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Record W1991412404 · doi:10.1061/41109(373)26

A Simulation Driven Visualization Framework for Construction Operations: Development and Application

2010· article· en· W1991412404 on OpenAlexaff
Amr ElNimr, Y. Mohammed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsVisualizationComputer scienceComponent (thermodynamics)High-level architectureConstruction industrySystems engineeringData visualizationArchitectureSoftware engineeringConstruction engineeringEngineeringInteroperabilityData miningOperating system

Abstract

fetched live from OpenAlex

Simulation is a relatively new area of engineering research, whose implementation in day-to-day construction industry activities continues to lag behind research efforts. It is widely viewed that the lack of a visualization component in construction operations simulation contributes to this low level of adoption within the industry. This paper discusses the development of a framework for simulation visualization and the implementation of this framework in modeling a real pipe spools assembly yard and pipe modules site installations. The framework can be used to better represent spatial data in simulation. The framework is developed through a distributed simulation based approach using High Level Architecture (HLA) standards. The paper demonstrates how this research can benefit construction through a real case study.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.327

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)0.0000.000

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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2010
Admission routes1
Has abstractyes

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