Optimal scheduling of rehabilitation and inspection/condition assessment in large buried pipes
Bibliographic record
Abstract
A decision framework is described to assist municipal engineers and planners in optimising the scheduling of rehabilitation as well as inspection and condition assessment of large buried pipes. These may include water transmission pipes, trunk sewers or other buried pipes with high costs of failure and high costs of inspection/condition assessment. A semi-Markov process is used to model the deterioration of a buried asset. The life of the asset is discretised into condition states, whereby the waiting times in each state are assumed to be random variables with known probability distributions. These probability distributions can bederived in two ways. Initially, when data are scarce the probability distributions can be based on expert opinion. Over time, as observed deterioration data are collected these probability distributions are continually updated to reflect the new observations. Age-dependent transition probability matrices are compiled, using conditional survival probabilities in the various states. The expected discounted total cost associated with an asset is computed as a function of time. The time to schedule the next inspection/condition assessment is when the total expected discounted cost is minimum, while immediate intervention should be planned if the time of minimum cost is less than a threshold period (2 to 3 years) away.The proposed framework was implemented for proof of concept in a demonstration computer application. Although usable in its current form, this paper identifies some issues that require as yet unavailable data as well as more research in order to develop the framework into a comprehensive application tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".