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Record W2936733265 · doi:10.29173/mocs7

Optimum Assembly Planning for Modular Construction Using BIM and 3D Point Clouds

2016· article· en· W2936733265 on OpenAlexaffvenue
Mohammad Nahangi, Christopher Rausch, Carl T. Haas

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReworkModular designPoint cloudComponent (thermodynamics)Computer scienceMatching (statistics)Point (geometry)Engineering drawingSystems engineeringIndustrial engineeringReliability engineeringEngineeringEmbedded systemMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Geometric and dimensional deviations often create challenges for component aggregation in the assembly of interchangeable components in modular construction. Although the components are designed interchangeably, once they are fabricated, there are inevitable discrepancies between the designed and built states. Such discrepancies create problems for fitting interchangeable modular components. This paper presents a framework for optimally planning the assembly of interchangeable components based on their as-built state. A 3D point cloud model is captured and the critical interfaces between modules are compared to the original state, integrated in the building information models (BIM), as 3D drawings. The optimization framework is implemented based on two different approaches: (1) minimization of the total deviation for minimizing rework, and (2) intervention of rework by finding the best matching component for each investigated slot. Results show that the method can be effectively used for reducing rework in modular construction by optimum assembly planning.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.910

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.229
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2016
Admission routes2
Has abstractyes

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