Bibliographic record
Abstract
Many types and sizes of cranes are used to lift and move heavy materials in construction sites. The technology used in cranes has improved dramatically in the past decades in a way that this technology has, in many ways, outstripped the ability of people to use the machines safely. This misusage makes crane's accidents one of the more severe and highly visible construction accidents. Heavy reliance must be placed on the factors aecting crane safety that may dier with its impact and severity from one project to another. The presented research in this paper establishes a methodology for assessing and evaluating crane safety in construction site by introducing a crane safety index (CSI) model using two dierent decision-making methodologies: Analytic Hierarchy process (AHP) and Fuzzy Logic. The goal of the developed model is to help planners improve crane operation and meet safety requirements. In order to achieve this goal, a survey was conducted aiming at assessing the factors that aect crane operation. The CSI was designed and applied to the collected data to obtain a safety index value. The presented research is vital for planners (contractors) because it gives them a fast, accurate, and advanced tool for obtaining a CSI. The index indicates the performance of crane safety operation in construction sites, allowing for a chance to spot and encompass the factors causing hazardous circumstances, and the possibility to enhance the crane safety plan.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Develops an AHP/fuzzy-logic crane safety index for construction sites; the object is site safety, not research.
The study develops a construction-crane safety index and does not study research practice.
Construction-site crane safety index using AHP and fuzzy logic; occupational safety domain.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".