Simplified Progressive Collapse Analysis of RC Flat Plate for Sustainable Buildings
Bibliographic record
Abstract
Progressive collapse is a situation where local failure of a primary structural component leads to the collapse of adjoining members which, in turn, leads to additional collapse by abnormal loads. Progressive collapse of buildings may cause tremendous casualties as well as property loss, and thus is recognized by building owners and engineers as significant challenge to cope with, so design codes to prevent progressive collapse are being proposed in the U.S., the U.K., Canada and EU etc., but there are barely any studies on the progressive collapse resistance of flat plate system. Therefore, Finite element analysis for the resistance capacity on progressive collapse of RC (reinforced concrete) flat plate system was analyzed by GSA (General Service Administration) guideline of U.S. in this study. Specifically, this study will propose the problem of EB (effective beam width) method, a practical seismic analysis method of flat plate system, on progressive collapse and intends to evaluate the proper resistance capacity of flat plate system on progressive collapse. EB models and PF (plate finite element) models were compared and analyzed to judge whether progressive collapse may be analyzed in EB models. DCR (demand-capacity ratio) values were compared in the alternated path method of GSA through linear static analysis, and the vertical displacements after column removal were compared through linear dynamic analysis. Also through nonlinear static analysis, maximum load factor and maximum displacement were determined by using the detailed solid element models. It is believed that the analysis results of the resistance capacity of RC flat plate system on progressive collapse will serve as useful data in evaluating the performances of flat plate system as sustainable buildings. ?? 2012 American Scientific Publishers All rights reserved.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".