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Record W7054859922

Assessment and Mitigation of Overheating Risks in Archetype and Existing Canadian Buildings under Recent and Projected Future Climates

2022· dissertation· en· W7054859922 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2022
Typedissertation
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsOverheating (electricity)RetrofittingNatural ventilationThermal comfortBuilding envelopeHabitabilityRisk assessmentPassive houseClimate changeControlled ventilation
DOInot available

Abstract

fetched live from OpenAlex

This research aims to develop a framework to assess and mitigate the overheating risks under projected future climates for both archetype and existing buildings. More specific objectives are to 1) determine the contribution and correlation of individual building envelope parameters to the change in indoor temperature in conjunction with ventilation, therefore, to determine whether high-energy-efficient buildings required by Canadian building codes to reduce heating consumption in new buildings are at lower or greater overheating risk compared to old buildings; 2) develop an automated calibration procedure to calibrate a building simulation model based on the indoor hourly temperature to achieve high accuracy to be used overheating studies in existing buildings; 3) assess overheating risks under current and future extreme years and recommend effective mitigation measures; and 4) provide an optimal design for retrofitting existing buildings to achieve lowest heating energy demand and highest thermal and visual comfort in new building design. To achieve these objectives, a robust sensitivity-analysis (SA) and calibration method, a systematic framework for evaluating overheating and passive mitigation measures, and an optimization methodology are developed and applied to an archetype detached-house and existing-school-buildings. 
\nThe results showed that the archetype and existing Canadian buildings have experienced overheating under current climates and the overheating risks will increase dramatically under future climates. Due to the positive contribution of lower U-values of windows, walls, and roofs and SHGC, high-energy-efficient houses have a lower overheating risk than old buildings if adequate ventilation (>2.2-ACH) is provided. Natural ventilation in the high-energy-efficient house is sufficient to reduce the overheating risk under the recent climate but will require adding interior and exterior shading under future climates. For existing-school buildings, the calibrated model achieved high accuracy. The results also showed that the use of exterior blind roll or a combination of night cooling and other mitigation measures that reduce solar heat gain is required under the recent climate and adding a cool roof will be required in future extreme years. For optimization design, the applied optimization methodology can generate several optimal building design solutions based on Window-Wall-Ratio.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.315
Teacher spread0.275 · 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