Probabilistic seismic collapse risk assessment of non-engineered masonry buildings in Malawi
Bibliographic record
Abstract
This study presents the most recent development of a nationwide earthquake risk model for non-engineered masonry buildings in Malawi. Due to its location within the East African Rift, Malawi experienced several moderate earthquakes that caused seismic damage and loss. Recently, a new probabilistic seismic hazard model has been developed by considering fault-based seismic sources, in addition to conventional areal sources. The most recent 2018 national census data provide accurate exposure information for Malawian people and their assets at detailed spatial resolutions. To develop seismic fragility functions that are applicable to Malawian housing stocks, building surveys and experimental tests of local construction materials have been conducted. By integrating these new developments of seismic hazard, exposure, and vulnerability modules, a quantitative seismic building collapse risk model for Malawi is developed on a national scale. For the rapid computation of seismic risk curves at individual locations, an efficient statistical approach for approximating the upper tail distribution of a seismic hazard curve is implemented. Using this technique, a seismic risk curve for a single location can be obtained in a few seconds, thereby, this can be easily expanded to the whole country with reasonable computational times. The results from this new quantitative assessment tool for seismic impact will provide a sound basis for risk-based disaster mitigation policies in Malawi.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".