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Record W3185388864 · doi:10.29173/mocs151

Development of Conceptual Modular Building Unit Design Framework for Inexperienced Designers at the Pre-Design Stage

2015· article· en· W3185388864 on OpenAlexvenueno aff
Jeonghoon Lee, Moonseo Park, Hyunsoo Lee, Minjung Kim, Hosang Hyun

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
FundersMinistry of Land, Infrastructure and Transport
KeywordsModular designProcess (computing)Design processSystems engineeringEngineering design processUnit (ring theory)Architectural engineeringEngineeringModular constructionConceptual designComputer scienceBuilding designConstruction engineeringEngineering managementProcess managementWork in processOperations managementHuman–computer interactionMechanical engineering

Abstract

fetched live from OpenAlex

Modular building construction has been increasing interest in adopting and utilizing off-site production technologies in house building in many countries and regions. Despite increasing interest of the modular building method, most of researchers were less interested in to support inexperienced designer who never experience in modular building design process. The objective of this paper is development of modular building construction design process framework, focusing on to provide for start-up modular company’s inexperienced designers’ improvement of modular building design understanding and reduce design errors at the enforcement design stage. To achieve this purpose, this paper adopts Building Systems Integration (BSI) concepts into framework and describes each step following proposed framework. This paper provides more easy to understand for inexperienced designer understanding the modular building design process than current textual guidelines. Subsequently, proposed framework can be translating for training materials to inexperienced modular building design process designers

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.315
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.051
GPT teacher head0.257
Teacher spread0.206 · 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 designBench or experimental
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 routes1
Has abstractyes

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