Earthquake vulnerability assessment of non-engineered URM residential buildings
Bibliographic record
Abstract
In many developing countries, unreinforced masonry (URM) buildings are often constructed without following established engineering practices, increasing the risk in seismically active areas. It is therefore imperative to develop a practical and cost-effective method for assessing the seismic vulnerability of these types of residential structures. This study focuses on the development of an approach to assess the seismic vulnerability of non-engineered URM residential buildings in earthquake-prone urban areas. The methodology modifies the macroseismic vulnerability method by using existing fragility curves to simulate local damage scenarios. In addition, it incorporates multi-criteria decision making techniques, namely: (i) the Analytical Hierarchy Process; and, (ii) the Technique for Order of Preference by Similarity to Ideal Solution. These MCDM techniques are used to weigh key structural parameters and develop a comprehensive vulnerability index. Applied to developing countries, this approach provides a detailed vulnerability assessment and simulates various damage scenarios. The results confirm the robustness of the proposed methodology and highlight its potential for wider application in places with similar URM building stock and seismic context.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".