Improved Performance-Based Plastic Design for RC Moment Resisting Frames: Development and a Comparative Case Study
Bibliographic record
Abstract
The performance-based plastic design (PBPD) method employs the global yield mechanism and target drift to design reinforced concrete moment resisting frames (RC-MRFs), which satisfies both the drift and strength limits without iterations. However, different structural systems have different hysteretic behaviors and the influence on the energy balance equation has not been dealt with in a quantitative manner. Moreover, the gravity loads are not considered in the plastic design procedure, which makes the beam design not within the safe margin for low and moderate seismic regions. In this paper, an improved PBPD method for RC-MRF is developed. Furthermore, a case study of seven-story RC-MRF is designed using both the improved PBPD and conventional equivalent static force design (ESFD) approaches. Comprehensive comparative analyses are performed in terms of nonlinear static pushover analysis, nonlinear dynamic analysis and seismic loss estimation. The results illustrate that the PBPD procedure can reduce the seismic losses. Hence, it is proved that PBPD is a viable and more robust design procedure as compared to the conventional ESFD procedure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".