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Record W2726756156 · doi:10.1556/1848.2017.8.1.11

Thermal transmittance reduction through exposed balcony slabs

2017· article· en· W2726756156 on OpenAlexaff
T. T. Zhang, Phalguni Mukhopadhyaya

Bibliographic record

VenueInternational Review of Applied Sciences and Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSlabMaterials scienceThermalComposite materialBridging (networking)Thermal conductivityTransmittanceThermal transmittanceStructural engineeringMoistureElectrical conductorThermal resistanceComputer scienceEngineeringOptoelectronics

Abstract

fetched live from OpenAlex

Thermal bridging caused by exposed concrete balcony slab is a major source of heat loss through energy efficient building envelopes. Moreover, thermal bridging can also create moisture management and indoor comfort challenges. Numerous investigations have been carried out to reduce heat transmittance through exterior building envelopes and minimize the energy use in buildings. The most effective way to minimize heat transmittance of exposed concrete balcony slabs is to thermally separate the exterior structure from the interior structure using thermal breaks. To enhance thermal separation, this paper investigates the effects of replacing high conductive materials such as reinforced concrete or structural steel with a multilayer composition of high-performance hybrid insulating systems. Reinforcing bars, such as fiber reinforced plastics (FRPs), having lower thermal conductivity than steel are used to connect interior to exterior and transfer loads. Numerical simulation tool THERM is used to study the effects of thermal breaks on energy performance of the concrete slab balcony joints. Simulation results indicate significant thermal performance improvement while high-performance hybrid insulating systems were used for exposed concrete balcony slab constructions, compared to traditional insulating systems used in similar constructions

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 score0.288
Threshold uncertainty score0.265

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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