www.nrc.ca/irc/ircpubs SIDD pipe bedding and Ontario Provincial Standards
Bibliographic record
Abstract
The current OPSS and OPSD that provide specifications for drainage pipes in Ontario, are shown to be a simplified and more conservative version of the traditional Marston-Spangler method. The SIDD method was developed through extensive finite element analyses. It improves the pipe installation practice by lessening the requirements in bedding and compaction, and allowing the use of native backfill materials. There is potential cost-savings in using the SIDD method. The SIDD standard adopted by ASCE/ANSI, however, seems to have missed a few important pieces of design information and is inconsistent with the original SIDD research publication in the definition of soil types. The paper shows that an improved version of the SIDD method should be considered for adoption as an alternative in the OPSS, while the traditional Marston-Spangler method is maintained in the standards. 2.1 Loads on buried pipe Design loads for rigid pipe have traditionally been determined using the Marston load theory (Marston 1930): (1) where W = backfill load per unit length; C = load factor; = unit weight of backfill material; and B = trench width, B d, at top of pipe for trench condition or pipe outside width, B c, for embankment condition. The formulas for calculating load factor, C, vary for different types of installation (Marston 1930). 2.2 Bedding factors In the Marston-Spangler method, bedding factors are used to relate the calculated external loads to the 3edge -bearing strength of pipe: (2) where S eb = 3-edge bearing strength; W = calculated external load; and B f , = bedding factor. Bedding factor, B f , is dependent on bedding angle, quality of contact between the bedding and the pipe, the supporting lateral pressure on the pipe, and the ...
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.298 | 0.107 |
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".