Improving Project Delivery Using Virtual Reality in Design Reviews —A Case Study
Bibliographic record
Abstract
Abstract With the advent of the computerized 3D model environment, the engineering review process for complex process plants has improved dramatically, however, recent technological advances are facilitating the next step in productivity. Typically, engineering companies conduct design reviews using a multidisciplinary panel of seasoned engineering experts to assess the safety and the utility of the design. The review is based upon a combination of traditional paper-based deliverables and on-screen views of the 3D model using conventional software tools, e.g. SP3D, PDMS and PDS. The Operations function is often not represented in the review phase at this early stage in the lifecycle of the asset. By conducting two separate design review exercises, first using the conventional design review method followed by a review built around virtual reality (VR) immersion, we were able to demonstrate incremental benefits possible through the incorporation of VR technology. The identified benefits fell into two main categories, specifically identification of design deficiencies that: were not observable using traditional design review methodswere overlooked by the traditional panel of expert reviewers Using VR technology, the immersive model review was provided in an intuitive format which allowed deeper understanding of the spatial context and allowed a more diverse team of field operatives and maintenance personnel to participate. The final design outcome was improved by the addition of practical insights from operations stakeholders who would otherwise have been excluded from the design review process. Ultimately, the combination of improved spatial understanding and greater diversity of inputs led to a client-certified savings of USD$2.55M in future modifications and reworks.
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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 | 0.065 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".