Development of a geotechnical design standard for buildings in Canada
Bibliographic record
Abstract
Canadians are in the enviable position of having created a world-leading reliability-based geotechnical design code for bridges, namely Section 6 Foundations and Geotechnical Systems of CSA S6:19, Canadian highway bridge design code (CHBDC). Further research is now required to develop similar design provisions for the buildings, where a minimum geotechnical standard is currently lacking in Canada. It is anticipated that such design provisions would be beneficial and could be considered for a future standard. This report summarizes the findings of a study that investigated target reliability levels for geotechnical systems and the resulting resistance factors required to achieve these reliability targets within a load and resistance factor design (LRFD) framework. The geotechnical problems considered include: Seismic design of deep foundations; Seismic and wind loading design of shallow foundations; Sliding resistance of shallow foundations; and Sliding and overturning resistance of retaining walls. This report also summarizes the results of an investigation into direct reliability-based design as an alternative to the LRFD approach. While most of the unknown resistance factors needed to develop a geotechnical design standard for buildings in Canada are calibrated in this report, areas that require additional research for the calibration of geotechnical resistance factors are identified in the summary.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".