Performance assessment and prioritization of mitigation approaches for pre-seismic code structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The selection of an efficient mitigation technique from a number of alternatives to reduce the seismic risk of pre-seismic code school buildings is the main focus of this study. An on-ground survey of the school building stock in a large study area that extends for 6000 km 2 enabled the selection of four different benchmark structures. Detailed simulation models are developed for the selected benchmark buildings and 14 retrofit alternatives to define their performance criteria and assess their seismic vulnerability. The earthquake hazard of the study region is accounted for using a wide range of ground motions, representing two seismic scenarios pertinent to several medium seismicity regions. The relative seismic performance of pre-code buildings and different mitigation alternatives from a large number of dynamic response simulations up to collapse is described in terms of fragility curves as well as a proposed measure of response termed the overall performance factor. This measure of response along with the systematic seismic assessment approach adopted in this study enable prioritizing different retrofit alternatives based on their performance-to-cost ratios, which help to arrive at an efficient and cost-effective mitigation strategy for the implementation at the regional scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it