Comparison of reliability evaluation methods between China and Canada standards for wood structures featuring duration of load
Bibliographic record
Abstract
Abstract Duration of load (DOL) is a key factor in design of wood structures, which makes the reliability analysis of wood structures more complicated. The importance of DOL is widely recognized, yet the methods and models through which it is incorporated into design codes vary substantially by country/region. Few investigations of the effect of different model assumptions of DOL and other random variables on the results of reliability analysis of wood structures can be found. In this paper, comparisons are made on the reliability analysis methods that underlie the China and the Canada standards for design of wood structures. Main characteristics of these two methods, especially the way how DOL is treated are investigated. Reliability analysis was carried out with the two methods employing the same set of material properties and load parameters. The resulted relationships between reliability index β and resistance partial factor γR* (the β–γR* curves) for four load combinations are compared to study the safety level indicated by the two methods. The comparison shows that the damage accumulation model (Foschi–Yao model) in the Canada analysis method is highly dependent on the type and duration of load, resulting in more conservative design than the China analysis method in loading cases dominated by dead load, but less conservative design in cases of high level of live loads. The characteristics of the load effect term of the performance function are also found to make considerable difference in reliability levels between the two methods. This study aims to provide references for researchers and standard developers in the field of wood structures.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".