Seismic Vulnerability Assessment in Non-Engineered Dwellings Using RVS Methods and Its Validation with a Quantitative Approach
Bibliographic record
Abstract
This research presents an analysis of seismic vulnerability in dwellings built without engineering criteria, aiming to determine their levels of vulnerability.Rapid Visual Screening (RVS) methods, such as those proposed by FEMA P-154 and developed by INDECI, were employed to assess the vulnerability of 20 dwellings.Additionally, a quantitative validation was conducted on two of these dwellings using parameters like lateral drift to complement the qualitative analysis.The results obtained through FEMA P-154 indicate that 50% of the dwellings exhibit a "Very High" level of vulnerability, 45% a "High" level, and 5% a "Low" level.Meanwhile, the INDECI method classifies 45% of the dwellings as having "Very High" vulnerability and 55% as "High."The quantitative evaluation of lateral drift showed that Dwelling 1 and Dwelling 2 experienced excessive drift values of 0.006979 and 0.004624, respectively, classifying both as "vulnerable" to seismic events.When comparing the qualitative methods (FEMA P-154 and INDECI) with the quantitative method (lateral drift), slight discrepancies were identified in the assigned vulnerability levels, although they maintained a close correlation.While the qualitative methods indicated high and very high vulnerability, the quantitative method classified them as "Vulnerable."Despite these differences, the results converge in highlighting the high susceptibility of the analyzed dwellings to seismic events.These findings emphasize the need to prioritize structural reinforcements to reduce seismic risk levels in the evaluated area.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".