Seismic Risk Assessment of Nonengineered Residential Buildings: State of the Practice
Bibliographic record
Abstract
The poor socioeconomic conditions in developing countries often lead to poorly constructed residential buildings that are particularly vulnerable to damage during an earthquake. A review of available literature carried out as part of a larger research program highlights the scarcity of existing fragility curves for the wide typology of nonengineered residential buildings around the world. Furthermore, fragility curves derived using empirical data are almost nonexistent due to the lack of postearthquake damage data and insufficient ground motion recordings in developing countries. The diversity in construction techniques and material quality in developing countries, particularly for nonengineered residential buildings, cannot be sufficiently represented through simplified or idealized analytical models. Therefore, the use of empirical based fragility curves is considered to be a well-suited approach for assessing the seismic risk levels for nonengineered residential buildings in developing countries. This paper presents a review and evaluation of existing seismic risk assessment studies and state-of-the-practice as it pertains to nonengineered buildings, and subsequently proposes the use of attenuation-based USGS ground motion and shaking intensity maps and geographic information system damage information to derive relevant fragility curves. The USGS ground motion and shaking intensity maps are proposed as they are developed using a consistent robust methodology.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".