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Record W2077547847 · doi:10.1061/41109(373)36

Spatial Trajectory Analysis for Cranes Operations on Construction Sites

2010· article· en· W2077547847 on OpenAlexaff
Jacek Olearczyk, Ulrich Hermann, Mohamed Al‐Hussein, Ahmed Bouferguène

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsPCL Construction (Canada)University of Alberta
Fundersnot available
KeywordsTrajectoryLift (data mining)Object (grammar)Computer sciencePosition (finance)Process (computing)BoomPath (computing)Point (geometry)Set (abstract data type)MathematicsArtificial intelligenceEngineeringData miningGeometry

Abstract

fetched live from OpenAlex

Managing the behaviour and trajectory of an object lifted by a crane is critical to a successful lift. This paper presents advancements in the development of mathematical algorithms for lifted object trajectory paths and analyses. The proposed methodology is divided into smaller manageable phases to control the process and at the same time to create independent modules. Each step of the lifted object movement was geometrically tracked, starting at the lifted object pick-point, through an optimum path development and completing at the object final position (set-point). Parameters such as the minimum distance between the lifted object and passing obstructions and minimum allowable clearance between the lifted object and the crane boom are some of the many predefined rules which were taken into account. The lifted object's spatial trajectory analysis and optimization is a part of the complex assignment relating to the crane selection process. A numerical example is presented to demonstrate the effectiveness of the proposed methodology and illustrate its essential value.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.908

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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
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

Citations7
Published2010
Admission routes1
Has abstractyes

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