Interoperability between Building Design and Building Energy Analysis
Bibliographic record
Abstract
Lack of interoperability has been criticized as an impediment to improving productivity in the Architecture, Engineering and Construction (AEC) industry. For example, the current information exchange between building design and energy analysis models has numerous issues, including object parametric information deficiencies, geometric misrepresentations and re-input data confusion. As a result, it leads to huge money, time and effort losses in practice. The objective of this paper is to present an automated solution for the seamless information exchange between building design and energy analysis models. In order to achieve this objective, a file converter is designed. The file format converter is built upon existing open standards (gbXML and DOE-2 input files) which facilitate the exchange of building design and energy analysis information. Designers could use the converter to properly analyze building energy efficiency for their design models. Also, the design models could be automatically updated if any modifications have been made in the corresponding energy analysis models. The converter has been implemented with Microsoft Visual C# Studio 2013 and its effectiveness has been tested with two popular design and energy analysis tools - Autodesk®-based products and eQUEST®. In addition, the creation of a software plug-in component has also been proposed in order to execute the file converter application. The results have demonstrated the overall rectification of the geometric and material misrepresentations resulting from the current software interoperability process.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".