Improving Project Delivery Using Virtual Reality in Design Reviews —A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it