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Record W4380991493 · doi:10.1051/e3sconf/202339603030

The BeTOP facility for performance testing of building systems

2023· article· en· W4380991493 on OpenAlexafffundabout
Umberto Berardi

Bibliographic record

VenueE3S Web of Conferences · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSystems engineeringScale (ratio)Computer scienceArchitectural engineeringField (mathematics)Efficient energy useReliability engineeringEnergy performanceFull scaleEngineering

Abstract

fetched live from OpenAlex

Proposing new materials and systems to improve buildings' performance and energy efficiency often requires testing their performance in the field. Experimental performance characterization of new and existing building systems is crucial to understanding their behaviour. Full-scale experimental test cell facilities have been at the forefront of experimental performance evaluation in building-related research, as they provide a realistic representation of buildings, including environmental conditions, assembling challenges, and operational characteristics. In this paper, trends in the design and construction of outdoor testing facilities are first discussed. Then, based on the current literature and the knowledge gained through visits to multiple facilities, the new test cell facility “BeTOP”, located in Toronto (Ontario), is described. BeTOP is a full-scale experimental facility with the capacity to perform multiple experimental tests simultaneously. This paper describes its characteristics, including structure details, testing capabilities, system details, current monitoring campaigns, and future testing potential. The paper concludes by showing that the design of a full-scale testing facility is crucial to observe the long-term performance of new systems under variable boundary conditions in a continental climate with cold winters and hot and humid summers.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.224

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 head0.073
GPT teacher head0.310
Teacher spread0.237 · 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 designOther design
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
Published2023
Admission routes3
Has abstractyes

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