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Record W2968234261 · doi:10.5267/j.esm.2019.6.004

Optimization of steel buildings by changing structural system and using lightweight materials

2019· article· en· W2968234261 on OpenAlexvenueno aff
Hamidreza Babaali, Fereydoon Omidinasab, Ahmad Dalvand, Sh. Akhondi

Bibliographic record

VenueEngineering Solid Mechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceStructural engineeringComputer scienceMechanical engineeringEngineeringConstruction engineering

Abstract

fetched live from OpenAlex

Inappropriate use of gravity and lateral load-bearing system and the use of inappropriate materials may increase in weight of the structure. Thus, we see an increase in gravity and lateral forces and consequently the beam and column dimensions of elements increase. In this paper, by taking several samples of buildings with steel frames and number of different floors and use of different materials as well as various gravity and lateral load-bearing systems this issue was investigated. It was observed that by the use of steel bracing system in both directions of buildings with steel frames; each different load-bearing results in minimum weight loading per unit surface of the skeleton of structure. It was also observed more effect of lightweight construction by increasing the number of floors for all lateral load-bearing systems. Effects of lightweight construction for different lateral load-bearing systems was investigated and we observed that the effects of lightweight construction commonly used for buildings with moment frame system in both directions were more than the rest of the buildings with lateral load-bearing systems.

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 score0.369
Threshold uncertainty score0.696

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 head0.003
GPT teacher head0.176
Teacher spread0.173 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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