Evaluation of dynamic analysis of diagrid tall steel building subjected to wind with control approach of operation and acceleration criteria
Bibliographic record
Abstract
Summary Considering the widespread use of diagrid systems in tall buildings, research on the behavior of this structural system against wind and control of comfort criteria based on acceleration seems necessary. According to various regulations, it can be found that there is not much guidance for designing this structural system, and its requirements have not yet been included in the design regulations. Therefore, one of the most important objectives of the present study is to study and apply the diagrid structural system as one of the new structural systems in high‐rise buildings. For this purpose, the effect of various parameters, including the acceleration of stories and the base shear under the dynamic wind load, was evaluated which was less studied so far. It is expected that the performance of diagrid will be evaluated by more accurately understanding the diagrid in tall structures and examining the operation and comfort criteria based on acceleration against wind load using dynamic analysis of time history in three methods: Cholesky, ergodic, and autoregressive (AR). The results were compared with the formulas of ASCE7 regulations and AIJ‐GBV‐2004 and ISO 10137: 2007 and National Research Council of Canada (NBCC) comfort criteria. Studies show that the acceleration of diagrid system stories, based on the ASCE7 dynamic‐wind response prediction equations, has exceeded the allowable limit of ASCE 7 regulations (20 gal or 20 milli‐g). This is due to the overestimation of the ASCE 7 equations relative to the results of wind time history analysis. Hence, in the 50‐story structure, the maximum roof acceleration resulting from ASCE 7 equation is 1.83 times the analytical results, in 70‐story structure 2.07 times, and in the 100‐story structure 1.87 times the results of dynamic‐wind time history 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 | 0.000 | 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) | 0.001 | 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".