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Record W4390140118 · doi:10.47982/jfde.2023.2.a1

Off-site prefabricated hybrid façade systems

2023· article· en· W4390140118 on OpenAlexaff
Ioannis Atsonios, Emmanouil Katsigiannis, Andrianos Koklas, Dionysios I. Kolaitis, Maria A. Founti, Charalampos Mouzakis, Constantinos Tsoutis, Daniel Adamovský, Jaume Ochogavia Colom, Daniel Philippen, Alberto Diego

Bibliographic record

VenueJournal of Facade Design and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsCascades (Canada)
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsArchitectural engineeringPopularityComputer scienceEngineering

Abstract

fetched live from OpenAlex

The residential sector is responsible for the largest share of global energy consumption, while the existing building stock in Europe is relatively old. This issue, in combination with the low rate of new constructions, highlights the necessity for deep renovation of existing buildings to reach NZEB standards. At the same time, in the last decades, off-site prefabricated solutions have gained popularity in the building market, allowing the reliable and effective integration of diverse components and reducing the total renovation cost and occupants’ disturbance. The current study describes three all-in-one “Plug & Play” prefab renovation solutions and their assessment in terms of thermal, static, acoustic, and fire performance. The assessing performance is selected depending on their incorporated element as well as the national regulations of the country where the renovation solution is going to be installed. The assessment aims to ensure their characteristics’ satisfaction with the European and national requirements. In parallel, the assessment identifies the accurate behaviour of prefab façade systems both in passive and active mode and improves/optimises any possible design drawbacks.

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: none
Teacher disagreement score0.515
Threshold uncertainty score0.396

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 head0.197
Teacher spread0.182 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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