Space-Based Condition Assessment Model for Buildings: Case Study of Educational Buildings
Bibliographic record
Abstract
Despite the importance of the condition assessment (CA) stage in the asset management process, literature review reveals that there are some drawbacks in the current practices. The objective of this paper is to develop a condition assessment model for buildings. A new building asset hierarchy is proposed in which the space is the principle element of evaluation. Physical components within a space are categorized into four main categories. Data are collected from experts via questionnaires to assign relative weights to models’ attributes using both the analytical network process (ANP) and the analytical hierarchy process (AHP) techniques. Finally, the multi attribute utility theory (MAUT) is used to calculate the physical condition assessment of spaces and the entire building. The developed model is applied to a case study of an educational building located in Montreal. Results of the model are compared with the calculated results by the building facility management team. Many lessons are learned from the study; among the most significant findings is the importance of building categories and subcategories that differ according to space type. This model will assist owners and facility managers in the condition assessment phase during the asset management process by applying several tools and techniques to provide an accurate condition assessment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".