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Record W4409800101 · doi:10.1061/jpcfev.cfeng-4902

Seismic Performance of Buildings during the November 2023 Earthquake in Jajarkot, Nepal

2025· article· en· W4409800101 on OpenAlexaff
Rajan KC, Kabin Lamichhane, Keshab Sharma, Mandip Subedi, S. Bhandari

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2025
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsUrban seismic riskEarthquake scenarioSeismic hazardSeismologyEnvironmental scienceGeologyForensic engineeringEnvironmental Seismic Intensity scaleEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.190
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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