Digital mock-ups as support tools for preventing risks related to energy sources in the operation stage of industrial facilities through design
Bibliographic record
Abstract
Building information modelling (BIM) and product lifecycle management (PLM) technologies provide automatic model checking (AMC) tools that can be used for the prevention of occupational health and safety (OHS) risks through design (PtD). Considering that the risks related to energy sources during the operation stage of industrial facilities can be major, our objective is to propose a PtD approach for this type of risk that uses AMC tools. For this purpose, our methodology is based on the information systems design-science paradigm. Considering that both BIM and PLM mock-ups can be used to design industrial facilities and that Catia V5 is one of the main software programs used to design industrial equipment and facilities, our methodology specifically includes a comparative literature review of the uses of AMC tools to check BIM and PLM mock-ups and a comparative study of the OHS risk prevention capabilities of the AMC tools available in Catia V5. Therefore, the PtD approach that we propose is specific to Catia V5. It consists of: 1) extracting rules related to hazardous energy control from regulatory requirements and classifying them according to their automation potential, 2) expressing the rules in a form that is compatible with the RASE method, 3) interpreting the rules using the RASE method, and 4) using a macro script to automatically check the compliance of the digital mock-ups. The proposed approach's contribution is that it makes it possible to support facility designers in automatically identifying hazardous energy sources in systems. In future studies, we intend to couple this PtD approach with methods that integrate dynamic system behavior to assess the level of risk corresponding to the hazardous sources identified.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".