Parametric and Non-Parametric Survival Models for “Time to Failure” of Water Pipelines: Case Study
Bibliographic record
Abstract
An application of survival analysis on Iranian water pipelines failure dataset is employed to shed additional light on the pipeline failure process as well as to extract useful information that can be helpful in future asset management planning. Survival analysis characterizes the distribution of the survival time for different groups of pipes, to compare this survival time among different type of materials. A parametric model is developed to simulate time to failure in the pipe network. The model was calibrated on the historical failure data collected over the period 1995 – 2001, and then it was tested using data since 2002. Using both parametric and non-parametric survival models makes it possible to establish a priority list for future water pipelines rehabilitation undertakings in accordance with their material type. Accordingly, it is recommended that implementation of pipeline rehabilitation projects proceeds firstly on metallic water mains, then on plastic water mains, and finally on cement water pipelines.
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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 | 0.000 | 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".