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Near Real-Time Motion Planning and Simulation of Cranes in Construction: Framework and System Architecture

2012· article· en· W1981172092 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.

Bibliographic record

VenueJournal of Computing in Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsConcordia University
Fundersnot available
KeywordsPlannerMotion planningMotion (physics)EngineeringAutomationArchitectureSystems engineeringComputer scienceSimulationArtificial intelligenceRobot

Abstract

fetched live from OpenAlex

Motion planning of cranes is an important issue in construction projects, where rapid and accurate planning directly affects the safety and productivity of operations. The work presented in this paper is directed toward developing a framework for near real-time motion planning of cranes that satisfies safety requirements and efficiently considers the dynamic properties of construction sites. This framework is not aiming to full automation but rather for providing assistance to crane operators to replan safe paths in near real time. The proposed motion planning framework is designed in a way that makes it possible to be generalized over different types of equipment, and has the ability of visualizing and simulating motion planning results in near real-time. The framework is applied to develop a specialized motion planning system for construction equipment, Intelligent Construction Equipment Planner (ICE-Planner). This system is integrated into three-dimensional software to define, solve, and visualize motion planning in near real time.

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: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.377

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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