Asset management analytics for urban water mains: a literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study presents a review of the state-of-the-art literature on water pipe failure predictions, assessment of water losses risk, optimal pipe maintenance plans, and maintenance coordination strategies. In addition, it provides a categorization of water main (WM) failures as well as a taxonomy of WM maintenance strategies. In particular, predictive and prescriptive analytics are highlighted with the investigation of their contributions and drawbacks from methodological and application perspectives. This review aims at providing a review of failure analytics developed recently in water mains domain either for prediction of failure or identification of optimal maintenance strategies conjointly. Future research directions and challenges are elaborated in advancing the understanding about the mechanisms leading to failures. The existing gaps between theory and practice in managing assets across water distribution networks ensuring cost-effectiveness and reliability are discussed. As knowledge about the state of the water mains and related areas is crucial, thus, this review provides an state-of-the-art update from recent studies, and accordingly, presents and discusses avenues for future research.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it