Building Science Integrated Systems Methodological Framework
Bibliographic record
Abstract
Building performance is governed by physical processes, which are dynamically coupled in time and space, and whose degrees of interactions are often difficult to measure and appreciate. As a result, suboptimal performance and failures often occur. The goal of high-performance buildings is to optimize major aspects such as energy efficiency, life-cycle costs, and lighting, which are tightly coupled by the underlying physical processes. The premise behind this research project is that building integration/optimization can only be achieved when grounded on a shared understanding and communication of the underlying physical principles governing building performance, which can then enable the transformation of these principles into meaningful performance metrics. This paper proposes a methodology for building systems integration through building science principles. At the core of the methodology, a vocabulary of building science concepts, principles, and metrics enables using existing knowledge to increase understanding and gain insights on the systems involved in a particular design (including degrees of coupling, redundancies, and behaviours), which in turn facilitates the creation of new knowledge that may be needed to integrate new systems and technologies. A set of generic building science rules implemented using systems theory will enable such knowledge creation while preserving systems integrity at all times. The goal of this research is not to create a knowledge-base to replace building science professionals but to leverage an explicit vocabulary to increase understanding, learning, and communication of building performance for improved building integration. Furthermore, it is envisioned that the knowledge-base will serve as a bridge between building simulation, decision analysis, and optimization. This paper presents the initial attempt to organize a wealth of building science knowledge into a structured vocabulary. The power of generality and usability of the methodology will be tested with a case study. The expected benefits of the approach are three-fold: 1) to promote a more systematic approach to optimize building systems, 2) to facilitate the integration of new systems and technologies in buildings, and 3) to improve the education and dissemination of building science knowledge for improved building integration.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".