Large-scale experimental evaluation of soil saturation effect on behaviour of buried pipes under operational loads
Bibliographic record
Abstract
The behaviour of pipelines in unsaturated soil is fundamentally different from that of pipelines buried in dry or saturated soils. However, the effect of soil saturation on buried pipe behaviour has been overlooked in past research mainly due to the very limited large-scale experimental studies available. Most of the available studies on buried pipes in unsaturated soil are based on numerical modelling that analyses the pipe behaviour using a calibrated soil model developed on the basis of fundamental unsaturated soil characteristics. Investigations with such approaches may not be acceptable owing largely to unverified or complicated pipe–soil interactions and three-dimensional stress re-distributions. In this paper, the effect of soil saturation on buried pipe behaviour is investigated using a comprehensive large-scale experimental setup. A detailed methodology of large-scale testing, which was used to obtain pipe deformations as well as soil stresses with reference to a cast iron pipeline buried in low-plasticity clay under different soil saturation levels, is presented. The results obtained from large-scale experiments are compared with those of three-dimensional finite element analysis. Results produced in this paper reveal that the degree of water saturation of backfill soil can significantly affect the pipe deformation under internal and external loadings.
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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 | 0.001 |
| 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".