An Integrated Condition Assessment Model for Educational Buildings Using BIM
Bibliographic record
Abstract
Building facilities compose a major part of any urban infrastructure. Despite their considerable economic, cultural and/or historic importance, several studies have shown that many buildings are sick, deteriorating and a major source of pollution. Maintaining a building is essential to keep it performing and functioning for a longer period of time as well as providing better quality of life for building occupants. Despite the importance of the condition assessment (CA) stage in the asset management process, literature review reveals that there is no building condition assessment framework that considers both physical and environmental conditions. Schools and educational facilities in Canada, which comprise a major component of the non-residential buildings sector, has passed 51% of their useful service life \n The primary objective of this research is to develop an Integrated Condition Assessment Model for Educational Buildings that considers both building physical and environmental conditions. This model will assist owners and facility managers in the condition assessment phase during the asset management process. As buildings are composed of spaces; this proposed model uses “space” as the principal element of evaluation. The Multi Attribute Utility Theory (MAUT) is used to calculate the physical and environmental conditions of each space, and the K-mean clustering is conducted to calculate the integrated condition of each one. 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. The proposed methodology upgrades the use of an object-oriented Building Information Model (BIM) so that it can be used as a platform and an advanced tool for storing, exchanging, and transferring assessment data inputs as well as serving in the assessment process. Integrated Condition Assessment model for Buildings (ICAB) is the developed automated tool that integrates with Revit© 2011. This integration allows the BIM model to be used as the data source and to provide any required graphical representation. \nThe model is implemented and tested using data collected from experts and from field measurements taken from an educational building in Montreal. Finally, the model was validated by experts working in the facilities management field and they acknowledged having good potentials.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".