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Record W2579284964 · doi:10.5555/3042094.3042501

Heavy lift analysis at feed stage for industrial project

2016· article· en· W2579284964 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWinter Simulation Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsPCL Construction (Canada)University of Alberta
Fundersnot available
KeywordsModular designAutomationLift (data mining)PrefabricationProject planningEngineeringProject managementSystems engineeringComputer scienceConstruction engineeringCivil engineering

Abstract

fetched live from OpenAlex

Modular construction has been a widely used method for industrial construction in Alberta. Heavy piperack modules are prefabricated and assembled offsite and transported to site for installation, which minimizes the impact of Alberta's harsh weather and improves efficiency. Such projects are large in scale, ranging from hundreds of modules to thousands; because of this, project planning often requires a relatively long period of time. At the front-end engineering design stage, information is limited, but the planning is critical for determining the appropriate cranes, locations, and lift sequences. To ensure sound planning, information must be extracted from the 3D models, which can be tedious without automation, and engineering analyses are required for crane location selection. This paper introduces a data-driven management system used for project planning. The outputs include selection of crane with locations considering site constraints. Valid automation is implemented in practice to achieve high efficiency.

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: none
Teacher disagreement score0.787
Threshold uncertainty score0.754

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.283
Teacher spread0.220 · 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