An Integrated Condition Assessment Model for Buildings
Bibliographic record
Abstract
Building facilities are a major part of urban infrastructure, as they provide shelter, living space, and service areas to accommodate human activity. Despite their great economic, cultural and historical importance, many studies have shown that buildings are sick, deteriorating and considered to be a major source of pollution. Lack of funds and mismanagement are the principle reasons for the unsatisfactory performance of building facilities. Maintaining a building is essential to keep it performing and functioning for a longer period of time. Despite the importance of the condition assessment (CA) stage in the asset management process, a literature review reveals that there is no building assessment framework that considers both physical and environmental conditions. The objective of this paper is to develop an integrated CA model that integrates both the physical and environmental aspects of buildings. This model provides an accurate, reliable, and sustainable framework capable of assessing a building from both physical and environmental perspectives. The framework is to be implemented and tested using data collected from experts as well as from operation systems for existing office buildings in North America. Details of the proposed framework and its implementation are presented. The research work in this paper assists facility managers and owner's organizations in administrating such buildings.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".