Parameterization and Evaluation of Seismic Resistance within the Context of Architectural Design
Bibliographic record
Abstract
This paper explores and discusses the possible relations between the architecture and seismic resistance of buildings. The first part of the paper stresses the practical importance of earthquake resistance in every building built in earthquake prone areas. Further on it is shown that the earthquake resistance in architectural expression can be dealt in hidden or concealed principles or on the other hand in revealed or emphasized ways. The paper hypothesize that the architectural design, which to a certain level reflects an earthquake threat, might provide a better designs with stronger architectural identity for buildings in earthquake-prone areas. In this context it summarizes the term “earthquake architecture”, which is defined as particular approach to design of buildings in architecture in a way which draws the inspiration from earthquake engineering. Such an approach might be one of the best responses of the architects (in cooperation with structural engineers and earthquake specialists) to the earthquake threats. In the second part of the paper the proposed method for recognition and evaluation of architecture in the context of earthquake resistance is presented. Using this evaluation method, it is possible to classify buildings at several different levels of “earthquake architecture”. Case study of three comparable competition projects is presented as example of using this method to evaluate the architectural design in the context of seismic resistance, and at the same time, to indicate the ways in which architecture can play its role part within earthquake resistant design.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".