Integration of 4D BIM, PtD and databases to improve OHS and knowledge management in construction
Bibliographic record
Abstract
The construction industry faces high incidences of accidents and injuries, resulting in project delays, additional costs, and loss of lives.In developing countries, conventional methods are employed to identify risks and prevent accidents due to limited familiarity with tools such as Building Information Modelling (BIM) and Safety and Health Management (SHM) models.Furthermore, the lack of knowledge retention and lessons learned hinders continuous improvement in safety.Previous research has proposed specific solutions to address these issues, including the integration of BIM in occupational risk management, the use of technology to store safety data, and the application of the Prevention through Design (PtD) approach.However, these solutions tend to focus on individual challenges.This paper introduces a novel methodology called Ultra Safety Design (USD), which comprehensively addresses OHS management in construction projects.USD combines the use of BIM, PtD, and a centralized database.BIM enables precise identification of hazards and risks during the design stages, facilitating the implementation of appropriate control measures.PtD promotes a proactive safety mindset by preventing risks from the design phase, and the centralized database allows for knowledge retention, information exchange, and referencing of previous projects, fostering a culture of continuous improvement.The study's results demonstrate effective risk mitigation, with a significant reduction in overall risk levels.The USD methodology proves to be an integral and effective approach to address OHS management in construction, integrating multiple tools and promoting continuous improvement in OHS practices.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".