Evaluation of defective sewer pipe–induced internal erosion and associated ground deformation using laboratory model test
Bibliographic record
Abstract
Sinkholes induced by long-term internal erosion around defective sewer pipes have been widely reported. There is a need for an efficient method to understand the influence of pipe defects on internal erosion and ground settlement. This paper presents an approach to the investigation of erosion-induced ground settlement and the susceptibility of pipe bedding materials to internal erosion. A new and efficient erosion test apparatus is introduced, aided by controlling most of the key influencing parameters. The corresponding ground displacement is tracked by image correlation based on particle image velocimetry (PIV). The basic parameters investigated are (i) the process of cavity initiation and evolution, (ii) the rate of soil loss, (iii) the gradation of eroded soil, and (iv) the corresponding ground displacement. The results indicate that particles less than 0.3 mm are highly vulnerable to erosion through 5 mm openings of embedment material with a maximum particle size of 4.75 mm. The proposed method is beneficial, as it allows measurement of the deformation at any time and at any location throughout the test and facilitates checking the resistance to erosion of pipe embedment materials.
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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) | 0.000 | 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) | 0.001 | 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".