Probabilistic evaluation of performance point in structures and investigation of the uncertainties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it