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Record W7055465886

Comparative analysis of floor systems for high-rise buildings : Examining diverse floor systems within the context of high-rise building construction aimed to identify the most efficient solution.

2024· article· en· W7055465886 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Key (lock)Structural systemRelation (database)Identification (biology)Building design
DOInot available

Abstract

fetched live from OpenAlex

The rapid growth of metropolitan areas worldwide has led to an increasing need for tall buildings. This has resulted in the requirement for the design of efficient, strong and stable buildings that meet the environmental, economic, and structural requirements. One approach to fulfilling these requirements is by carefully selecting structural elements such as beams, columns, walls, and floors. An efficient building element integrates considerations of environmental sustainability, user comfort, and structural necessities. Therefore, during the preliminary design phase, it is crucial to conduct a comprehensive evaluation of the performance of structural elements types to identify those that effectively address these pressing needs. This master’s thesis aims to thoroughly examine the overall performance of floor systems in tall buildings. The research methodology involves doing a literature review to build a theoretical framework and identify key factors that affect the performance of floor systems. An extensive evaluation of several floor systems was carried out, taking into account numerous criteria. This analysis involved performing manual calculations for certain factors, while also utilizing available data for others. The final results were organized in tables, with the floor span used as a shared parameter. The tables were divided into distinct sections for 5-meter and 10-meter span designs. Following the results of this step, specific floor systems were selected based on their superior overall performance in relation to the indicated variables. In the next step, the chosen floors were modeled in an existing high-rise building using RFEM structural analysis software. The structural model, obtained from the Dlubal website, relates to a building located in Vancouver, Canada. The structural frame underwent a conversion from a combination of timber and concrete to a full steel frame with additional beams. Afterward, the specific floors identified were modeled individually. The findings from the first phase of the study revealed that concrete floors offer significant advantages in terms of their first natural frequency and fire resistance. In contrast, timber floors excel in terms of carbon footprint, thermal transmittance (U-value), and reduced weight, which benefits foundation design. A holistic assessment showed that precast concrete hollow core, CLT panel, and composite steel deck flooring are comparatively superior choices. As a result, these flooring types were selected for the second phase: structural analysis. The structural analysis findings indicate that the composite floor system outperforms the other alternative floors in terms of stiffness and structural integrity. However, when considering both the comparative and structural analysis results, the hollow core floor system emerges as the optimal choice in the construction of high-rise buildings.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.308
Teacher spread0.278 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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