Calibration of design code for buried structures
Bibliographic record
Abstract
The reliability-based calibration procedures were applied to develop load and resistance factors for the Ontario Highway Bridge Design Code (1979, 1983, and 1991) and recently the Canadian Highway Bridges Design Code (2000). However, the load components for buried structures were not considered. The development of a statistical model for earth pressure requires a special approach. Therefore, this paper deals with the reliability-based calibration of the design code for buried (cut-and-cover) structures. A typical running structure consists of reinforced concrete walls forming a rectangular box section, while an underground station may have a one- to six-cell box. The major load components include earth pressure, water pressure and weight of the concrete. Other load components such as live load are relatively small. Statistical parameters are derived for representative structures and structural systems. The correlation between load components is estimated based on the available field data. Structural performance is measured in terms of the reliability index. Reliability indices are calculated for a representative spectrum of running structures and stations. In general, the reliability indices for existing buried structures are higher than those for bridges or buildings. The target reliability index has been selected on the basis of calculated reliability indices, comparison with other structures, and cost analysis (consequences of failure). The optimum load and resistance factors are calculated and recommended for the design code to achieve a uniform safety level.Key words: buried structure, code calibration, load models, reinforced concrete, reliability analysis, resistance models.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".