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A Holistic methodology for lifecycle energy consumption of heritage buildings

2021· article· en· W3172129897 on OpenAlexaffabout
Abobakr Al-Sakkaf, Eslam Mohammed Abdelkader, Samer El-Zahab, Ashutosh Bagchi, Tarek Zayed

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsArchitectural engineeringCultural heritageIndustrial heritageEnergy consumptionField (mathematics)Consumption (sociology)Computer scienceConstruction engineeringEngineeringCultural heritage managementGeography

Abstract

fetched live from OpenAlex

Abstract Heritage buildings are historically exceptional in their landscape and specific attention must be paid to their architectural element and components. Recently, the techniques that are utilized for the study and protection of cultural heritage have been on the rise in the research field. Studies have shown that project life cycle phases can be implemented to determine the performance of a given building in general. However, heritage buildings and their need were not considered. The project life cycle phases include: 1) planning, 2) manufacturing, 3) transportation, 4) construction, 5) operation and 6) maintenance phases. In addition, there is a need for an encompassing rating system that is capable of determining the most optimal pathway for rehabilitating heritage buildings. Hence, this article aims to present a comprehensive life cycle energy analysis model that optimizes expenditure over all building components by optimizing the budget. Furthermore, as a proof of concept, two case studies are applied in this research-GN in Canada and MP in the KSA.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.083
GPT teacher head0.286
Teacher spread0.203 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1
Published2021
Admission routes2
Has abstractyes

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