A Review of Quantitative Approaches to Intelligent Building Assessment
Bibliographic record
Abstract
This paper provides a review of the assessment methods of intelligent buildings (IBs). Based on a review of rating method currently used for building assessment, 6 rating systems for IB assessment are compared according to assessment clusters such as Architecture, Engineering, Environment, Economics, Management, and Sociology. The 6 IB rating systems include the AIIB method developed by the Asian Institute of Intelligent Buildings (AIIB), Hong Kong, China; the BRE method developed by the Building Research Establishment Ltd., UK; CABA method developed by the Continental Automated Building Association (CABA), Canada & USA; the IBSK method developed by the Intelligent Building Society of Korea (IBSK), Korea; the SCC method developed by the Shanghai Construction Council (SCC), China; and the TIBA method developed by the Architecture and Building Research Institute, Ministry of the Interior, Taiwan, China. Although the AIIB method is identified as the most comprehensive assessment system, its four weaknesses are explained. The paper concludes that an innovative building approach using analytic network process will bring advantages to IB assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".