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Record W2038591776 · doi:10.1016/j.asej.2014.09.006

Reducing the operational energy demand in buildings using building information modeling tools and sustainability approaches

2014· article· en· W2038591776 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAin Shams Engineering Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsConcordia University
Fundersnot available
KeywordsSustainabilityBuilding information modelingArchitectural engineeringEnergy consumptionEnergy demandEngineeringEfficient energy useSustainable designEnergy analysisEnergy modelingArchitectureEnergy (signal processing)Environmental economicsCivil engineeringOperations management

Abstract

fetched live from OpenAlex

A sustainable building is constructed of materials that could decrease environmental impacts, such as energy usage, during the lifecycle of the building. Building Information Modeling (BIM) has been identified as an effective tool for building performance analysis virtually in the design stage. The main aims of this study were to assess various combinations of materials using BIM and identify alternative, sustainable solutions to reduce operational energy consumption. The amount of energy consumed by a double story bungalow house in Johor, Malaysia, and assessments of alternative material configurations to determine the best energy performance were evaluated by using Revit Architecture 2012 and Autodesk Ecotect Analysis software to show which of the materials helped in reducing the operational energy use of the building to the greatest extent throughout its annual life cycle. At the end, some alternative, sustainable designs in terms of energy savings have been suggested.

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)0.001
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 score0.611
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.195
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