Criticality Model to Prioritize Pipeline Rehabilitation Decisions
Bibliographic record
Abstract
Sewer networks are comprised of a huge maze of underground pipelines. They are designed and laid to transfer sewage medium to treatment plants or disposal areas. However, their conditions are subject to deterioration owing to ageing. The American Society of Civil Engineers claimed that the sewer infrastructure grade is D+. Inspection, assessment, and effective decisions are required to enhance their performance through their service life. There are several inspection methods and assessment models that can evaluate the condition of the pipelines. When evaluating the global network, the city or municipality will end up having thousands of pipeline conditions. Therefore, they confront obstacles in deciding which pipelines to tackle first. The objective of this study is to design a criticality model that is based on multiple factors that could affect the criticality of one pipeline to another. The study evaluates the weights for the environmental, economic, and public factors as well as their subfactors by using the analytic network process (ANP). The results concluded that the most important factor is the economic factor. The criticality model is implemented on an actual case study brought from the city of Edmonton, Canada. Based on the results, 20% of the pipelines are of low criticality; 70% are of medium criticality; and 10% are of high criticality. This study is expected to enhance the prioritization of the pipelines in the network for efficient future decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".