Conceptual Framework for Safety Improvement in Mobile Cranes
Bibliographic record
Abstract
Cranes are among the most important equipment used in the construction industry. The use of cranes is growing day by day as the project delivery paradigm in construction is increasingly shifting from the old traditional method where projects are built entirely on-site to an off-site approach relying on modularisation. In essence, modularisation proceeds in two steps: (i) breaking down projects into modules that are fabricated in controlled environments, and (ii) shipping these modules to construction sites for assembly using high capacity cranes. In this respect, the safety during crane operation is of an utmost important issue because an error while lifting, carrying, and placing a load, can cause disastrous accidents. One of the major causes of these accidents is due to improper communication between signalman and operator. The technology is advancing but the crane industry is still relying on the old methods for communication which are by hand signal and two-way radio communication system. This paper introduces a conceptual framework to enhance the communication between the signalman and the crane operator. In this framework, a camera fixed on the helmet of the signalman captures a video that is segmented into frames to which mathematical algorithms are applied in order to interpret their content. This digital graphical (pictorial) content is then transcribed into commands that are displayed on the cabin’s monitor for the operator whether the latter has a direct line of sight to the signalman or not this will increase the efficiency of the crane operator. This framework also eliminates the need for tagman and improves the safety and productivity of the crane operation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".