Bibliographic record
Abstract
One of the greatest challenges facing municipal engineers is the condition assessment of buried infrastructure assets. It is a mandatory process to establish and employ management strategies for these assets. Condition assessment of water mains is challenging compared to other infrastructure assets because they are typically underground, operated under pressure, and mostly they are inaccessible. To assess the condition of water mains, current research considers physical, environmental, and operational factors and their effect on different types of water mains. A condition assessment model, using the analytic hierarchy process (AHP), is developed in order to set up rehabilitation priority for water mains. Various factors are incorporated in the developed model, such as physical (pipe type, size, age, breakage rate), environmental (Cathodic protection, ground water level, soil type, surface type, and road type), and operational (Hazen-Williams factor, operational pressure). Data, which are collected from municipal engineers who are experts in water system, include pair-wise comparison matrices among factors and their sub-factors. The AHP procedure is applied to these pair-wise comparison matrices in order to generate the relative weights of each factor on a scale out of 1.0. A model is developed to determine the condition of water main based on the AHP results. Each factor weight represents the relative importance of this factor among other factors that affect water main condition. Results show that pipe age has the highest relative contribution factor among others (20.95%); then pipe material (17.49%); however, the third factor is the breakage rate (13.13%). On the other hand, the least factor is type of service (2.85%). The developed model will assist municipal expertise to prioritise pipe inspection and rehabilitation planning for their existing water mains.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".