A Cloud-Based Architecture for BIM as an Asset for Project Management
Bibliographic record
Abstract
After more than 30 years of research, better solutions continue to be sought for nuclear power plant decommissioning and radioactive waste management. As some approaches are interesting, improvements are still required in order for them to become generalized solutions. \nThis thesis is a part of a larger research project that focuses on developing robotic and automated technologies that could support the decommissioning of the nuclear power plant in Pickering, Ontario. The overarching research project is divided into four main tasks: (i) automatic scanning of parts of a nuclear power plant; (ii) creation of BIMs (Building Information Models) from these scans for integrated asset management, and decommissioning planning and analysis; (iii) non destructive evaluation of elements in the nuclear power plant; and (iv) packing optimization of the radioactive waste for its storage and management. \nThis thesis concerns the second part of the project: creating BIM from the scans (point clouds) generated automatically by a robotic mobile platform. Using Revit® and Recap®, the point clouds are opened in the software and the BIM is created manually from them. A comparison with automatic recognition is made and the limits of both methods are analyzed in order to present the state of the art of automation in this process and the future improvements that can be done. \nDividing this larger research project into four tasks is necessary but creates data management problems, representative of the decommissioning planning challenge. In fact all the data is collected separately with no common storage. Because of the size of this project, it appears possibly advantageous to create an interface where all the data can be shared and accessible by all allowed members. However, the confidentiality of some information must be respected. The security aspect of the developed cloud-based interface is introduced in this thesis and its different functions are presented. \nThe working environment programmed here can be utilized as an approach for BIM-based asset and project management. To prepare for future modifications and generalization to other domains or fields of construction, it has the advantage of being customizable. Indeed, all the functions here are coded in Javascript and are designed for this nuclear power plant decommissioning project. But other functionalities can be developed and existing ones can be suppressed to suit perfectly another project.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".