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Record W4312297981 · doi:10.18687/laccei2022.1.1.66

Comparison of the Structural Response of the Most Common Building Systems in Peru and their Impact on the Cost of Multifamily Buildings

2022· article· en· W4312297981 on OpenAlexaff
Anita Elizabet Alva Sarmiento, Yris Alejandra Samané Murrugarra, María de Los Ángeles Arana Díaz

Bibliographic record

VenueProceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions” · 2022
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsImpact
Fundersnot available
KeywordsArchitectural engineeringComputer scienceConstruction engineeringEngineering

Abstract

fetched live from OpenAlex

This research was carried out with the purpose of making known the structural characteristics of the most common construction systems in Peru: MDL, Aporticados, Masonry and Dua l to determine which is the system that presents better seismic behavior and its impact on the cost; from the documentary review performed in our thesis "Characterization of the Structural Response of the Systems Walls Of Limited Ductility, Aporticado, Masonry and Dua l and its Incidence on the Cost in Multifamily Buildings, Cajamarca 2021", where, the structural variables were analyzed: maxim um interstory displacement, basal shear, maximum drift, dynamic shea r, shear at the base, fundamental period of the structure, spectral acceleration, and cost by means of data compilation formats, which were subsequently compared by means of percentage of variation of the aforementioned parameters. The results yielded structural and economic comparisons by parameter for each system and heights considered, where low buildings were taken as 1, 2 and 3 stories, medium buildings as 5 stories, and tall buildings as 7, 8, 9 and 10 stories.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.070
GPT teacher head0.381
Teacher spread0.310 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions”Same topicFacilities and Workplace ManagementFrench-language works237,207