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Record W3211701357 · doi:10.29173/mocs168

From Digital to Physical: Advancing Industrialized Construction through Digital Prototyping Processes

2015· article· en· W3211701357 on OpenAlexaffvenue
Basem Eid Mohamed, Frederic Gemme, Aaron Sprecher

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsMcGill University
Fundersnot available
KeywordsWorkflowFlexibility (engineering)Rapid prototypingSystems engineeringPersonalizationArchitectureComputer scienceEngineeringSoftware engineeringManufacturing engineeringMechanical engineeringWorld Wide Web

Abstract

fetched live from OpenAlex

Recent development in digital design strategies and fabrication tools has offered the Architecture, Engineering, and Construction (AEC) industry the opportunity to rethink the prefabricated building industry. Different methodologies have been proposed with the aim of formulating an efficient link between design and fabrication processes, based on devising effective data workflows to overcome complexities associated with conventional production models. This paper demonstrates recent developments in implementing a Digital Prototyping model toward establishing a comprehensive strategy for design and fabrication of a family of building components. The proposed model is specifically designed and implemented to realize a prefabricated construction system, BONE Structure®, with the aim of enabling high precision in design and fabrication, in addition to supporting pre-defined assembly on jobsites. This paper represents a phase from an ongoing research endeavor toward infusing the design and production processes with digital logic, aiming for high flexibility and customization in building design, specifically the housing realm.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.014
GPT teacher head0.219
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
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

Citations0
Published2015
Admission routes2
Has abstractyes

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