Bibliographic record
Abstract
Probability based design(PBD)method has some advantages against current design methods. First, it can provide the quantitative values for the structural safety or capacity through the reliability index,. That presented the certainty on the corresponding structure for the designer or user, also that permitted the broad consideration in the safety of structures. In addition, it can give the quantitative lifetime of the related structure in the calculation process of target reliability index. Also, incidental economical efficiency can be expected because decrease of required structural material can be obtained by using the practical material data. Unlikely current deterministic structural design methods, main advantage is the reflection of real condition in the structural design process by application of the data with not small clear specimen but structural size material. Advanced countries, namely America, Canada, Europe, Australia and New Zealand already converted from allowable stress design(ASD) method to PBD method and used as a standard wooden structures code in the late 1980s and 1990s. Other domestic constructions standards such as the steel or concrete constructions accepted and used the PBD methods already. Accordingly, wooden structural design method also should be converted from deterministic ASD to probabilistic LRFD(Load and resistance factor design) in order to keep pace with worldwide demands for PBD. Hence, to suggest the reason of introduction the PBD in domestic wooden structural design and analysis, a brief example was used to show the different reliability index by using the different design methods. Definition, merits and demerits of deterministic ASD and probabilistic LRFD were followed. Also the three examples were presented to show the similarity and differences between ASD and LRFD. Finally, connection problems that might cause a disputation in wooden structural design and analysis were broadly examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.006 | 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".