of the inspected pipes. The model combines two functions: (1) a Weibull distribution function to represent the distribution of pipe ages at the time when the maximal corrosion depth reaches 100% of the pipe wall thickness; and (2) a generalized extreme value (GEV) distribution function, with the location parameter varying as a function of pipe age, to represent the distribution of maximal corrosion pit depths on pipes that did not reach a maximal corrosion pit equal to 100% of their wall thickness. The developed model offers a good representation of the distribution of observed maximal corrosion depths for Quebec City's 150-mm cast iron water pipes. censored data, GEV distribution function, maximum likelihood, pipe age, soil characteristics, stochastic model © IWA Publishing 2013 You do not currently have access to this content.
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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 | 0.000 | 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".