Long-term monitoring of SIDD Type IV installations
Bibliographic record
Abstract
An evaluation of the standard installation direct design (SIDD) prediction method has been undertaken by constructing and monitoring full-scale test beds installed according to SIDD Type IV specifications at four test sites across southern Ontario, Canada. Stresses around the test beds were monitored over a period of 20 months. The internal diameter of the test pipe segments varied from 600 mm to 900 mm; in situ soil conditions ranged from organic clay to sand, and burial depths varied from 1.5 to 3 times the diameter of the installed pipe. All test sections were subjected to frequent heavy traffic loads, representing a worse case loading scenario. Measurements from the 20 month monitoring period were compared with predictions from Ontario Provincial Standards and SIDD specifications. It was concluded that the SIDD method reasonably predicts the stress envelope around a buried rigid pipe installed using the cut-and-cover construction method. The indirect design method currently used by the Ontario Provincial Standards was found to provide an overly conservative prediction of soil stresses at the invert of the pipe. Field measurements also suggest that the value of the horizontal arching factor (HAF) currently recommended by SIDD for Type IV installations is overly conservative and can be increased while maintaining a conservative design approach.Key words: soil, pipe, interaction, rigid, SIDD, monitoring.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".