Bibliographic record
Abstract
The trenchless rehabilitation of damaged rigid sewers has become a competitive alternative to conventional methods of pipeline replacement. However, buckling caused by fluid load is identified as the important limit state in the current pipe-liner design standard, while the contribution of the damaged rigid host pipe in the assessment of resistance to earth loads as well as disturbance to the liner (e.g., vehicle loads) is neglected. Fullscale testing in the laboratory is used to investigate the soil–host and pipe–liner interaction. Two host-pipe–liner systems are examined. The first system involves a liner that fits perfectly inside a host pipe. The second system features initial lack of fit between the liner and the host pipe, and gaps across the fractures in the host pipe, to investigate ungrouted repair of rigid pipe with severe damage. The test geometry and measurement scheme to evaluate local bending and movement at the fractures in the host pipe are described. Key parameters affecting local bending are identified, including initial lack of fit between the liner and the host pipe as well as the hoop stiffness of the host pipe. This recent research on repair of sanitary and storm water sewers is discussed in the context of culvert rehabilitation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".