Fragility curves for non-engineered masonry buildings in developing countries derived from real data based on structural surveys and laboratory tests
Bibliographic record
Abstract
Abstract Malawi is located within the southern branch of the active East African Rift System, where earthquakes of moment magnitude ( M w ) 7.0 or greater can occur along major faults. The majority of dwellings in the country are non-engineered unreinforced masonry constructions, built by local artisans with little input from engineers. These constructions are highly vulnerable to seismic events due to poor-quality materials and lack of construction detailing. This study presents a new methodology to assess the seismic fragility curves of typical dwellings located in the Central and Southern Malawi. On-site inspections of buildings are carried out to assess geometrical and structural features of 646 façades, and an experimental campaign is performed to characterise the mechanical properties of local construction materials. The collected data allow the identification of different building typologies in terms of quality of materials and construction techniques. The critical failure modes for each of the inspected façade at their ultimate limit state are evaluated analytically. Damage limit states are defined and adopted to derive simplified Static Push-Over (SPO) curves, transformed into incremental dynamic analysis (IDA) curves by using SPO2IDA. The IDA curves are then used to obtain fragility curves for the specific damage limit states. The fragility curves presented herein are the first to be calculated for these building typologies, based on local data, and unfortunately, they show that buildings in Malawi are far more vulnerable to earthquakes than estimated from previously available international reference data. The fragility curves developed in this study may prove useful for assessing the seismic risk of these building typologies in Malawi and other East African countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".