Digital campus innovation project: integration of building information modelling with building performance simulation and building diagnostics
Bibliographic record
Abstract
Building Information Modelling (BIM) has emerged as a powerful technology that creates a central hub for managing building energy and resources at all phases of the building life cycle. Without it, many tools that lack interoperabil-ity are used, thus massively under-exploiting the efforts of other building design and management parties; this largely describes the status quo. However, despite the power of BIM, it has not been readily adopted by industry, and especially not at the community and campus scale. Digital Campus Innova-tion (DCI) is a large multi-year and multidisciplinary project involving development of a methodology for use of BIM for operation and maintenance of a portion of Carleton Univer-sity’s 45 interconnected buildings. Major elements include: (1) development of highly-detailed BIM models for site and buildings; (2) conversion from BIM models to building per-formance simulation (BPS) models; (3) model validation us-ing measured data; (4) building Fault Detection and Diagnos-tics (FDD) using advanced algorithms and calibrated mod-elling; and (5) advanced building performance data visualiza-tion on top of 3D BIM model. This paper will describe the methodologies that are being developed while demonstrating the ongoing processes by a case study of Canal Building, a part of DCI project. While the project is only one year old, impactful examples have already demonstrated BIM as an in-valuable technology that improves indoor environment qual-ity, reduces energy costs, and has a potential application for asset management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".