The stiffness of unsaturated railway formations
Bibliographic record
Abstract
The rational design of a substructure to support a rail track requires an estimation of the stiffness value of the formation on which it is to be built. Stiffness values derived from back-analyses of deformations of the ground beneath the track have been found by the authors to be much higher than those predicted from laboratory element testing on saturated specimens. This may be because of differences in compaction between field and laboratory, or because suctions created by lack of saturation play a key role in controlling stiffness, and therefore the performance of the track when in use. To test the latter hypothesis a laboratory study has been carried out on material representative of that found in South African railway formations. This was tested at constant dry density and various water contents, with matric suctions determined using different established techniques, and very-small-strain stiffness levels obtained from resonant column testing. A suction stress characteristic curve was developed to identify the contribution of suction to the overall effective stress for this material. The results show that suction can indeed be an important contributing factor to the magnitude of stiffness. For material tested at constant dry density, the stiffness initially increases with reducing compaction water content, and therefore with increasing suction. It subsequently reduces back towards the saturated value as the compaction water content approaches zero, even though the matric suction continues to increase. The relative increase in very-small-strain stiffness due to suction depends, to a large extent, on the net normal stress during the stiffness measurement. The effect of matric suction is proportionately greatest at the low net normal stress levels that apply for shallow infrastructures such as rail formations. Also, the operational stiffness depends not only on the current water content (and therefore suction), but also on the water content at which the material has been compacted.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".