From AutoCAD to 3ds Max: An automated approach for animating heavy lifting studies
Bibliographic record
Abstract
Modular construction is a dominant manufacturing method for industrial construction in Alberta, Canada. Modularization requires large-capacity mobile cranes to lift heavy modules, such as piperack modules. The current practice utilizes AutoCAD to generate heavy lift studies for modular onsite installations. Heavy lift studies consist of 2D and 3D simulations of the lifting scenarios, along with the corresponding calculations (e.g., lifting capacity checking, ground bearing pressure checking). These static simulations provide snapshots of mobile cranes at pick and set configurations, but they do not represent the movements between the two configurations. For better communication among site engineers and crews, current static heavy lift studies need to be improved by animating the entire lifting process. 3ds Max is an animation tool that can visualize the lifting process, but the tedious and manual process of preparing the animation restricts efficiency and productivity. This research thus introduces a newly developed animation system that automates the transfer of heavy lift studies from AutoCAD into Autodesk 3ds Max animation. Also in this research, the kinetics of mobile cranes are studied and generic crane movements are defined. Using MAXScript, a script is written to link the crane and project database for automatic generating of animations. This research aims to provide the construction industry with a generic method for automating the animation process for heavy lifts based on AutoCAD and 3ds Max systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".