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Record W3011942206 · doi:10.18280/ijsse.100110

Using BIM for the Assessment of the Seismic Performance of Educational Buildings

2020· article· en· W3011942206 on OpenAlexvenueno aff
Karen Díaz, J.M. Gutiérrez, Andrea Prado, Rosanna Casadey, Gino Pannillo, Felipe Muñoz-La Rivera, Rodrigo F. Herrera, Juan Carlos Vielma

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersComisión Nacional de Investigación Científica y TecnológicaPontificia Universidad Católica de ValparaísoCorporación de Fomento de la Producción
KeywordsConstruction engineeringComputer scienceEngineeringForensic engineering

Abstract

fetched live from OpenAlex

The progress of the study of seismic vulnerability has allowed the formulation of new assessment methodologies, which take into account not only the behaviour of the structural and non-structural elements, but also the components that, due to their importance and cost, can represent an investment that in some cases becomes greater than the cost of the whole building. To carry out this more specific type of study, it is necessary to use tools that allow estimating, locating and properly characterizing the components, which has been a problem that has not yet been solved, due to the inability to maintain together all the components in a single model of a building. This paper presents the results of a research in which BIM procedures have been combined to overcome these deficiencies, successfully implementing it in the assessment of the seismic vulnerability of a set of university buildings which have been built in the middle of 1970's and 2000's, improving the quality of the information necessary to perform the numerical simulations and the consequent quantification of the damage that allowed obtaining the required repair costs, under the scenario of the occurrence of a maximum probable earthquake.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.244
Teacher spread0.234 · 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 designSimulation or modeling
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

Citations15
Published2020
Admission routes1
Has abstractyes

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