Critical lifting simulation of heavy industrial construction in gaming environment
Bibliographic record
Abstract
Purpose Heavy industrial construction often relies on large mobile cranes to erect equipment and pre-assembled modules. Engineering calculations are required for the lifting analysis where lifting capacity is analyzed to ensure the feasibility of the lifting scenarios. Such engineering calculations are often presented in static formats, e.g. two-dimensional or three-dimensional models. However, it is difficult to help practitioners (e.g. lifting engineers, site crews and operators) understand the complexity of the lifting process and thus operational decisions are often made intuitively. Therefore, this paper aims to introduce a game-based simulation system to allow for interactive analysis of the lifting process to improve lifting efficiency and safety. Design/methodology/approach The proposed method treats the mobile crane as a robot with degree-of-freedoms, and the movements are simulated in the Unity game environment. The lifting capacity is calculated dynamically based on the lifting object weight, rigging weight and lifting radius. Findings Compared with the four-dimensional visualization, this development has added a dimension of real-time interactive simulation; this allows the users to understand the complexity and feasibility of the lifting process. Originality/value The developed prototype has been tested and validated using a real case study from a heavy industrial project with the possibility of generalizing crane lifting configurations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".