The Effect of Foundation Flexibility on Probabilistic Seismic Performance of Plan‐Asymmetric Buildings with Different Strength Distributions
Bibliographic record
Abstract
In this research, the probabilistic seismic performance of asymmetric reinforced concrete wall‐frame buildings with different strength distributions incorporating foundation flexibility effects is examined. By using probability‐based performance evaluation approach, it is possible to provide a more accurate prediction of the different strength distribution effect on the seismic performance of asymmetric buildings and find the most efficient strength distribution for meeting each performance level. These efficient distributions can be adopted in the performance‐based design of asymmetric buildings. For this purpose, first, the regression analysis and the concepts of efficiency and sufficiency were used to determine an optimal intensity measure (IM) for incremental dynamic analysis and evaluating the seismic response of the considered building models. Then, the proper magnitude of interstory drift capacity for this type of buildings in each limit state was estimated using the damage index concept. Finally, the effects of different strength distributions and the flexibility of foundation were studied on the seismic performance of the asymmetric buildings by investigating the mean annual frequencies of exceeding structural performance levels and confidence levels to satisfy performance objectives. It is concluded that irregular distributions of stiffness and strength in the plan of a building highly affect the seismic performance of buildings. Also, the results show that the optimum strength distribution is a function of the objective performance level and these optimum strength distributions are the same for both fixed‐ and flexible‐base conditions. Meanwhile, the flexible effect of foundation increases the mean annual frequencies of exceedance within the range of 10% to 45% and significantly decreases the confidence levels in most cases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".