3D Visualization-Based Motion Planning of Mobile Crane Operations in Heavy Industrial Projects
Bibliographic record
Abstract
The successful completion of modular-based heavy industrial construction projects relies heavily on safe and efficient crane operations, which have a direct impact on project productivity. Corresponding to the design of reliable crane operations, this paper proposes three-dimensional (3D) visualization-based motion planning for mobile cranes that integrates 3D visualization with mathematical algorithms based on “what if” scenarios. This method facilitates the design of collision-free mobile crane operations for a large number of lifts in congested areas. The proposed methodology is established based on two types of interactive analyses: (1) rotation analysis to build 3D visualization for the motions of crane body configurations; and (2) spatial analysis to detect potential collision errors in order to design collision-free crane operations. The rotation analysis accounts for calculating the angles describing the orientations of mechanical elements in the crane system by reading the coordinates of the crane location and the pick and set points of the object (module) to be lifted. The spatial analysis is used to monitor and maintain sufficient clearances between existing obstacles and the crane body configurations in order to prevent potential collisions during crane operation design. The methodology is tested on a case study in order to illustrate its effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".