Related Literature Review 5D Model for Project and Operation/Maintenance Remote Monitoring of Equipment and Piping System
Bibliographic record
Abstract
This 5D model research study gathers data and integrates existing research papers and related studies into this review.The goal is to reflect on the concept to realize this research and the issue and problems faced by previous research.Moreover, the industries' operational possibility, practicality, and applicability align with Industry 4.0 and the Digital Twin perspective.This paper presents an extensive literature analysis concerning the 5D model concept utilizing and integrating into one (1) platform with 3D CAD software during the entire project engineering construction stages, including operation and maintenance.The 5D model study focuses on platform development and innovation in a remote or virtual environment.The remote control and monitoring apply to project engineering/construction, plant operation/maintenance (OM), and system decommissioning.Specifically, Oil & Gas, Petrochemical, Petroleum Refinery, etc.It will also include Building Information Model (BIM) related research study, especially in costing and scheduling development.Due to the limited and unavailability of data for a specific study, the 5D model concept is not everywhere.However, it has already been formulated from various perspectives of researchers, modelers, and software developers in different stages.The researcher brings together related topics like virtual project management (VPM), virtual team (vT), data management system (DMS), web-portal system, building information modeling (BIM), cloud-based software (CBS), project triad, multi-structure & scale, lean construction, safety, quality, google earth, control/inspection, and remote-control monitoring, 3D model design, and computerized maintenance management system (CMMS).The study review of the 5D model concept within the specific area is evaluated, analyzed, and summarized in the below, figures and tables.It concluded that the effective implementation of the idea increases competitiveness, high safety, quality, and performance.Moreover, it does not duplicate another research study on this 5D model concept.However, the relevant topic's idea may be improved, optimized, and innovated to realize this concept and better application.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".