Investigation of immersion influence on dynamic properties of high-speed railway subgrade with semi-rigid waterproof functional layer through field-excitation testing
Bibliographic record
Abstract
The subgrade below the under-construction Kunming–Shanghai high-speed railway faces serious stability problems induced by considerable swelling and shrinkage volume change of expansive soil along with variation of moisture content. To prevent the occurrence of potential damage, reasons for the frequent occurrence of drainage ditch breakage and platform damage along the railway corridor in expansive soil areas were analyzed. A novel integral ancillary facility and semi-rigid waterproof functional layer were developed. Large-scale field dynamic tests were carried out to investigate the fatigue performance of the waterproof functional layer and the influences of different service environments (relatively dry condition and immersion condition) on the dynamic response of the subgrade. Results show that the fatigue property and waterproofing capability of the waterproof functional layer can fulfill the standard requirements for the high-speed railway subgrade. There was an enlargement phenomenon of the dynamic stress and vibration velocity when the service environment of subgrade changed from a relatively dry condition to an immersion condition. The vibration velocity seemed to increase along the depth of the graded broken stone surface layer and decrease linearly with further depth increase from the bottom surface of the waterproof functional layer.
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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".