Integrated Building Information Model to Identify Possible Crane Instability Caused by Strong Winds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it