Integrated Building Information Model to Identify Possible Crane Instability Caused by Strong Winds
Bibliographic record
Abstract
Large scale construction projects often involve the lifting of heavy equipment. With increases in equipment size, lifting operations create new challenges in crane selection. In terms of safety, stability is one of the most important factors to consider when selecting cranes. Although practitioners often apply simulation tools to select appropriate cranes, the effect of wind on crane stability is not yet considered in the selection process. Considering that cranes are among the most expensive types of equipment, contractors need to plan the crane operations properly to improve safety and reduce cost and time. This paper presents a methodology to implement the safe operation of cranes by identifying possible crane instability caused by strong winds using Building Information Modeling (BIM), a tool which prepares smart designs to integrate and coordinate cross-disciplinary designs, the construction process, and facility management decisions. A methodology is proposed to integrate wind effects on crane operations which can be considered a major step in developing future BIM. Through a case study involving multiple heavy lifts in an industrial project, the benefits of the proposed methodology are identified.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".