Risk Analysis in Civil Construction Services Using Mobile Elevating Work Platforms
Bibliographic record
Abstract
The use of vertical transportation equipment in Civil Construction, such as Mobile Elevating Work Platforms - MEWP had optimized activities, avoiding wasted time and simplifying. However, brings in addiction to risks inherent to mechanical equipment, the height factor, increasing the probability of accidents occurrences. The aim of this work was to identify the risks of accidents and nonconformities with the Brazilian legislation present in the activities using MEWP, in order to propose measures of control and systematization of the actions, to integrate accident prevention in construction sites. For this purpose, a checklist was developed and applied in a large Construction workplace located in the Metropolitan Region of Recife-Brazil. According to the data obtained, a list of control guidelines and safety measures was elaborated, identifying the responsibilities of users and operators in services using MEWP. It was observed that the irregularities found are due factors related to operation, maintenance and use inspection of the equipment and significant occurrence of irregularities such as absence of work area isolation, daily inspections and electrical grounding in motorized MEWP. These facts need for improvements making improvements on training and inspections to ensure the fulfillment of the safety standards and an accident-free work environment.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".