Data acquisition and analysis for water main rehabilitation techniques
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
The ability to regularly deliver safe drinking water is a constant challenge to municipalities worldwide. In Canada, the replacement/rehabilitation cost of water mains is estimated to be $28 billion (1997–2012). Therefore, selecting cost-effective repair and/or rehabilitation scenario(s) is essential to optimise the quality of existing water mains and minimise unnecessary rehabilitation costs. The research presented in this paper identifies several rehabilitation methods for water mains, which are classified into three main categories: (1) repair (i.e. open trench, sleeves); (2) renovation (i.e. slip lining, cement mortar lining, epoxy lining, cured in place pipe (CIPP)); and (3) replacement (i.e. pipe bursting, micro-tunnelling, horizontal directional drilling, auger boring, open cut). Due to complexity, scarcity, and enormity of data required to perform life cycle cost (LCC) and select the cost-effective scenario(s), the research presented focuses on LCC data acquisition and analysis. Data were collected from contractors and municipalities in Canada. Rehabilitation decision trees were developed as a preparation step for future LCC implementation. Breakage rate analysis was successfully developed to predict the intervals of various rehabilitation alternatives. The research presented is relevant to researchers and practitioners (municipal engineers, consultants, and contractors) to prioritise pipe inspection and rehabilitation planning for 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.
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 it