Seismic Performance of Buildings during the November 2023 Earthquake in Jajarkot, Nepal
Bibliographic record
Abstract
On November 3, 2023, a local magnitude ML 6.4 (moment magnitude, MW 5.7) earthquake struck the Ramidanda epicenter (28°50’24’’ N, 82°11’24’’ E) in Jajarkot, Nepal, at 11:47 p.m. local time (18:02 GMT), with a maximum intensity VI on the Mercalli Intensity Scale. Continuous aftershocks further devastated partially affected villages in Jajarkot, West Rukum, and Salyan. This seismic sequence stands as one of the most destructive earthquakes in Nepal since the 2015 Gorkha Earthquake, with a total death toll of 154 and over 366 people injured. The earthquake caused the complete collapse of 26,557 houses, while 35,455 houses were partially damaged. Postearthquake reconnaissance showed that the damage to masonry buildings in the affected areas was mainly due to poor construction quality, degraded construction materials, and noncompliance with codal provisions. Although reinforced concrete buildings in proximity to the main shock epicenter suffered minor damages, many of the affected structures were found to lack appropriate design or construction adherence to the national building code of Nepal. This paper, based on the postearthquake field visit, aims to present the structural damages in buildings incurred during the earthquake, discussing case histories of the affected buildings, their patterns, and the failure mechanisms. The findings highlight the critical need to enforce rigorous building codes and standards to mitigate seismic risk in vulnerable regions like Nepal.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".