Spatial Trajectory Analysis for Cranes Operations on Construction Sites
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".