On the Performance-Based Engineering Concepts for Historic Structures: Challenges and Expectations
Bibliographic record
Abstract
The development and application of performance-based engineering concepts in seismic design has been a primary focus of work in earthquake engineering during the last few decades. Although performance-based design and evaluation of masonry structures has been advanced, considerable effort is still required, for example in the application of the methods to historic masonry. Historic structures are of great importance to current and future generations as they convey historical and cultural aspects of past civilizations. Some of those structures have survived earthquakes for centuries while others collapsed, revealing our lack of knowledge concerning the seismic behaviour of such structures. Historic structures are typically massive and stiff and can be vulnerable to seismic events - even ones of low to moderate severity. The seismic vulnerability of such structures arises possibly due to the attraction of high inertial forces, the lack of ductility to dissipate seismic energy and/or the deterioration and weakening of the material over time. The seismic vulnerability of a structure is a function of the interaction of ground motion parameters and the structure itself. Here, we explore the seismic vulnerability of stone structures, strength and deformation, elastic moduli ratio, and damping mechanisms and ratios: all are needed to understand the seismic performance of historic structures.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".