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Record W3099037304 · doi:10.1061/9780784482865.134

BIM-Based Automated Drafting System in Cabinet Manufacturing

2020· article· en· W3099037304 on OpenAlex
Yichen Tian, Jingwen Wang, Nan Zhang, Mohamed Al‐Hussein

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

VenueConstruction Research Congress 2020 · 2020
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReworkBuilding information modelingCabinet (room)ArchitectureAutomationEngineeringSystems engineeringManufacturing engineeringInformation modelInformation exchangeComputer scienceSoftware engineeringEmbedded systemCompatibility (geochemistry)Mechanical engineering

Abstract

fetched live from OpenAlex

Building information modeling (BIM) has become an important tool during the planning, designing, and construction phases in the architecture, engineering, and construction (AEC) industry. Implementing manufacturing-centric BIM can improve the efficiency of communication, the interaction and the data flow between the builder and the contractor that manufactures the building components. In the construction industry, the information gap between builder and cabinet manufacturer causes cabinet rework and material waste, which leads to construction delays and cost increases. To address this issue, extending manufacturing-centric BIM applications into cabinet design and manufacturing can enhance the information exchange as well as enrich the information within the BIM model. Thus, this paper presents an automated approach based on BIM for cabinet layout design and planning in order to optimize the design and improve the drafting efficiency. An application prototype is developed in the BIM environment to achieve the objectives through the automation of drafting and planning with the support of Autodesk Revit. A case study of cabinet design and production for a residential building is subsequently presented to prove the feasibility of this application. As the main contribution of the proposed research, the in-depth integration of BIM model with the automated drafting system achieves full automation of cabinet layout design.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.830

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.280
Teacher spread0.253 · 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