Probabilistic evaluation of performance point in structures and investigation of the uncertainties
Bibliographic record
Abstract
The main goal of the performance based design of structures is to rationally predict the structures’ performance during earthquakes which may occur during the lifetime of the structure. In this sort of design, a specific displacement is defined as target displacement and the structure is subjected to a force in order to reach this target displacement. This design process includes uncertainties in loading, materials and analysis methods of the performance point. Therefore, statistical and probabilistic analysis should be considered. In this paper, uncertainty sources for determining the performance point are defined and then the procedures suggested in the codes are introduced. In the next step, an appropriate probability distribution function is defined for uncertainty parameters and finally the performance point of the structure is determined regarding these parameters in accordance with the codes. In addition, the sensitivity of the performance point with respect to the mentioned parameters is investigated. Results indicate that sensitivity of the performance point to geometric characteristics is of great importance and other parameters such as dead and live load stand in the second level in terms of sensitivity. An appropriate lateral loading pattern with the least uncertainty is also proposed for buildings. Key words: Performance level, performance point, probabilistic design, uncertainty, sensitivity analysis.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".