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Record W2767245298 · doi:10.1193/120116eqs218m

Seismic Vulnerability Assessment of Low‐Rise Reinforced Concrete Buildings Affected by the 2015 Gorkha, Nepal, Earthquake

2017· article· en· W2767245298 on OpenAlexafffund
Svetlana Brzev, Bishnu Pandey, Dev Kumar Maharjan, Carlos E. Ventura

Bibliographic record

VenueEarthquake Spectra · 2017
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsUniversity of British ColumbiaBritish Columbia Institute of Technology
FundersBritish Columbia Institute of Technology
KeywordsMasonryLow-riseShear wallReinforced concreteInfillUnreinforced masonry buildingVulnerability assessmentGeologyGeotechnical engineeringEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Low‐rise reinforced concrete (RC) frames with brick masonry infill walls up to five stories high have been used for housing construction in Nepal since the late 1980s. Many buildings of this type were damaged and/or collapsed in the 25 April 2015 Gorkha earthquake (M 7.8), even in areas characterized with moderate shaking intensity such as Kathmandu Valley. Due to inadequate design and/or construction of RC frame components, these buildings essentially behave like masonry shear wall structures with a shear‐dominant failure mechanism. The paper presents the findings of a field survey of 98 RC buildings affected by the 2015 earthquake. The main objective of the study was to correlate the observed damage in the buildings using the modified European macroseismic scale (EMS)‐98 and the wall index (defined as the wall area in the direction of shaking divided by the total building plan area above the level of interest). The results can be used to help establish recommendations regarding the required wall index for low‐rise RC buildings in 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.246
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2017
Admission routes2
Has abstractyes

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