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Record W2172812709 · doi:10.1139/cjce-2015-0431

Life cycle assessment of low-rise office building with different structure–envelope configurations

2015· article· en· W2172812709 on OpenAlexvenueno aff
Othman Alshamrani

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersEuropean Commission
KeywordsBuilding envelopePrecast concreteEnvironmental impact assessmentLife-cycle assessmentEnvelope (radar)Civil engineeringFrame (networking)Energy consumptionEnvironmental scienceEngineeringArchitectural engineeringComputer scienceEnvironmental economicsMechanical engineeringEconomics

Abstract

fetched live from OpenAlex

This paper presents life cycle assessment approach to study the impact of structure and envelope types on the energy consumption and environmental impact of an office building in New York City, USA. In addition, the future costs of environmental impact for various structure and envelope options are assessed according to the current practice and market price of CO 2 . Eight structure and envelope types for the low-rise office building are investigated, which include concrete and steel structures with various combinations of envelopes. The parameters such as life cycle energy use, global warming potential, and water, air and land emissions are analyzed. The energy simulation is performed by using eQUEST software while the environmental impact is assessed by using ATHENA® impact estimator. The building with concrete frame is proved to have lower environmental impact compared to that with steel frame. The precast concrete building is found to be the most economical alternative due to the minimal environmental impact cost. This study is expected to enable the decision makers and building owners to select the best alternative with respect to energy use, and environmental and economic constraints.

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: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.537

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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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