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Method for Conceptual Design Applied to Office Buildings

2002· article· en· W2110418442 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Computing in Civil Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConceptual designPareto principleMulti-objective optimizationSet (abstract data type)Rank (graph theory)GraphicsGenetic algorithmMathematical optimizationComputer scienceComputer graphicsIndustrial engineeringOperations researchEngineeringData miningMachine learningMathematicsHuman–computer interactionComputer graphics (images)

Abstract

fetched live from OpenAlex

The paper presents a computer-based method for the multicriteria conceptual design of engineered artifacts. The proposed method involves genetic-based stochastic search, Pareto optimization, and color-filtered graphics. A multicriteria genetic algorithm broadly searches the governing body of design knowledge and identifies Pareto designs that are equal-rank optimal in the sense that each is not simultaneously dominated for all objective criteria by any other feasible design. Computer color filtering of the Pareto-optimal design set creates informative graphics that identify trade-off relationships between competing objective criteria, as well as design subsets having particular designer-specified attributes. Much of the paper is devoted to presenting a detailed illustration of the method for the cost-revenue conceptual design of high-rise office buildings, including several examples.

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: Methods · Consensus signal: none
Teacher disagreement score0.537
Threshold uncertainty score0.658

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 communication0.0000.000
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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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