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Record W2947964674 · doi:10.15866/irehm.v4i4.15002

Comparison Between Live Loads Specified by Various Standards

2016· article· en· W2947964674 on OpenAlexaboutno aff
Sami W. Tabsh

Bibliographic record

VenueInternational Journal of Earthquake Engineering and Hazard Mitigation (IREHM) · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEurocodeEngineeringWind engineeringStructural loadBuilding codeSingle-family detached homeArchitectural engineeringCivil engineeringGeographyStructural engineeringArchaeology

Abstract

fetched live from OpenAlex

A comparison is conducted between the live loads on building structures specified by major structural standards in North America, Europe and Southeast Asia. These include the American Society of Civil Engineers ASCE7, National Building Code of Canada Volume 1, British Standard 6399 Parts 1 and 3, Eurocode EN 1991-1-1, and the Hong Kong Building (Construction) Regulation 17. The study showed that the uniform live loads in the five considered standards were close for assembly areas with moveable seats, private rooms in hotels and hospitals, balconies for other than small family residences, and dance halls. However, there were significant differences between the standards for assembly areas with fixed seats, restaurants, operation rooms in hospitals, corridors and public areas, library stack rooms, passenger vehicles garages, and uninhabitable roofs. The study also indicated that the reduction in the live load on columns is based on the tributary area in North American standards, whereas it is based on the number of supported stories for the other considered standards. Finally, the load factors that are applied to the live load were somewhat similar among the various standards for the load combinations that do not include wind load, but were different for the load combinations that consider wind load.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.666

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 head0.009
GPT teacher head0.247
Teacher spread0.239 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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