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Record W2171106967 · doi:10.5555/2429759.2429834

Simulation of mobile crane operations in 3D space

2012· article· en· W2171106967 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 · 2012
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVisualizationLift (data mining)ScheduleProcess (computing)Computer scienceEngineeringIndustrial engineeringSimulationOperations researchArtificial intelligenceData mining

Abstract

fetched live from OpenAlex

A 3D model allows users to visualize the construction process during a given period of the schedule. This paper presents a methodology to aid practitioners in preparing lift studies with crane selection, positioning, and lift optimization using a 3D space. The 3D visualization helps to identify collision free paths and optimize lifting activities based on optimal crane paths with cycle time and speed of each crane activity from simulation models in Simphony. The proposed methodology provides to help lifting engineering and project manager select the best possible crane. A case study-based approach is utilized to illustrate the proposed methodology. The case study involves construction of a four story, sixty-eight unit building for older adults in Westlock, AB, Canada. The 3D visualization model was provided for the construction team more than two months before the scheduled day of lifting, which assisted the contractor in selecting the optimum crane and successfully completing all lifts (thirty modules, 25 tons each) in just two working days.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score0.639

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 communication0.0000.001
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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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