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Record W2575656890 · doi:10.5555/3042094.3042502

A prototype for simulating the kinematics of crane rigging oscillatory motion using Simphony.NET

2016· article· en· W2575656890 on OpenAlexaff
Ronald Ekyalimpa, Martin Akolo Chiteri, Simaan AbouRizk

Bibliographic record

VenueWinter Simulation Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsKinematicsComputer scienceVirtual prototypingAutomationMotion (physics)SimulationPerspective (graphical)Double pendulumWork (physics)Scope (computer science)Equations of motionMotion controlPendulumControl engineeringInverted pendulumEngineeringMechanical engineeringArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

Crane hoisting operations represent a significant portion of the work scope on construction sites, especially those that have adopted a modularized approach to construction. Creating metrics that can be used in the automation of these processes can result in higher jobsite efficiencies from a safety and productivity perspective. This study created a virtual simulation environment prototype that can be experimented with to generate the required metrics for crane hoisting automation. The equation of motion for this oscillatory motion was first defined. Thereafter numeric solutions to this equation were explored from a continuous simulation perspective using Simphony.NET. Then prototyping of simple pendulum motion was implemented using the continuous simulation services in Simphony.NET and verification done using Mathematica.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.038
GPT teacher head0.272
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2016
Admission routes1
Has abstractyes

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