Experimental Investigation of Rehabilitated Steel Culvert Performance under Static Surface Loading
Bibliographic record
Abstract
An alternative approach to replacing corrugated steel culverts is to insert a new pipe inside the existing culvert and grout the space between them, a process known as sliplining. Though sliplining is a preferred rehabilitation approach among departments of transportation, very little research has been done to investigate the capacity enhancement provided by sliplining and how the load is shared between components in a sliplined culvert (i.e., the existing pipe, the liner, the grout, and the surrounding soil). A series of experiments was conducted on a deteriorated corrugated steel culvert at two different burial depths (600 and 900 mm) under surface loading before and after the culvert was rehabilitated with a grouted high density polyethylene (HDPE) slipliner. The rehabilitated culvert was found to be considerably stiffer and the diameter changes under surface load were reduced by more than 90% compared with the unrehabilitated culvert suggesting that the negative arching between the pipe and the soil has increased. The strains in the existing pipe were also reduced by more than 70% with negligible strain being measured in the invert of the culvert where the corrosion was concentrated. The results also indicated that when a neat grout with a compressive strength of approximately 30 MPa is used (what could be considered to be high strength in comparison with low density grouts featuring entrained air), the existing pipe and the grout carry most of the load, and the liner’s main role is to improve the hydraulic conductivity. Additionally, because of the increased stiffness of the rehabilitated pipe, the surrounding soil carries less of the surface load applied above the pipe.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".